PREDICTION OF STILLBORN PIGLETS FROM MULTIPAROUS SOWS
Background: Assisting sows during parturition reduces the number of stillborn piglets caused by anoxia. However, in industrial settings with a large number of animals, the capacity for assistance is limited. The development of predictive models based on existing data can enable farms to anticipate s...
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Universidad Autónoma de Yucatán
2025-03-01
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| Series: | Tropical and Subtropical Agroecosystems |
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| Online Access: | https://www.revista.ccba.uady.mx/ojs/index.php/TSA/article/view/5658 |
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| author | Daniel Alonso Domínguez-Olvera José Guadalupe Herrera-Haro José Ricardo Bárcena-Gama María Esther Ortega-Cerrilla Francisco Ernesto Martínez-Castañeda Antonio José Rouco-Yáñez María Angélica Ortiz-Heredia Nathaniel Alec Rogers-Montoya |
| author_facet | Daniel Alonso Domínguez-Olvera José Guadalupe Herrera-Haro José Ricardo Bárcena-Gama María Esther Ortega-Cerrilla Francisco Ernesto Martínez-Castañeda Antonio José Rouco-Yáñez María Angélica Ortiz-Heredia Nathaniel Alec Rogers-Montoya |
| author_sort | Daniel Alonso Domínguez-Olvera |
| collection | DOAJ |
| description | Background: Assisting sows during parturition reduces the number of stillborn piglets caused by anoxia. However, in industrial settings with a large number of animals, the capacity for assistance is limited. The development of predictive models based on existing data can enable farms to anticipate stillbirths in sows. Objective: To develop a predictive model to identify factors affecting the presence of stillborn piglets (PSbP), estimate the probability of their occurrence, and establish a classification criterion accordingly. Methodology: Data from 2 415 farrowings in 822 sows (Landrace, Yorkshire, and their crossbreeds) were analyzed. Five variables relating to the current farrowing and five variables related to the preceding one were examined. Our study used cross-validation (groups = 5), modeling the response variable (PSbP, 1: presence, 0: absence). Results: The only factor shown to have a negative effect (p<0.01) on PSbP was litter weight at birth, while litter size at birth and parity (number of farrowings) were seen to have a positive effect (p<0.01). PSbP prevalence during training and testing were 0.297 and 0.296 respectively. The model's estimated probability levels were 0.311 during training and 0.303 during testing, indicating an accurate probability estimation. When categorizing using the optimal cutoff point of 0.395, the predictive efficiency as measured by the area under the Receiver Operating Characteristic (ROC) curve was 0.846 for training and 0.813 for testing. Implications: Implementing this model of information-management software could make it possible to provide swift, efficient technical assistance to sows in need, with a high level of predictive efficiency. Conclusions: The probabilistic model described here based on a Bayesian approach and adjusted based on a categorization criterion showed effective predictive efficiency in the prediction of stillborn piglets. |
| format | Article |
| id | doaj-art-60e0cbb2e4034f9d9ed7c70749202b43 |
| institution | OA Journals |
| issn | 1870-0462 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Universidad Autónoma de Yucatán |
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| series | Tropical and Subtropical Agroecosystems |
| spelling | doaj-art-60e0cbb2e4034f9d9ed7c70749202b432025-08-20T02:16:46ZengUniversidad Autónoma de YucatánTropical and Subtropical Agroecosystems1870-04622025-03-0128110.56369/tsaes.56581799PREDICTION OF STILLBORN PIGLETS FROM MULTIPAROUS SOWSDaniel Alonso Domínguez-Olvera0José Guadalupe Herrera-Haro1José Ricardo Bárcena-Gama2María Esther Ortega-Cerrilla3Francisco Ernesto Martínez-Castañeda4Antonio José Rouco-Yáñez5María Angélica Ortiz-Heredia6Nathaniel Alec Rogers-Montoya7Colegio de PostgraduadosColegio de PostgraduadosColegio de PostgraduadosColegio de PostgraduadosUniversidad Autónoma del Estado de MéxicoUniversidad de MurciaColegio de PostgraduadosUniversidad Nacional Autónoma de MéxicoBackground: Assisting sows during parturition reduces the number of stillborn piglets caused by anoxia. However, in industrial settings with a large number of animals, the capacity for assistance is limited. The development of predictive models based on existing data can enable farms to anticipate stillbirths in sows. Objective: To develop a predictive model to identify factors affecting the presence of stillborn piglets (PSbP), estimate the probability of their occurrence, and establish a classification criterion accordingly. Methodology: Data from 2 415 farrowings in 822 sows (Landrace, Yorkshire, and their crossbreeds) were analyzed. Five variables relating to the current farrowing and five variables related to the preceding one were examined. Our study used cross-validation (groups = 5), modeling the response variable (PSbP, 1: presence, 0: absence). Results: The only factor shown to have a negative effect (p<0.01) on PSbP was litter weight at birth, while litter size at birth and parity (number of farrowings) were seen to have a positive effect (p<0.01). PSbP prevalence during training and testing were 0.297 and 0.296 respectively. The model's estimated probability levels were 0.311 during training and 0.303 during testing, indicating an accurate probability estimation. When categorizing using the optimal cutoff point of 0.395, the predictive efficiency as measured by the area under the Receiver Operating Characteristic (ROC) curve was 0.846 for training and 0.813 for testing. Implications: Implementing this model of information-management software could make it possible to provide swift, efficient technical assistance to sows in need, with a high level of predictive efficiency. Conclusions: The probabilistic model described here based on a Bayesian approach and adjusted based on a categorization criterion showed effective predictive efficiency in the prediction of stillborn piglets.https://www.revista.ccba.uady.mx/ojs/index.php/TSA/article/view/5658probabilistic modellogistic regressioncross-validationsus scrofa domesticus. |
| spellingShingle | Daniel Alonso Domínguez-Olvera José Guadalupe Herrera-Haro José Ricardo Bárcena-Gama María Esther Ortega-Cerrilla Francisco Ernesto Martínez-Castañeda Antonio José Rouco-Yáñez María Angélica Ortiz-Heredia Nathaniel Alec Rogers-Montoya PREDICTION OF STILLBORN PIGLETS FROM MULTIPAROUS SOWS Tropical and Subtropical Agroecosystems probabilistic model logistic regression cross-validation sus scrofa domesticus. |
| title | PREDICTION OF STILLBORN PIGLETS FROM MULTIPAROUS SOWS |
| title_full | PREDICTION OF STILLBORN PIGLETS FROM MULTIPAROUS SOWS |
| title_fullStr | PREDICTION OF STILLBORN PIGLETS FROM MULTIPAROUS SOWS |
| title_full_unstemmed | PREDICTION OF STILLBORN PIGLETS FROM MULTIPAROUS SOWS |
| title_short | PREDICTION OF STILLBORN PIGLETS FROM MULTIPAROUS SOWS |
| title_sort | prediction of stillborn piglets from multiparous sows |
| topic | probabilistic model logistic regression cross-validation sus scrofa domesticus. |
| url | https://www.revista.ccba.uady.mx/ojs/index.php/TSA/article/view/5658 |
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