Relationship between animal-based on-farm indicators and meat inspection data in pigs.

BACKGROUND: This study aimed to validate slaughterhouse indicators collected during meat inspection as an alternative to on-farm animal welfare indicators. For this purpose, the assessments of twelve on-farm and seven slaughterhouse indicators of 628 pigs from three different farms were combined into three indices, differentiated between on-farm and slaughterhouse: (1) limb health, (2) other organ health, and (3) respiratory health. At first, an assessment at animal-level using agreement parameters was carried out to ascertain whether the same welfare or health issues were identified on-farm and at slaughterhouse, taking the production period (farrowing, rearing and fattening period) and the last weeks before slaughtering into account. Second, the connection of slaughterhouse findings on the individual on-farm health indices was examined using logistic regressions, to determine whether certain welfare issues can be better monitored using slaughterhouse indicators. RESULTS: Acceptable agreement was determined using the Prevalence-Adjusted Bias-Adjusted Kappa (PABAK) for the farrowing and fattening period, but not for the rearing period. A more detailed analysis of the weeks before slaughter shows that there is still a poor agreement 8 weeks before slaughter and an acceptable agreement 4 weeks before slaughter. This indicated the slaughterhouse indicators pneumonia, pleuritis and pericarditis as possible estimators of fever and deviant behavior on-farm and the slaughterhouse indicators bursitis and joint inflammations as possible estimators of lameness. In the second part of the analysis, the connection of slaughterhouse findings on the individual on-farm health indices was investigated; a significant influence of the farm on the limb and respiratory indices and no significant influence of the slaughterhouse findings could be determined, provided that all weekly assessments during the lifetime of the pigs have been taken into account. However, an influence of the slaughterhouse findings on the respiratory index and on the other organ index could be determined if only the weekly assessments four and eight weeks before slaughter, respectively, were taken into account. CONCLUSIONS: In general, the possible suitable indicators detected by the PABAK, could replace some health-related indicators but a complete substitution of on-farm welfare assessment is not possible. In addition, the traceability over time must be investigated further.

Enhancing the value of meat inspection records for broiler health and welfare surveillance: longitudinal detection of relational patterns.

BACKGROUND: Abattoir data are under-used for surveillance. Nationwide surveillance could benefit from using data on meat inspection findings, but several limitations need to be overcome. At the producer level, interpretation of meat inspection findings is a notable opportunity for surveillance with relevance to animal health and welfare. In this study, we propose that discovery and monitoring of relational patterns between condemnation conditions co-present in broiler batches at meat inspection can provide valuable information for surveillance of farmed animal health and welfare. RESULTS: Great Britain (GB)-based integrator meat inspection records for 14,045 broiler batches slaughtered in nine, four monthly intervals were assessed for the presence of surveillance indicators relevant to broiler health and welfare. K-means and correlation-based hierarchical clustering, and association rules analyses were performed to identify relational patterns in the data. Incidence of condemnation showed seasonal and temporal variation, which was detected by association rules analysis. Syndrome-related and non-specific relational patterns were detected in some months of meat inspection records. A potentially syndromic cluster was identified in May 2016 consisting of infection-related conditions: pericarditis, perihepatitis, peritonitis, and abnormal colour. Non-specific trends were identified in some months as an unusual combination of condemnation reasons in broiler batches. CONCLUSIONS: We conclude that the detection of relational patterns in meat inspection records could provide producer-level surveillance indicators with relevance to broiler chicken health and welfare.

A time-series approach for clustering farms based on slaughterhouse health aberration data.

A large amount of data is collected routinely in meat inspection in pig slaughterhouses. A time series clustering approach is presented and applied that groups farms based on similar statistical characteristics of meat inspection data over time. A three step characteristic-based clustering approach was used from the idea that the data contain more info than the incidence figures. A stratified subset containing 511,645 pigs was derived as a study set from 3.5 years of meat inspection data. The monthly averages of incidence of pleuritis and of pneumonia of 44 Dutch farms (delivering 5149 batches to 2 pig slaughterhouses) were subjected to 1) derivation of farm level data characteristics 2) factor analysis and 3) clustering into groups of farms. The characteristic-based clustering was able to cluster farms for both lung aberrations. Three groups of data characteristics were informative, describing incidence, time pattern and degree of autocorrelation. The consistency of clustering similar farms was confirmed by repetition of the analysis in a larger dataset. The robustness of the clustering was tested on a substantially extended dataset. This confirmed the earlier results, three data distribution aspects make up the majority of distinction between groups of farms and in these groups (clusters) the majority of the farms was allocated comparable to the earlier allocation (75% and 62% for pleuritis and pneumonia, respectively). The difference between pleuritis and pneumonia in their seasonal dependency was confirmed, supporting the biological relevance of the clustering. Comparison of the identified clusters of statistically comparable farms can be used to detect farm level risk factors causing the health aberrations beyond comparison on disease incidence and trend alone.