Bayesian network as an aid for Food Chain Information use for meat inspection.

Current ante mortem inspection involves a check of relevant Food Chain Information (FCI) transmitted by the farmer to the slaughterhouse on a regulatory FCI document. Since 2000, a farm sanitary form with FCI data has been used for all consignments of broiler chickens in France. However, the FCI needs to be standardized for the collection and interpretation of data. A study was conducted to develop an expert system, undertaken to elaborate on a simple decision support system capable of predicting whether the flocks will present a high condemnation risk, based on FCI. For this, 'optimal' (i.e. on-farm survey data) and 'worthy' (i.e. farmers' declaration on existing farm sanitary form) data quality conditions were considered to estimate the lower and upper reference bounds of the confidence that the decision-makers could have in such a tool. Chicken broiler flocks (404) were randomly selected in 15 slaughterhouses located in Western France in 2005. Condemnation proportion and farm sanitary form were collected for each selected flock. Information about health history and technical performances were also specifically collected on farm. Condemnation risk category was modelled from the on-farm collected information, using a Bayesian network and assuming this represented the optimal data quality conditions. Corresponding information declared by the farmer on the existing farm sanitary form was secondly used in the network to evaluate the impact of the uncertainty of such information on the condemnation classification obtained with the expert system. The learnt Bayesian network had 16 explanatory variables pertaining to technical characteristics and sanitary features of the flock. Using a threshold of 1% of condemned carcases to define high risk, the network sensitivity and specificity were 55% and 93%, respectively, corresponding to positive and negative predictive values of 70% and 87%. When declared existing information was used in the network, the sensitivity and specificity were 16% and 96%, respectively, corresponding to positive and negative predictive values of 57% and 80%. Results suggested that the predictive network developed may be insufficient for correctly classifying chicken flocks for targeting of management procedures, and in its current form, the expert system may be unlikely to be implemented in the field. However, it could help to improve the standardization of both form design and FCI interpretation at a national level.

Factors associated with mortality of broilers during transport to slaughterhouse.

In recent years, broiler mortality during transport to the slaughterhouse has become a cause for concern because of animal welfare considerations and associated economic losses. A descriptive and analytical epidemiological study was carried out to estimate the extent of DoA in poultry broiler production in the main producing regions of France and to determine factors influencing the DoA rate. Data regarding animal characteristics and rearing, catching, transport and lairage conditions were collected on farm and at the slaughterhouse for 404 chicken broiler flocks processed during 2005. The average DoA rate was 0.18% (from 0% to 1.4%). Variables found to be associated (P < 0.05) with the DoA rate in a multivariable negative binomial model were flock cumulative mortality on farm, the catching system (mechanical being more at risk than manual), the density in crates (more space allowance being associated with less mortality) and climatic conditions (rain and wind being associated with more DoA). Mortality during transport is thus related to all production steps from the farm to the slaughterhouse. Efforts have therefore to be made by all professionals to contain mortality on farm and during catching and transportation.

Feasibility of screening broiler chicken flocks for risk markers as an aid for meat inspection.

The feasibility of using risk markers to screen broiler chicken flocks and anticipate their risk of condemnation at meat inspection was examined in 404 randomly selected flocks in 15 French slaughterhouses in 2005. Condemnation rate and information about rearing conditions, health history, catching and loading, transport and slaughtering were collected. The Poisson regression model of the condemnation rate consisted of six simple and biologically relevant predictors: production type, frequency of farmer's visits during the starting period, health disorders during rearing, on-farm mortality, mortality during transport, and slaughter-line speed. Although accurate prediction of the condemnation rate for a given flock was not feasible, flocks with low or high risk of condemnation could be distinguished. These findings could be useful at various stages of chicken production, to monitor and improve farm husbandry practices, minimize the impact of transport conditions, and optimize meat inspection procedures.

Postmortem condemnations of processed broiler chickens in western France.

A total of 404 broiler chicken flocks processed in 15 slaughterhouses in western France were studied to estimate the condemnation prevalence and describe the official reasons for condemnation and the main macroscopic lesions observed in a sample of the condemned carcases. The condemnation rate was 87 per 10,000 birds slaughtered (95 per cent confidence interval 79 to 95 per 10,000) but differed significantly according to the type of poultry produced (standard, light, heavy or certified). The main reasons for condemnation were emaciation and congestion, with rates of 30 and 22 per 10,000 birds slaughtered, respectively. Congestion was significantly associated with arthritis and ascites, whereas infected skin lesions were associated with bruises and abnormalities of colour, odour or conformation.