Department of Food Science and Technology, Virginia Tech, Blacksburg, Virginia, USA.
Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.
Appl Environ Microbiol. 2023 Feb 28;89(2):e0152922. doi: 10.1128/aem.01529-22. Epub 2023 Feb 2.
The heterogeneity of produce production environments complicates the development of universal strategies for managing preharvest produce safety risks. Understanding pathogen ecology in different produce-growing regions is important for developing targeted mitigation strategies. This study aimed to identify environmental and spatiotemporal factors associated with isolating Salmonella and from environmental samples collected from 10 Virginia produce farms. Soil ( = 400), drag swab ( = 400), and irrigation water ( = 120) samples were tested for Salmonella and , and results were confirmed by PCR. Salmonella serovar and species were identified by the Kauffmann-White-Le Minor scheme and partial sequencing, respectively. Conditional forest analysis and Bayesian mixed models were used to characterize associations between environmental factors and the likelihood of isolating Salmonella, Listeria monocytogenes (LM), and other targets (e.g., spp. and Salmonella enterica serovar Newport). Surrogate trees were used to visualize hierarchical associations identified by the forest analyses. Salmonella and LM prevalence was 5.3% (49/920) and 2.3% (21/920), respectively. The likelihood of isolating Salmonella was highest in water samples collected from the Eastern Shore of Virginia with a dew point of >9.4°C. The likelihood of isolating LM was highest in water samples collected in winter from sites where <36% of the land use within 122 m was forest wetland cover. Conditional forest results were consistent with the mixed models, which also found that the likelihood of detecting Salmonella and LM differed between sample type, region, and season. These findings identified factors that increased the likelihood of isolating Salmonella- and LM-positive samples in produce production environments and support preharvest mitigation strategies on a regional scale. 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. The findings of the study show that prevalence of Salmonella and L. monocytogenes is low overall in the produce preharvest environment but does vary by space (e.g., region in Virginia) and time (e.g., season), and the likelihood of pathogen-positive samples is influenced by different spatial and temporal factors. Therefore, the results support regional or scale-dependent food safety standards and guidance documents for controlling hazards to minimize risk. This study also suggests that water source assessments are important tools for developing monitoring programs and mitigation measures, as spatiotemporal factors differ on a regional scale.
农产品生产环境的异质性使得制定通用策略来管理收获前农产品安全风险变得复杂。了解不同农产品种植地区的病原体生态学对于制定有针对性的缓解策略很重要。本研究旨在确定与从弗吉尼亚州 10 个农产品农场采集的环境样本中分离出沙门氏菌和李斯特菌相关的环境和时空因素。对土壤( = 400)、拖拉拭子( = 400)和灌溉水( = 120)样本进行了沙门氏菌和李斯特菌的检测,结果通过 PCR 进行了确认。沙门氏菌血清型和李斯特菌种分别通过 Kauffmann-White-Le Minor 方案和部分 测序进行鉴定。条件森林分析和贝叶斯混合模型用于描述环境因素与分离沙门氏菌、单核细胞增生李斯特菌(LM)和其他目标(例如, spp. 和沙门氏菌肠亚种纽波特)的可能性之间的关联。替代树用于可视化森林分析确定的层次关联。沙门氏菌和 LM 的流行率分别为 5.3%(49/920)和 2.3%(21/920)。在弗吉尼亚州东海岸采集的露点大于 9.4°C 的水样中分离出沙门氏菌的可能性最高。在冬季从土地利用中森林湿地覆盖率小于 36%的地点采集的水样中,分离出 LM 的可能性最高。条件森林的结果与混合模型一致,混合模型还发现,检测到沙门氏菌和 LM 的可能性因样本类型、地区和季节而异。这些发现确定了在农产品生产环境中增加分离出沙门氏菌和 LM 阳性样本的可能性的因素,并支持在区域范围内实施收获前缓解策略。本研究旨在研究弗吉尼亚州的不同种植地区,并确定与病原体流行率相关的因素在不同地区之间可能存在的差异。对时空数据进行建模,以确定各种农场来源中增加病原体可能性的相关因素。研究结果表明,在农产品收获前环境中,沙门氏菌和单核细胞增生李斯特菌的总体流行率较低,但因空间(例如,弗吉尼亚州的地区)和时间(例如,季节)而异,且阳性样本的可能性受不同时空因素的影响。因此,研究结果支持制定控制危害以最大限度降低风险的区域或基于规模的食品安全标准和指导文件。本研究还表明,水源评估是制定监测计划和缓解措施的重要工具,因为时空因素在区域范围内存在差异。