New Tool to Simulate Microbial Contamination of on-Farm Produce: Agent-Based Modeling and Simulation
2017
Han, S., National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA), Wanju, Republic of Korea | Lee, K.H., National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA), Wanju, Republic of Korea | Yang, S.G., Sungkyunkwan University, Suwon, Republic of Korea | Kim, H.Y., RDA, Jeonju, Republic of Korea | Kim, H.J., National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA), Wanju, Republic of Korea | Ryu, J.G., National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA), Wanju, Republic of Korea
This study was conducted to develop an agent-based computing platform enabling simulation of on-farm produce contamination by enteric foodborne pathogens, which is herein called PPMCS (Preharvest Produce Microbial Contamination Simulator). Also, fecal contamination of preharvest produce was simulated using PPMCS. Although Agent-based Modeling and Simulation, the tool applied in this study, is rather popular in where socio-economical human behaviors or ecological fate of animals in their niche are to be predicted, the incidence of on-farm produce contamination which are thought to be sporadic has never been simulated using this tool. The agents in PPMCS including crop, animal as a source of fecal contamination, and fly as a vector spreading the fecal contamination are given their intrinsic behaviors that are set to be executed at certain probability. Once all these agents are on-set following the intrinsic behavioral rules, consequences as the sum of all the behaviors in the system can be monitored real-time. When fecal contamination of preharvest produce was simulated in PPMCS as numbers of animals, flies, and initially contaminated plants change, the number of animals intruding cropping area affected most on the number of contaminated plants at harvest. For further application, the behaviors and variables of the agents are adjustable depending on user's own scenario of interest. This feature allows PPMCS to be utilized in where different simulating conditions are tested.
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