Predicting Risk of Ammonia Exposure in Broiler Housing: Correlation with Incidence of Health Issues
This study assesses the risk of ammonia exposure in broiler chicken production and correlates these risks with health issues, utilizing machine learning techniques. Two broiler breeds, fast-growing (Ross®, 42 days) and slow growing (Hubbard®, 63 days), were studied at different densities. Slow-growi...
| Main Authors: | , , , , , , , |
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| Format: | article |
| Language: | Inglés |
| Published: |
MDPI
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/20.500.11939/8846 https://www.mdpi.com/2076-2615/14/4/615 |
| Summary: | This study assesses the risk of ammonia exposure in broiler chicken production and correlates these risks with health issues, utilizing machine learning techniques. Two broiler breeds, fast-growing (Ross®, 42 days) and slow growing (Hubbard®, 63 days), were studied at different densities. Slow-growing birds had a fixed density of 32 kg/m2, while fast-growing ones were housed at low (16 kg/m2) and high (32 kg/m2) densities. The high concentration of atmospheric ammonia has been associated with a greater occurrence of bird health problems, such as pododermatitis, visual impairment and mucosal lesions compared to birds stocked in controlled environments with low concentrations of atmospheric ammonia. A total of 1250 birds were used, and classification algorithms (decision tree, SMO, Naive Bayes, and Multilayer Perceptron) were applied to predict ammonia risk levels. The analysis involved data selection, pre-processing, transformation, mining, and interpretation of results. The Multilayer Perceptron proved the most effective in predicting exposure risk. The Spearman’s correlation coefficient indicated a strong correlation between high ammonia concentrations and higher incidences of injuries in the birds that were evaluated. This research highlights the importance of managing ammonia levels in broiler production to mitigate health risks for both fast- and slow-growing breeds. |
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