• Abstract

    Respiratory disease is the most significant issue in the pig industry and leads to high mortality and reduced growth rates. Housing conditions are crucial for maintaining the health and welfare of animals to optimize growth rates. This study aimed to evaluate the respiratory health status of pigs raised in different housing conditions using artificial intelligence (AI) technology and to determine the relationships between respiratory health and environmental factors. Eighty growing pigs (Largewhite × Landrace) × Duroc) were randomly distributed into two housing conditions: the control group, which had normal housing conditions, and the treatment group, which had poor housing conditions. A two-sample t test was performed to compare the groups, and a cross-correlation analysis was conducted to determine associations between respiratory health and days of exposure to environmental factors. The results show significant (p < 0.001) lower incidence of respiratory health status along with a significant (p < 0.001) greater frequency of coughing in pigs raised under poor housing conditions. Respiratory health is negatively associated with high exposure to temperature, relative humidity, and CO2 at lags 0 (R = -0.873, -0.887, and -0.869, respectively), -1 (R = -0.708, -0.742, and -0.705, respectively), and -2 (R = -0.545, -0.590, and -0.599, respectively). Furthermore, respiratory health was negatively associated with high exposure to NH3 (R = -0.420) at lag 0. In conclusion, AI technology is an effective tool for monitoring respiratory health. However, further evaluation and calibration of the alarm system threshold are recommended. These findings suggest that maintaining proper control of environmental factors can help prevent respiratory diseases in pigs.

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How to cite

Lagua, E., Mun, H.-S., Ampode, K. M. B., Chem, V., Park, H.-R., Kim, Y.-H., & Yang, C.-J. (2024). Monitoring using artificial intelligence reveals critical links between housing conditions and respiratory health in pigs. Journal of Animal Behaviour and Biometeorology, 12(1), 2024008. https://doi.org/10.31893/jabb.2024008
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