Factors Associated With E. coli Levels in and Salmonella Contamination of Agricultural Water Differed Between North and South Florida Waterways

Abstract

The microbial quality of agricultural water is often assessed using fecal indicator bacteria (FIB) and physicochemical parameters. The presence, direction, and strength of associations between microbial and physicochemical parameters, and the presence of human pathogens in surface water vary across space (e.g., region) and time. This study was undertaken to understand these associations in two produce-growing regions in Florida, USA, and to examine the pathogen ecology in waterways used for produce production. The relationship between Salmonella presence, and microbial and physicochemical water quality; as well as weather and land use factors were evaluated. Water samples were collected from six sites in North Florida (N = 72 samples) and eight sites in South Florida (N = 96 samples) over 12 sampling months. Land use around each sampling site was characterized, and weather and water quality data were collected at each sampling. Salmonella, generic Escherichia coli, total coliform, and aerobic plate count bacteria populations were enumerated in each sample. Univariable and multivariable regression models were then developed to characterize associations between microbial water quality (i.e., E. coli levels and Salmonella presence), and water quality, weather, and land use factors separately for North and South Florida. The E. coli and total coliforms mean concentrations (log10 MPN/100 mL) were 1.8 ± 0.6 and >3.0 ± 0.4 in North and 1.3 ± 0.6 and >3.3 ± 0.2 in South Florida waterways, respectively. While Salmonella was detected in 23.6% (17/72) of North Florida and 28.1% (27/96) of South Florida samples, the concentration ranged between <0.48 and 1.4 log10 MPN/100 mL in North Florida, and <0.48 and 3.0 log10 MPN/100 mL in South Florida. Regression analyses showed no evidence of a correlation between either log10 total coliforms or E. coli levels, and if a sample was Salmonella-positive. The factors associated with Salmonella presence and log10E. coli levels in North Florida differed from those in South Florida; no factors retrained in multivariable regression models were the same for the North and South Florida models. The differences in associations between regions highlight the complexity of understanding pathogen ecology in freshwater environments and suggest substantial differences between intra-state regions in risk factors for Salmonella contamination of agricultural water.

Salmonella Prevalence Is Strongly Associated with Spatial Factors while Listeria monocytogenes Prevalence Is Strongly Associated with Temporal Factors on Virginia Produce Farms

“…While other studies have examined the association between geospatial, physiochemical, and meteorological factors and foodborne-pathogen prevalence in the produce preharvest environment (12,14,15,17,20,21,(23)(24)(25)(26)(27)(28)(29), to our knowledge, no research has used advanced statistical methods to model weather, land use, and subregional data on pathogen prevalence in Virginia, specifically the Eastern Shore of Virginia. The Eastern Shore of Virginia has been associated with several salmonellosis outbreaks linked to tomatoes from 2002 to 2010 (1,30,31).…”

“…Studies in other produce-growing regions have compared microbial quality between both inter-and intrastate growing regions and have also observed some regional differences (12,13,15,17,26). For instance, Salmonella levels (most probable number [MPN]/100 mL) in water had an average MPN of CFU/100 mL approximately 2 log higher when water temperature was .26.7°C, regardless of region in Florida (United States), but at lower water temperatures (#26.7°C), Salmonella levels in water were 1 log CFU MPN/100 mL higher in South Florida than North Florida (12). The findings reported here, and in previous studies, highlight differences in Salmonella prevalence between intrastate growing regions and suggest the importance of performing risk assessments and management at local and regional scales.…”

“…These findings support future environmental studies on the Eastern Shore of Virginia with a temporal component to enhance sampling surveys in order to understand ecological dynamics of the environment that appear favorable for Salmonella. While there are some studies (12,25,44,45) examining associations between land use and Salmonella prevalence in environmental samples, universal trends are lacking, and the associations instead appear to be based on study or location. For example, a 2021 study, observed percent developed open space in a 1,000-to 5,000-ft buffer zone was negatively associated with the odds of agricultural water in the southwestern United States being positive for Salmonella (44).…”

“…While Salmonella and L. monocytogenes have been isolated from a variety of on-farm sources, including water and soil, potential pathogen prevalence is variable, and distribution is heterogenous (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22). For example, on New York State (United States) produce farms, the prevalence of L. monocytogenes and Salmonella across all sample types (soil, drag swab, fecal, and water) was 15.0% (88/588) and 4.6% (27/588), respectively, and farm, season, and sample type were found to be significantly associated with the frequency of pathogen-positive samples (17).…”

Population dynamics of Listeria spp., Salmonella spp., and Escherichia coli on fresh produce: A scoping review

“…These findings highlight a key knowledge gap in pre‐harvest studies, as soil, soil amendments, and irrigation water have all been highlighted as key vectors of pathogen contamination of produce (Alegbeleye et al., 2018). Specifically, several previous reports have shown that the survival of pathogens in soil and soil amendments can be influenced by factors such as moisture content and temperature (Sharma et al., 2016; Underthun et al., 2017), and the survival of pathogens in irrigation water can be influenced by factors such as pH, temperature, and turbidity (Murphy et al., 2022; Sharma et al., 2020; Weller et al., 2020). Therefore, more intensive reporting of these key characteristics can provide better insight into population dynamics outcomes on produce in pre‐harvest studies.…”