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Abstract
Consumer demand has increased for pastured poultry products in the recent years. It is necessary to identify the meteorological factors and farm management and processing practices associated with the prevalence of Salmonella on pastured poultry farms. Presence of Salmonella in the environment could lead to contamination of the final product. The objective of this study was to develop predictive models that identify the specific meteorological, farm management and processing factors that contribute to the presence of Salmonella, samples including soil, feces, and whole carcass rinses. Random forest method was used to develop the models, and receiver operating characteristic (ROC) curves were used to evaluate the performances of these models. All models generated in this study had predicting abilities with the area under the ROC curve values above 0.87. The predictive models developed in this study can provide users practical and effective tool to make informed decisions based on scientific evidence.