scholarly journals Web Delivery of a Monte Carlo Simulation Model: The Base and Yield Analyzer Experience

2005 ◽  
Vol 37 (2) ◽  
pp. 425-431 ◽  
Author(s):  
James W. Richardson ◽  
Joe L. Outlaw

The provision for producers to update base acres and payment yields in the 2002 farm bill afforded an opportunity to test whether it was feasible to deliver a complex simulation model directly to producers. A Monte Carlo simulation model for assessing the economic impacts of the alternative base and yield options on individual farms was developed and made available to producers via the World Wide Web. The experiences and challenges from this collaborative extension and research effort are described, as well as the issues educators might consider before delivering complex software to a national audience via the Web.

Author(s):  
Thomas Oscar

The first step in quantitative microbial risk assessment (QMRA) is to determine distribution of pathogen contamination among servings of the food at some point in the farm-to-table chain. In the present study, distribution of Salmonella contamination among servings of chicken liver for use in QMRA was determined at meal preparation. A combination of five methods: 1) whole sample enrichment; 2) quantitative polymerase chain reaction; 3) cultural isolation; 4) serotyping; and 5) Monte Carlo simulation were used to determine Salmonella prevalence (P), number (N), and serotype for different serving sizes. In addition, epidemiological data were used to convert serotype data to virulence (V) values for use in QMRA. A Monte Carlo simulation model based in Excel and simulated with @Risk predicted Salmonella P, N, serotype, and V as a function of serving size from one (58 g) to eight (464 g) chicken livers. Salmonella P of chicken livers was 72.5% (58/80) per 58 g. Four serotypes were isolated from chicken livers: 1) Infantis (P = 28%, V = 4.5); 2) Enteritidis (P = 15%, V = 5); 3) Typhimirium (P = 15%, V = 4.8); and 4) Kentucky (P = 15%, V = 0.8). Median Salmonella N was 1.76 log per 58 g (range: 0 to 4.67 log/58 g) and was not affected ( P > 0.05) by serotype. The model predicted a non-linear increase ( P ≤ 0.05) of Salmonella P from 72.5% per 58 g to 100% per 464 g, minimum N from 0 log per 58 g to 1.28 log per 464 g, and median N from 1.76 log per 58 g to 3.22 log per 464 g. Regardless of serving size, predicted maximum N was 4.74 log, mean V was 3.9, and total N was 6.65 log per lot (10,000 chicken livers). The data acquired and model developed in this study fill an important data and modeling gap in QMRA for Salmonella and chicken liver.


2008 ◽  
Vol 28 (12) ◽  
pp. 2388-2393
Author(s):  
王翔 Wang Xiang ◽  
裴香涛 Pei Xiangtao ◽  
邵鹏 Shao Peng ◽  
黄文浩 Huang Wenhao

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