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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 eachmodel, 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 1,098mof the sampling site, while Enterococcus and coliform levels were only associated with pasture cover within 1,098m (not 122 or 366m). Ruminant markers were positively associated with pasture cover within 122m, but not 366 or 1,098m. 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. 1,098m) when developing these strategies. This work was supported by Onondaga Environmental Institute (OEI). Manuscript preparation and data analyses were supported by the National Institute of Environmental Health Sciences of the National Institutes of Health (NIH) under award number T32ES007271, and Specialty Crops Research Initiative project 2019-51181-30016 from the United States Department of Agricultural (USDA) National Institute of Food and Agriculture. Published version

Since E. coli is considered a fecal indicator in surface water, government water quality standards and industry guidance often rely on E. coli monitoring to identify when there is an increased risk of pathogen contamination of water used for produce production (e.g., for irrigation). However, studies have indicated that E. coli testing can present an economic burden to growers and that time lags between sampling and obtaining results may reduce the utility of these data. Models that predict E. coli levels in agricultural water may provide a mechanism for overcoming these obstacles. Thus, this proof-of-concept study uses previously published datasets to train, test, and compare E. coli predictive models using multiple algorithms and performance measures. Since the collection of different feature data carries specific costs for growers, predictive performance was compared for models built using different feature types [geospatial, water quality, stream traits, and/or weather features]. Model performance was assessed against baseline regression models. Model performance varied considerably with root-mean-squared errors and Kendall’s Tau ranging between 0.37 and 1.03, and 0.07 and 0.55, respectively. Overall, models that included turbidity, rain, and temperature outperformed all other models regardless of the algorithm used. Turbidity and weather factors were also found to drive model accuracy even when other feature types were included in the model. These findings confirm previous conclusions that machine learning models may be useful for predicting when, where, and at what level E. coli (and associated hazards) are likely to be present in preharvest agricultural water sources. This study also identifies specific algorithm-predictor combinations that should be the foci of future efforts to develop deployable models (i.e., models that can be used to guide on-farm decision-making and risk mitigation). When deploying E. coli predictive models in the field, it is important to note that past research indicates an inconsistent relationship between E. coli levels and foodborne pathogen presence. Thus, models that predict E. coli levels in agricultural water may be useful for assessing fecal contamination status and ensuring compliance with regulations but should not be used to assess the risk that specific pathogens of concern (e.g., Salmonella, Listeria) are present.