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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
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 algo...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518164/ https://www.ncbi.nlm.nih.gov/pubmed/34649629 http://dx.doi.org/10.1186/s40813-021-00234-x |
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author | Pessoa, Joana McAloon, Conor Rodrigues da Costa, Maria García Manzanilla, Edgar Norton, Tomas Boyle, Laura |
author_facet | Pessoa, Joana McAloon, Conor Rodrigues da Costa, Maria García Manzanilla, Edgar Norton, Tomas Boyle, Laura |
author_sort | Pessoa, Joana |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8518164 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-85181642021-10-20 Adding value to food chain information: using data on pig welfare and antimicrobial use on-farm to predict meat inspection outcomes Pessoa, Joana McAloon, Conor Rodrigues da Costa, Maria García Manzanilla, Edgar Norton, Tomas Boyle, Laura Porcine Health Manag Research 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. BioMed Central 2021-10-14 /pmc/articles/PMC8518164/ /pubmed/34649629 http://dx.doi.org/10.1186/s40813-021-00234-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Pessoa, Joana McAloon, Conor Rodrigues da Costa, Maria García Manzanilla, Edgar Norton, Tomas Boyle, Laura Adding value to food chain information: using data on pig welfare and antimicrobial use on-farm to predict meat inspection outcomes |
title | Adding value to food chain information: using data on pig welfare and antimicrobial use on-farm to predict meat inspection outcomes |
title_full | Adding value to food chain information: using data on pig welfare and antimicrobial use on-farm to predict meat inspection outcomes |
title_fullStr | Adding value to food chain information: using data on pig welfare and antimicrobial use on-farm to predict meat inspection outcomes |
title_full_unstemmed | Adding value to food chain information: using data on pig welfare and antimicrobial use on-farm to predict meat inspection outcomes |
title_short | Adding value to food chain information: using data on pig welfare and antimicrobial use on-farm to predict meat inspection outcomes |
title_sort | adding value to food chain information: using data on pig welfare and antimicrobial use on-farm to predict meat inspection outcomes |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518164/ https://www.ncbi.nlm.nih.gov/pubmed/34649629 http://dx.doi.org/10.1186/s40813-021-00234-x |
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