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.

Fecal contamination of surface water has been associated with multiple enteric disease outbreaks and food recalls. Thus, it is important to understand factors associated with fecal contamination of agricultural water sources. Since fecal indicator bacteria (FIB) were used to monitor surface water for potential fecal contamination, the purpose of the present study was to characterize associations between environmental factors, and (i) FIB (E. coli, Enterococcus, and coliform) levels, and (ii) host-specific fecal marker detection. This study used data collected from 224 sites along 3 waterways, which spanned an urban-rural gradient around Syracuse, New York. Between 2008 and 2017, 2,816 water samples were collected, and E. coli, Enterococcus, and/or coliform concentrations were enumerated. Thirty-one samples were also tested for human and ruminant microbial source-tracking markers. Water quality (e.g., turbidity, nitrate) and weather data were also collected for each site. Univariable Bayesian regression was used to characterize the relationship between each microbial target and land use, water quality, and weather factor. For each model, probability of direction and region of practical equivalence overlap (ROPE) were calculated to characterize the association's direction and strength, respectively. While levels of different FIB were not correlated with each other, FIB levels were associated with environmental conditions. Specifically, FIB levels were also positively associated with temperature, nutrient and sediment levels. Log10 E. coli levels increased by 0.20 (CI = 0.11, 0.31) and log10 Enterococcus levels increased by 0.68 (CI = 0.08, 1.24) for each log10 increase in salinity and nitrate, respectively. These findings may indicate that similar processes drove microbial, sediment, and nutrient contamination of the sampled watersheds. While fecal contamination was strongly associated with land use, the direction of association varied between FIBs and the buffer distance used to calculate land use metrics. E. coli levels and human marker detection were positively associated with percent pasture cover within 122, 366, and 1,098 m of the sampling site, while Enterococcus and coliform levels were only associated with pasture cover within 1,098 m (not 122 or 366 m). Ruminant markers were positively associated with pasture cover within 122 m, but not 366 or 1,098 m. These findings highlight the importance of considering (i) adjacent land use (and associated non-point sources of contamination) when developing strategies for managing fecal hazards associated in agricultural and recreational water, and (ii) spatial scale (e.g., 122 vs. 1,098 m) when developing these strategies.

Currently, on-farm food safety decisions are typically made independently of conservation considerations, often with detrimental impacts on agroecosystems. Comanaging agricultural environments to simultaneously meet conservation and food safety aims is complicated because farms are closely linked to surrounding environments, and management decisions can have unexpected environmental, economic, and food safety consequences.

L. monocytogenes was associated with more than 60 produce recalls between 2017 and 2020 including tomato, cherry, broccoli, lemon, and lime recalls. This study describes the effects of temperature, time and food substrate as factors influencing L. monocytogenes behavior on whole intact raw fruits and vegetables. A cocktail of five L. monocytogenes strains previously associated with foodborne outbreaks were used. Ten intact whole fruit and vegetable commodities were chosen based on data gaps identified in a systematic literature review. Produce investigated belong to major commodity families: Ericaceae (blackberry, raspberry, and blueberry), Rutaceae (lemon and mandarin orange), Roseaceae (sweet cherry), Solanaceae (tomato), Brassaceae (cauliflower and broccoli) and Apiaceae (carrot). Intact inoculated whole fruit and vegetable commodities were incubated at 2, 12, 22, 30 and 35 °C with relative humidities matched to typical real-world conditions. Foods were sampled (n=6) for up to 28 days, depending on temperature. Growth and decline rates were estimated using the DMFit for Excel. Growth rates were compared with ComBase modeling predictions for L. monocytogenes. Almost every experiment showed initial growth, followed by subsequent decline. L. monocytogenes was able to grow on whole intact surface of all produce tested, except for carrot. The 10 produce commodities supported growth of L. monocytogenes at 22 and 35°C. Growth and survival at 2 and 12°C varied by produce commodity. The standard deviation of the square root growth and decline rates showed significantly larger variability in both growth and decline rates within replicates as temperature increased. When L. monocytogenes growth occurred, it was conservatively modeled by ComBase Predictor, and growth was generally followed by decreases in concentration. This research will assist in understanding the risks of foodborne disease outbreaks and recalls associated with L. monocytogenes on fresh whole produce.

This study sought to examine different growing regions across the state of Virginia and to determine how factors associated with pathogen prevalence may differ between regions. Spatial and temporal data were modeled to identify factors associated with an increased pathogen likelihood in various on-farm sources.