Applicability of machine learning methods for classifying lightweight pigs in commercial conditions
Salgado-López, Pau (Universitat Autònoma de Barcelona. Servei de Nutrició i Benestar Animal)
Casellas Vidal, Joaquim (Universitat Autònoma de Barcelona. Departament de Ciència Animal i dels Aliments)
Solar Diaz, Iara (DNA Genetics LLC)
Rathje, Thomas (DNA Genetics LLC)
Gasa, Josep (Universitat Autònoma de Barcelona. Departament de Ciència Animal i dels Aliments)
Solà Oriol, David (Universitat Autònoma de Barcelona. Departament de Ciència Animal i dels Aliments)

Fecha:	2024
Resumen:	The varying growth rates within a group of pigs present a significant challenge for the current all-in-all-out systems in the pig industry. This study evaluated the applicability of statistical methods for classifying pigs at risk of growth retardation at different production stages using a robust dataset collected under commercial conditions. Data from 26,749 crossbred pigs (Yorkshire × Landrace) with Duroc at weaning (17 to 27 d), 15,409 pigs at the end of the nursery period (60 to 78 d), and 4996 pigs at slaughter (151 to 161 d) were analyzed under three different cut points (lowest 10%, 20%, and 30% weights) to characterize light animals. Records were randomly split into training and testing sets in a 2:1 ratio, and each training dataset was analyzed using an ordinary least squares approach and three machine learning algorithms (decision tree, random forest, and generalized boosted regression). The classification performance of each analytical approach was evaluated by the area under the curve (AUC). In all production stages and cut points, the random forest and generalized boosted regression models demonstrated superior classification performance, with AUC estimates ranging from 0. 772 to 0. 861. The parametric linear model also showed acceptable classification performance, with slightly lower AUC estimates ranging from 0. 752 to 0. 818. In contrast, the single decision tree was categorized as worthless, with AUC estimates between 0. 608 and 0. 726. Key prediction factors varied across production stages, with birthweight-related factors being most significant at weaning, and weight at previous stages becoming more crucial later in the production cycle. These findings suggest the potential of machine learning algorithms to improve decision-making and efficiency in pig production systems by accurately identifying pigs at risk of growth retardation. The use of machine learning algorithms on large commercial datasets that are updated regularly could allow the swine industry to identify pigs at risk of growth retardation, thus improving management interventions, enhancing efficiency, and reducing body weight variability at slaughter.
Ayudas:	Ministerio de Ciencia, Innovación y Universidades, Gobierno de España PID2019-103915GB-I00 Agència de Gestió d'Ajuts Universitaris i de Recerca 2023FI-200080
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Area under the curve ; Classification algorithms ; Efficiency ; Growth retardation ; Live weight ; Swine
Publicado en:	Translational Animal Science, Vol. 8 (december 2024) , ISSN 2573-2102

DOI: 10.1093/tas/txae171
PMID: 39697266

14 p, 2.0 MB

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