Towards an industry-wide, multilevel evaluation framework for pig meat inspection: potential applications and implementation challenges

Abstract

Meat inspection (MI) data can be useful as a monitoring tool of animal health and welfare on farm, thereby enhancing the sustainability and productivity of livestock. There is also concern about certain limitations of these data, primarily related to the quality and harmonisation of inspections across various slaughterhouses. In our study, we investigated the development of a cross-slaughterhouse ranking system for farmers using MI data. The integration of new digital tools in Germany, such as the web-based database Qualifood®, offers new opportunities for collecting and utilising MI data across different slaughterhouses, enabling at the same time digital feedback of these information to livestock farmers. To accomplish our research goal, MI data over a period of 5 years (2020–2024) was exported from Qualifood®. Our analysis was conducted using MI data from both cattle and pig farms. However, this manuscript focuses on presenting the statistical analysis model using pig data and the category “respiratory health” as a representative case study. We presented an annual overview of reference values -the so-called basic risk- for respiratory health findings using generalised linear mixed models. The basic risk of respiratory health findings for pigs showed a gradual decline from 14.4% in 2020 to approximately 12.0% in 2024. The calculated basic risks establish a reference for normal finding rates and provide a baseline assessment of respiratory health in southern Germany based on MI data. Furthermore, we estimated the variability of key random effects derived. Across all years, SDs for farm and delivery levels remain relatively stable between the selected and full datasets, indicating consistent variability at these levels. However, the slaughterhouse-level SDs are substantially higher in the full dataset compared to the selected slaughterhouses suggesting notable heterogeneity in reporting or detection practices across facilities. This underlines the importance of slaughterhouse selection when conducting cross-facility analyses and benchmarking. Towards a cross-slaughterhouse evaluation, we compare the farmer-specific risks and the basic risk using the concept of relative risk, also known as risk ratio. Our model demonstrates how recent advancements in digitalisation enable the evaluation of MI data across multiple slaughterhouses, underscoring the importance of region-wide, digital, and standardised MI data collection as a foundation for consistent and reliable cross-slaughterhouse assessments. By addressing inconsistencies in recording quality, our model can support a data-driven decision-making for farmers, industry stakeholders, and veterinary authorities, ultimately reinforcing the entire agricultural value chain and animal health and welfare management.