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

Evaluating swine disease occurrence on farms using the state-space model based on meat inspection data: a time-series analysis

BACKGROUND

Data on abnormal health conditions in animals obtained from slaughter inspection are important for identifying problems in fattening management. However, methods to objectively evaluate diseases on farms using inspection data has not yet been well established. It is important to assess fattening management on farms using data obtained from slaughter inspection. In this study, we developed the state-space model to evaluate swine morbidity using slaughter inspection data.

RESULTS

The most appropriate model for each disease was constructed using the state-space model. Data on 11 diseases in slaughterhouses over the past 4 years were used to build the model. The model was validated using data from 14 farms. The local-level model (the simplest model) was the best model for all diseases. We found that the analysis of slaughter data using the state-space model could construct a model with greater accuracy and flexibility than the ARIMA model. In this study, no seasonality or trend model was selected for any disease. It is thought that models with seasonality were not selected because diseases in swine shipped to slaughterhouses were the result of illness at some point during the 6-month fattening period between birth and shipment.

CONCLUSION

Evaluation of previous diseases helps with the objective understanding of problems in fattening management. We believe that clarifying how farms manage fattening of their pigs will lead to improved farm profits. In that respect, it is important to use slaughterhouse data for fattening evaluation, and it is extremely useful to use mathematical models for slaughterhouse data. However, in this research, the model was constructed on the assumption of normality and linearity. In the future, we believe that we can build a more accurate model by considering models that assume non-normality and non-linearity.

Forecasting of daily new lumpy skin disease cases in Thailand at different stages of the epidemic using fuzzy logic time series, NNAR, and ARIMA methods

Lumpy skin disease (LSD) is an important transboundary disease affecting cattle in numerous countries in various continents. In Thailand, LSD is regarded as a serious threat to the cattle industry. Disease forecasting can assist authorities in formulating prevention and control policies. Therefore, the objective of this study was to compare the performance of time series models in forecasting a potential LSD epidemic in Thailand using nationwide data. For the forecasting of daily new cases, fuzzy time series (FTS), neural network auto-regressive (NNAR), and auto-regressive integrated moving average (ARIMA) models were applied to various datasets representing the different stages of the epidemic. Non-overlapping sliding and expanding window approaches were also employed to train the forecasting models. The results showed that the FTS outperformed other models in five of the seven validation datasets based on various error metrics. The predictive performance of the NNAR and ARIMA models was comparable, with NNAR outperforming ARIMA in some datasets and vice versa. Furthermore, the performance of models built from sliding and expanding window techniques was different. This is the first study to compare the forecasting abilities of the FTS, NNAR, and ARIMA models across multiple phases of the LSD epidemic. Livestock authorities and decision-makers may incorporate the forecasting techniques demonstrated herein into the LSD surveillance system to enhance its functionality and utility.

Development of a Porcine Slaughterhouse Kidney Perfusion Model

Machine perfusion techniques are becoming standard care in the clinical donation and transplantation setting. However, more research is needed to understand the mechanisms of the protective effects of machine perfusion. For preservation related experiments, porcine kidneys are acceptable alternatives to human kidneys, because of their size and similar physiology. In this experiment, the use of slaughterhouse kidneys was evaluated with normothermic kidney perfusion (NKP), thereby avoiding the use of laboratory animals. Porcine kidneys were derived from two local abattoirs. To induce different degrees of injury, different warm ischemic times and preservation techniques were used. After preservation, kidneys were reperfused for 4 h with two different NKP solutions to test renal function and damage. The effect of the preservation technique or a short warm ischemic time was clearly seen in functional markers, such as creatinine clearance and fractional sodium excretion levels, as well as in the generic damage marker lactate dehydrogenase (LDH). Porcine slaughterhouse kidneys are a useful alternative to laboratory animals for transplantation- and preservation-related research questions. To maintain kidney function during NKP, a short warm ischemic time or hypothermic machine perfusion during the preservation phase are mandatory.

Agricultural Holdings and Slaughterhouses' Impact on Patterns of Pathological Findings Observed during Post-Mortem Meat Inspection

Simple Summary

The pig sector represents one of the most economically important farming sectors in the European Union. Farmers and their veterinarians are responsible for ensuring animal health as well as sending healthy animals for slaughter. At slaughter, official veterinarians conduct meat inspections to ensure that only meat fit for human consumption enters the food processing chain. The data collected during meat inspections can provide valuable information about herd health for the farmers, the attending veterinarians and veterinary authorities. The aim of this study was to describe the data and analyse whether these data are suitable to be used as a feedback system for farmers to enable them to improve their herd management. Data used comprised all meat inspection findings in 2016 from 4.6 million pigs originating from 9172 agricultural holdings (farms) and slaughtered in 66 slaughterhouses in Austria. Analysis showed that diseases of the lung (pneumonia, 21.9%) and of the liver (milk spots, 19.9%) were most frequently detected. The agricultural holdings, the slaughterhouses and the time periods (quarter) had an influence on the observed occurrence and composition of the prevalence of post-mortem findings. Furthermore, within the slaughterhouses, the recorded patterns of pathological findings differed, which points towards the need for further harmonisation to provide high-quality feedback to farmers and veterinarians.

Abstract

Meat inspection data can provide valuable information about herd health to producers, veterinarians and veterinary authorities and can be used as a feedback system for farmers to improve their herd management. The aim of this study was to analyse the influence of agricultural holdings, slaughterhouses and time periods (quarters) on the occurrence and composition of the prevalence of post-mortem findings of 4 million pigs slaughtered in Austria in 2016, by applying a permutation multivariate analysis of variance. Pneumonia (21.9%) and milk spots (19.9%) were the most frequently recorded conditions. Our analysis indicated a statistically significant influence of all three considered factors (agricultural holdings, slaughterhouses and periods) on the prevalence of post-mortem findings. The observed prevalence could not only be explained by the differences between the farms of origin and slaughterhouses but also by the variability within the slaughterhouses. Much of the explained variance of the prevalence was due to differences between producers (mean R² = 0.61), followed by slaughterhouses (mean R² = 0.19) and period (mean R² = 0.05). To meet the demand for a valid feedback system to farmers and attending veterinarians, a robust and ideally more detailed recording of frequent pathologies, especially those affecting the respiratory tract and the liver, should be developed.