Development of Benchmark Curves to Early Detect Health-Related Productivity Deviations Using Production Indicators in Swine Nursery and Finishing Lots
Pedro Mil-Homens, Mafalda (Universitat Autònoma de Barcelona. Departament de Sanitat i d'Anatomia Animals)
Portal, Ximena Paz (Hanor Company)
Magalhaes, Edison (Iowa State University. Department of Animal Science)
Holck, Tyler (Feed His People)
Stave, Joel (Prairie Systems)
Gebhardt, Jordan (Kansas State University. Department of Diagnostic Medicine/Pathobiology)
Costa, Eduardo (Wageningen University. Department of Epidemiology, Bio-informatics and Animal Models)
Holtkamp, Derald (Iowa State University. Department of Veterinary Diagnostic and Production Animal Medicine)
Dórea, Fernanda (Food and Agriculture Organization of the United Nations)
Wang, Chong (Iowa State University. Department of Statistics)
Trevisan, Giovani (Iowa State University. Department of Veterinary Diagnostic and Production Animal Medicine)
Linhares, Daniel (Iowa State University. Department of Veterinary Diagnostic and Production Animal Medicine)
Silva, Gustavo (Iowa State University. Department of Veterinary Diagnostic and Production Animal Medicine)

Data:	2025
Resum:	The nursery and finishing phases are critical for profitability and sustainability in swine production, but effective methods for early disease detection in these stages remain underdeveloped. This study used routinely collected production indicators from nursery (42-56 days postfarrowing) and finishing lots (115-120 days postfarrowing) to create production benchmark curves for anomaly detection. These curves were developed for farms without diagnosed health challenges and compared to those with diagnosed health issues, based on tissue submissions and diagnostic codes (Dx codes). Statistical methods such as resampling techniques, Bayesian statistics, and standard deviation (SD) thresholds were employed to build the benchmark curves. The main objective was to test and compare the benchmarks using detection, early detection rate (EDR), time-to-detect (TTD), and false positive rate (FPR). Results showed that bootstrapping (BOOT), jackknife (JK), and Markov chain Monte Carlo (MCMC) methods provided the highest EDRs, although they were prone to false positives. For nursery lots, it was observed that using cumulative average with one SD for feed disappearance (EDR 49. 2% and FPR 9. 8%) and estimated weight (EDR 47. 2% and FPR 8. 8%) showed the best balance between EDR and FPR, and using MCMC for mortality showed the best balance between EDR and FPR (EDR 38. 8% and FPR 13. 8%). For finishing lots, using cumulative average with one SD showed a more balanced performance with FPR below 14. 0% and EDR of 21. 3% for feed disappearance, 67. 4% weight, and 59. 7% for mortality. These findings demonstrate the potential of using production indicators for early health challenge detection in swine operations.
Nota:	Altres ajuts: National Institute of Food and Agriculture 2023-68008-39860
Drets:	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.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Bayesian statistics ; Benchmarking ; Nursery and finishing ; Resampling techniques ; Surveillance ; Swine
Publicat a:	Transboundary and emerging diseases, Vol. 2025 (august 2025) , ISSN 1865-1682

DOI: 10.1155/tbed/9952020
PMID: 40843277

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