High-performance simulation of disease outbreaks in growing-finishing pig herds raised by the precision feeding method

• Published in: Computers and electronics in agriculture Vol. 225; p. 109335
• Main Authors: Pham, Linh Manh, Le, Duc-Toan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-10-2024
• Subjects: Agent-based model; Disease outbreak; Precision feeding system; Simulation; Swine; Disease outbreak; Precision feeding system; Simulation; Swine; Agent-based model
• ISSN: 0168-1699
• DOI: 10.1016/j.compag.2024.109335

•An advanced simulation model of the precision feeding system for pigs is presented.•A state machine behavior model is combined with a resilience and resistance model.•A disease outbreak model is integrated into the advanced precision feeding model.•A high-performance simulation program is created to run disease control scenarios.
Perturbations always affect livestock during the breeding process, including harmful diseases. Researching the impact of disease outbreaks on pig herds is extremely important so that disease control measures can be applied early. However, conducting practical experiments on disease outbreaks is extremely expensive. Precision feeding systems (PFS) for pigs use data on the animal’s own feed intake to calculate the appropriate amount of feed for each individual. This helps increase productivity and product quality while contributing to reducing waste generation in the environment. Daily feed intake (DFI) and cumulative feed intake (CFI) data can be automatically collected and estimated from the PFS, which can help detect or predict disease outbreaks. In this article, we introduce an advanced simulation model of the PFS for pigs and the integration of disease outbreak models into this system. A disease outbreak simulation application within the pig herd raised by the precision feeding method is also developed for running high-performance experimental simulations. The results of the simulation scenarios are analyzed and compared with data from a real-world experiment to assess the accuracy of the application. The correlation coefficient values of DFI in all scenarios fall within the range of 0.25 to 0.5, suggesting almost no correlation between simulated DFI and actual DFI. The overall average correlation coefficient of CFI for all scenarios is 0.764, falling within the strong correlation range. It can be concluded that the simulation accurately represents CFI values compared to reality.