Exploring combined laboratory test and slaughter data for better decision making regarding respiratory health of finishing pigs Tijs Tobias1,3, Irene Bisschop1, Martijn Bouwknegt2, Anouk Veldhuis1, Lucía Dieste Pérez1, Inge Santman-Berends1, Gerdien van Schaik1,3 1Royal GD, Deventer, The Netherlands, 2Vion Food Group, Boxtel, The Netherlands, 3Utrecht University, Faculty of Veterinary Medicine, Utrecht, The Netherlands
Introduction Extensive diagnostic and slaugther data is generated from finishing pig farms, reflecting disease and production. Combining data from various sources may support farmers decision making. Aim: explore the added value of combining slaughter data and diagnostic test results from finishing pig farms.
Methods and Materials
Royal GD
Results • IAV + results associated with lower average and IQR of backfat thickness. (Fig. 2). • PRRSv + results associated with higher slaughter weights and muscle thickness, and lower average backfat thickness • Mhyo + results associated with higher average and IQR of net weight and backfat thickness.
Combined and anonymised data from 47 selected farms (fig 1.):
• App + results associated with average body weight and IQR and with backfat and muscle thickness.
Aggregated data: one record per class per slaughter batch, including date, outcome, explanatory and fixed parameters.
• No significant associations were found between PRRSv or IAVsw results and condemnation data.
Outcome parameters: Continuous data: (e.g., average and variation (IQR) slaughter weight, meat and backfat thickness) per record. Count data: condemnation of organs and carcass observations
• For Mhyo and App results, models for condemnation data did not converge, llikely due to selection bias of farms with specific respiratory health status
Explanatory variables: Lab test results for Influenza A virus (IAV), PRRSv, Mycoplasma hyopneumoniae (Myo) and Actinobacillus pleuropneumoniae (App) within 60 days before slaughter.
Tijs Tobias
Finishing pig stage
Conclusions • Lab test results for respiratory pathogens before slaughter are associated with slaughter weight, muscle and backfat measurements. • Results indicate that laboratory results may be predictive for outcome at slaughter and provide opportunities to further prioritise prevention and management of specific respiratory disease on pig farms.
Recommendations • Stakeholder and end-user interpretations and prioritisations, including economic considerations, are recommended for further implementation, • Extensive and more variable data recommended for models evaluation for organ condemnation data.
Slaughter stage
Data collection
laboratory result data up to 60 days pre-slaughter
Fixed variables: season, trend over time, class (sex) and slaughterhouse. Models: linear regression models, multilevel mixed-effects negative binomial regression models, or Kruskal-Wallis equality test for two non-normally distributed parameters.
Data combine & analyse
Anonymized data for 47 farms Performance and condemnation data
2020-2023 ~354.000 pigs
Fig 1. Schematic representation of stages for data origin, data management and analysis
Avg .Net weight (kg)
Models for associations between lab results for Influenza and PRRS (antibodies & PCR) provided non signicant associations
Actinobacillus pleuropneumoniae + antibody test
Mycoplasma PRRS + Influenza PRRS + hyopneumoniae + antibody test antibody test PCR test antibody test Fig 2. Results displaying significant associations between laboratory results for respiratory pathogens and specific slaughter performance parameters. Non significant associations are not shown. Magnitude and direction of association is indicated by relative size of + or – indicators. Models for condemnation of lungs or livers did not converge. All coloured images are generated with ChatGPT
DECIDE Consortium
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101000494. This document reflects only the author’s view and the Research Executive Agency (REA) and the European Commission cannot be held responsible for any use that may be made of the information it contains.
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2022