Weller Daniel, Brassill Natalie, Rock Channah, Ivanek Renata, Mudrak Erika, Roof Sherry, Ganda Erika, Wiedmann Martin
Department of Food Science and Technology, Cornell University, Ithaca, NY, United States.
Department of Soil, Water and Environmental Science, University of Arizona, Maricopa, AZ, United States.
Front Microbiol. 2020 Feb 6;11:134. doi: 10.3389/fmicb.2020.00134. eCollection 2020.
Agricultural water is an important source of foodborne pathogens on produce farms. Managing water-associated risks does not lend itself to one-size-fits-all approaches due to the heterogeneous nature of freshwater environments. To improve our ability to develop location-specific risk management practices, a study was conducted in two produce-growing regions to (i) characterize the relationship between levels and pathogen presence in agricultural water, and (ii) identify environmental factors associated with pathogen detection. Three AZ and six NY waterways were sampled longitudinally using 10-L grab samples (GS) and 24-h Moore swabs (MS). Regression showed that the likelihood of detection (Odds Ratio [OR] = 2.18), and codetection (OR = 6.49) was significantly greater for MS compared to GS, while the likelihood of detecting was not. Regression also showed that codetection in AZ (OR = 50.2) and NY (OR = 18.4), and detection in AZ (OR = 4.4) were significantly associated with levels, while detection in NY was not. Random forest analysis indicated that interactions between environmental factors (e.g., rainfall, temperature, turbidity) (i) were associated with likelihood of pathogen detection and (ii) mediated the relationship between levels and likelihood of pathogen detection. Our findings suggest that (i) environmental heterogeneity, including interactions between factors, affects microbial water quality, and (ii) levels alone may not be a suitable indicator of food safety risks. Instead, targeted methods that utilize environmental and microbial data (e.g., models that use turbidity and levels to predict when there is a high or low risk of surface water being contaminated by pathogens) are needed to assess and mitigate the food safety risks associated with preharvest water use. By identifying environmental factors associated with an increased likelihood of detecting pathogens in agricultural water, this study provides information that (i) can be used to assess when pathogen contamination of agricultural water is likely to occur, and (ii) facilitate development of targeted interventions for individual water sources, providing an alternative to existing one-size-fits-all approaches.
农业用水是农产品农场食源性病原体的重要来源。由于淡水环境的异质性，管理与水相关的风险并不适合采用一刀切的方法。为了提高我们制定针对特定地点风险管理措施的能力，在两个农产品种植区开展了一项研究，以（i）描述农业用水中[具体物质]水平与病原体存在之间的关系，以及（ii）确定与病原体检测相关的环境因素。对亚利桑那州（AZ）的三条水道和纽约州（NY）的六条水道进行纵向采样，使用10升抓取式水样（GS）和24小时摩尔拭子（MS）。回归分析表明，与GS相比，MS检测到[具体病原体]的可能性（优势比[OR]=2.18）以及共同检测到[具体病原体]的可能性（OR=6.49）显著更高，而检测到[另一种具体病原体]的可能性则不然。回归分析还表明，在亚利桑那州（OR=50.2）和纽约州（OR=18.4）共同检测到[具体病原体]，以及在亚利桑那州检测到[另一种具体病原体]（OR=4.4）与[具体物质]水平显著相关，而在纽约州检测到[该病原体]则不然。随机森林分析表明，环境因素（如降雨、温度、浊度）之间的相互作用（i）与病原体检测的可能性相关，（ii）介导了[具体物质]水平与病原体检测可能性之间的关系。我们的研究结果表明，（i）环境异质性，包括因素之间的相互作用，会影响微生物水质，（ii）仅[具体物质]水平可能不是食品安全风险的合适指标。相反，需要利用环境和微生物数据的针对性方法（例如，使用浊度和[具体物质]水平来预测地表水被病原体污染风险高低的模型）来评估和减轻与收获前用水相关的食品安全风险。通过确定与农业用水中病原体检测可能性增加相关的环境因素，本研究提供了以下信息：（i）可用于评估农业用水病原体污染可能发生的时间，（ii）有助于为个别水源制定针对性干预措施，为现有的一刀切方法提供了替代方案。
