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    Adding value to food chain information: using data on pig welfare and antimicrobial use on-farm to predict meat inspection outcomes

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    Author
    Pessoa, Joana
    McAloon, Conor
    Rodrigues da Costa, Maria
    García Manzanilla, Edgar
    Norton, Tomas
    Boyle, Laura
    Keyword
    Boosted trees
    Food chain information
    Machine learning
    meat inspection
    pig
    Welfare
    Date
    2021-10-14
    
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    URI
    http://hdl.handle.net/11019/2724
    Citation
    Pessoa, J., McAloon, C., Rodrigues da Costa, M. et al. Adding value to food chain information: using data on pig welfare and antimicrobial use on-farm to predict meat inspection outcomes. Porc Health Manag 7, 55 (2021). https://doi.org/10.1186/s40813-021-00234-x
    Abstract
    Background Using Food Chain Information data to objectively identify high-risk animals entering abattoirs can represent an important step forward towards improving on-farm animal welfare. We aimed to develop and evaluate the performance of classification models, using Gradient Boosting Machine algorithms that utilise accurate longitudinal on-farm data on pig health and welfare to predict condemnations, pluck lesions and low cold carcass weight at slaughter. Results The accuracy of the models was assessed using the area under the receiver operating characteristics (ROC) curve (AUC). The AUC for the prediction models for pneumonia, dorsocaudal pleurisy, cranial pleurisy, pericarditis, partial and total condemnations, and low cold carcass weight varied from 0.54 for pneumonia and 0.67 for low cold carcass weight. For dorsocaudal pleurisy, ear lesions assessed on pigs aged 12 weeks and antimicrobial treatments (AMT) were the most important prediction variables. Similarly, the most important variable for the prediction of cranial pleurisy was the number of AMT. In the case of pericarditis, ear lesions assessed both at week 12 and 14 were the most important variables and accounted for 33% of the Bernoulli loss reduction. For predicting partial and total condemnations, the presence of hernias on week 18 and lameness on week 12 accounted for 27% and 14% of the Bernoulli loss reduction, respectively. Finally, AMT (37%) and ear lesions assessed on week 12 (15%) were the most important variables for predicting pigs with low cold carcass weight. Conclusions The findings from our study show that on farm assessments of animal-based welfare outcomes and information on antimicrobial treatments have a modest predictive power in relation to the different meat inspection outcomes assessed. New research following the same group of pigs longitudinally from a larger number of farms supplying different slaughterhouses is required to confirm that on farm assessments can add value to Food Chain Information reports.
    Funder
    Teagasc Walsh Scholarship Programme; Meat Industry Ireland
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.1186/s40813-021-00234-x
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    Pig Development
    Pig Development
    Teagasc publications in Biomed Central

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