Approximately 13% of reported foodborne outbreaks are linked to produce; many of these outbreaks have been traced back to contamination originating from the preharvest environment. Salmonella is a key pathogen of concern in produce production. For example, in 2005 a Salmonella outbreak in tomatoes was traced back to contaminated surface water that was used for irrigation. The outbreak of Salmonella Newport had an estimated 72 cases of illness in 16 states. <P>We propose that risk factors that influence the likelihood of Salmonella contamination in the preharvest environment can be classified into three major categories, including (i) landscape features, (ii) meteorological events and (iii) management practices. This research project will use extensive microbiological, environmental and farm management data to predict specific risk factors (e.g., spatial locations, management practices) that increase the risk of Salmonella contamination in the preharvest environment. By modeling Salmonella contamination in the preharvest environment as an ecological process, we aim to supply farmers with science-based recommendations to minimize the risk of preharvest contamination (e.g., changes to farm management practices; avoidance of well-defined spatial areas that present a high contamination risk). <P>This goal will be achieved through the following aims: Objective 1: Use molecular subtyping methods to identify potential sources, persistence and diversity of Salmonella in produce fields; Objective 2: Determine associations between produce field management practices and the likelihood of a Salmonella positive field in the preharvest environment; Objective 3: Determine meteorological and landscape factors that predict the likelihood of Salmonella in the preharvest environment; Objective 4: Develop and independently validate geospatial algorithms predicting spatial locations favorable for Salmonella in the preharvest environment.
Produce safety is an issue of increasing concern due to multiple factors that may be driving an increase in the number of foodborne disease outbreaks. First, the consumption of fresh fruits and vegetables is actively promoted by the United States and other world governments as an important component of a healthy diet. Second, produce commodities are commonly consumed raw in the United States and Europe, which may contribute to a heightened risk of foodborne disease. Produce associated outbreaks have the highest number of foodborne illness cases compared to other commodity associated outbreaks. The foodborne pathogen, Salmonella, accounts for approximately half of the produce associated outbreaks in the US. Therefore, this research project is designed to identify risk factors that contribute to Salmonella contamination and predict locations favorable for Salmonella at the farm and field scale in the produce preharvest environment.
An improved understanding of risk factors, including (i) landscape features (e.g., proximity to pastures), (ii) meteorological events (e.g., precipitation), and (iii) management practices (e.g., manure usage) is key to minimizing contamination of produce in the preharvest environment. A combination of microbiological, statistical and geographical information system (GIS) approaches will aid in the completion of this project. Ultimately, the overarching goal of the project is to minimize the risk of produce associated outbreaks by supplying growers with recommendations, in order to reduce the likelihood of preharvest contamination of produce. For instance, one recommendation may be to plant higher risk produce crops (e.g., leafy greens) in fields predicted to have a lower Salmonella prevalence, thus reducing the likelihood of contamination.
Methods to be used are detailed to correspond to the four main objectives of our project.
<br/>Obj 1: Subtyping is an important tool that can be utilized to identify and characterize potential sources, persistence, and diversity of foodborne pathogens in the environment. Salmonella isolates obtained during all phases of the project will be serotyped and pulsed field gel electrophoresis (PFGE). Subtype data will be analyzed to identify likely sources of contamination by comparing the subtypes of the Salmonella isolates from produce environments to subtype data from other sources. This comparison will be performed at the level of serotype diversity and using PFGE data. Comparison of PFGE data will be performed as it has been shown that some PFGE types within a given serotype can be associated with specific hosts. Data created through this aim may also facilitate identification of specific contamination patterns, which may identify risk factors for Salmonella presence. These analyses will test the hypothesis that persistence of a Salmonella subtype may be due to an environmental factor(s) that represent or promotes favorable conditions for Salmonella survival outside the host.
<br/>Obj 2: A commonly used approach to address produce preharvest food safety issues is to recommend farm management practices (e.g., water usage) that are thought to improve food safety (e.g., by reducing pathogen prevalence). We will administer questionnaires to growers about specific field level practices and collect environmental samples that will be tested for Salmonella, from the corresponding fields for which management practices were evaluated through the questionnaires. The resulting data will be used for formal statistical and epidemiological analyses. These analyses will identify risk factors that are significantly associated with an increased or decreased prevalence of Salmonella. For example, these analyses may show that fields that are irrigated with surface water are more likely to be positive for Salmonella; this type of outcome would then help develop a management practice to reduce the risk surface water poses.
<br/>Obj 3: Salmonella is in a constant flux between hosts and the environment. Classification tree (CT) analysis can be used as a technique to predict factors that may contribute to an environment being favorable for Salmonella. We will collect meteorological and landscape data for all environmental samples collected during the scope of the project to develop CTs for meteorological and landscape factors to help identify factors that promote favorable conditions and locations for Salmonella with the ultimate goal of using this information to develop further recommendations for strategies to minimize preharvest Salmonella contamination.
<br/>Obj 4: As the factors identified in the CT will be merely exploratory, unless validated, we aim to independently validate the CT. We will do this by using the factors identified by the CT to determine locations in fields where the CT predicts higher or lower Salmonella prevalence. Independent validation of predicted locations will be vital to determine if the CT rules are accurate predictors for Salmonella contamination.