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Predicting Risk of Ammonia Exposure in Broiler Housing: Correlation with Incidence of Health Issues

Publicated to:Animals. 14 (4): 615- - 2024-02-01 14(4), DOI: 10.3390/ani14040615

Authors: Barbosa, Leonardo V S; Lima, Nilsa Duarte da Silva; Barros, Juliana de Souza Granja; de Moura, Daniella Jorge; Estelles, Fernando; Ramon-Moragues, Adrian; Calvet-Sanz, Salvador; Garcia, Arantxa Villagra

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Abstract

Simple 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 (R), 42 days) and slow growing (Hubbard (R), 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.Abstract The study aimed to forecast ammonia exposure risk in broiler chicken production, correlating it with health injuries using machine learning. Two chicken breeds, fast-growing (Ross (R)) and slow-growing (Hubbard (R)), were compared at different densities. Slow-growing birds had a constant density of 32 kg m-2, while fast-growing birds had low (16 kg m-2) and high (32 kg m-2) densities. Initial feeding was uniform, but nutritional demands led to varied diets later. Environmental data underwent selection, pre-processing, transformation, mining, analysis, and interpretation. Classification algorithms (decision tree, SMO, Naive Bayes, and Multilayer Perceptron) were employed for predicting ammonia risk (10-14 pmm, Moderate risk). Cross-validation was used for model parameterization. The Spearman correlation coefficient assessed the link between predicted ammonia risk and health injuries, such as pododermatitis, vision/affected, and mucosal injuries. These injuries encompassed trachea, bronchi, lungs, eyes, paws, and other issues. The Multilayer Perceptron model emerged as the best predictor, exceeding 98% accuracy in forecasting injuries caused by ammonia. The correlation coefficient demonstrated a strong association between elevated ammonia risks and chicken injuries. Birds exposed to higher ammonia concentrations exhibited a more robust correlation. In conclusion, the study effectively used machine learning to predict ammonia exposure risk and correlated it with health injuries in broiler chickens. The Multilayer Perceptron model demonstrated superior accuracy in forecasting injuries related to ammonia (10-14 pmm, Moderate risk). The findings underscored the significant association between increased ammonia exposure risks and the incidence of health injuries in broiler chicken production, shedding light on the importance of managing ammonia levels for bird welfare.

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