SEARCHCOHORTinfectious_disease, food_safety

cohort·infectious_disease, food_safety, environmental_health, microbiology, agricultural_medicine, epidemiology·PMC10117993

A longitudinal study to examine the influence of farming practices and environmental factors on pathogen prevalence using structural equation modeling

Frontiers in Microbiology · 17 authors, 4 centres

AI SUMMARY

FIDELITY 88%

POPULATIONSoil, water, manure, and compost samples from farms in Ohio (18 farms) and Georgia (6 farms) over the 2018–2019 growing seasons

INTERVENTIONDifferent farming practices including use of biological soil amendments of animal origin (dairy manure, poultry manure, green compost) and irrigation water sources (surface water, well water, non-irrigated)

COMPARISONPathogen prevalence compared across amendment types, irrigation sources, seasons, and geographic regions; structural equation modeling compared direct and indirect effects of weather, soil fertility, and microbiome diversity

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AGENT 01 · STRUCTURED SUMMARY

TRACE TO SOURCE

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TL;DR

This longitudinal study across farms in Ohio and Georgia found that *Listeria monocytogenes* and *Arcobacter* were the most prevalent pathogens in soil, manure, and water, while *Salmonella*, STEC, and *Campylobacter* were rarely detected (<10%). Structural equation modeling revealed that weather (especially air temperature), use of biological soil amendments of animal origin (particularly dairy manure), and soil microbiome diversity significantly influenced pathogen prevalence, with temperature having opposite effects on *Lm* (decreased) and *Arcobacter* (increased). These findings indicate that pathogen-specific mitigation strategies are needed for produce safety, as a one-size-fits-all approach is unlikely to control all foodborne pathogens in agricultural environments.

Full summary

5,154 CHARS

**Background:** Foodborne illness linked to fresh produce remains a major health concern. Key bacterial pathogens—*Salmonella*, STEC, *Listeria monocytogenes* (*Lm*), and *Campylobacter*—are associated with outbreaks from leafy greens, tomatoes, and other commodities. Farm practices such as manure application, irrigation water source, and environmental factors may influence pathogen prevalence in the pre-harvest environment. This study aimed to evaluate these factors using a multi-pathogen, multi-region longitudinal design.

**Methods:** Farms were recruited in Ohio (OH, n=18) and Georgia (GA, n=6) during the 2018–2019 growing seasons. OH farms used dairy manure (DM), poultry manure (PM), or no biological soil amendments of animal origin (BSAAO); GA farms were certified organic using PM or green compost. Soil, water (source and dripline), manure, and compost samples were collected monthly and tested for *Lm*, *Arcobacter*, *Salmonella*, *Campylobacter*, and STEC using culture enrichment and PCR confirmation. Soil chemical characteristics (pH, organic matter, nutrients via Mehlich 3 extraction), 16S rRNA gene sequencing (V4 region, Illumina MiSeq), and daily meteorological data (air temperature, precipitation, solar radiation) were collected. Structural equation modeling (SEM) was used to analyze direct and indirect effects on *Lm* and *Arcobacter* prevalence in OH soil (n=154 complete cases for *Lm*, n=144 for *Arcobacter*). A parsimonious SEM was conducted for GA *Lm* soil data (n=99 complete cases). Fisher's exact test assessed associations between pathogen prevalence and amendment type, water source, and season.

**Key Results:** *Salmonella*, STEC, and *Campylobacter* were detected at low prevalence in soil and water (<10%), precluding statistical modeling. *Lm* and *Arcobacter* were found in soil (13% and 7% overall, respectively), manure (49% and 32%, respectively), and water (18% and 39%, respectively). In OH, *Lm* prevalence in soil was 15% (45/311) and *Arcobacter* 8% (21/260). In GA, *Lm* prevalence in soil was 9% (10/106) and *Arcobacter* 2% (1/67). DM had the highest pathogen prevalence: *Lm* 60% (34/57), *Arcobacter* 39% (22/57), *Campylobacter* 38% (21/57), STEC 18% (9/57). No pathogens were detected in green compost. Surface water had significantly higher pathogen prevalence than well water: *Lm* 26% vs. 0% (p<0.0001), *Arcobacter* 52% vs. 8% (p<0.0001). SEM results for OH soil showed: (1) Soil amendment directly increased *Lm* prevalence (p=0.01) and *Arcobacter* prevalence (p=0.08), with DM > PM > no BSAAO. (2) Weather directly affected *Arcobacter* prevalence (p=0.04, increasing with temperature) and indirectly affected *Lm* prevalence through soil fertility (p=0.05). (3) Increasing air temperature decreased *Lm* prevalence (effect size ~6) and increased *Arcobacter* prevalence. (4) Irrigation source had a direct effect on *Lm* prevalence (p=0.06), with non-irrigated fields having the highest *Lm* prevalence (19%), despite high *Lm* prevalence in surface water. (5) Soil fertility decreased with increasing temperature and was highest in DM-amended fields. (6) Increasing soil fertility decreased Simpson's Diversity Index (SDI) (p=0.005), and decreasing SDI was correlated with higher *Lm* prevalence (p=0.02), indicating an indirect positive effect of soil fertility on *Lm*. (7) *Flavobacterium* was significantly more abundant in *Lm*+ samples (mean relative abundance 2.66% vs. 1.33%), while *Gemmata* was more abundant in *Lm*- samples (1.23% vs. 0.96%). For GA soil, weather directly impacted *Lm* prevalence (p=0.04), with PM-amended fields showing higher *Lm* prevalence (14%) than green compost-amended fields (8.5%). A structural break in OH *Lm* prevalence was detected in March (p=0.05). Seasonal analysis showed *Lm* prevalence in OH surface water peaked in summer (p=0.02), while *Lm* in OH soil was highest in winter/spring. *Arcobacter* in OH soil was highest in summer/fall.

**Clinical Implications:** This study demonstrates that pathogen prevalence in agricultural environments is influenced by complex interactions between farm management practices, weather, and soil ecology. The finding that *Lm* and *Arcobacter* respond differently to environmental factors—particularly temperature—indicates that mitigation strategies must be pathogen-specific. The association between BSAAO use and increased pathogen prevalence supports current food safety guidance on manure management. The unexpected finding that non-irrigated fields had higher *Lm* prevalence than irrigated fields suggests that unmeasured confounders (e.g., animal proximity, wildlife intrusion) may play important roles. The inverse relationship between soil microbiome diversity and *Lm* prevalence supports the hypothesis that a healthy soil microbiome may suppress certain pathogens, though this benefit may be offset by the increased pathogen introduction risk from BSAAO use. These results highlight the need for integrated, multi-factorial risk assessment models that consider pathogen ecology, regional climate, and farm-specific practices to develop effective produce safety interventions.

PICO

PPOPULATION

Soil, water, manure, and compost samples from farms in Ohio (18 farms) and Georgia (6 farms) over the 2018–2019 growing seasons

IINTERVENTION

Different farming practices including use of biological soil amendments of animal origin (dairy manure, poultry manure, green compost) and irrigation water sources (surface water, well water, non-irrigated)

OOUTCOME

Prevalence of *Listeria monocytogenes*, *Arcobacter*, *Salmonella*, *Campylobacter*, and Shiga-toxin-producing *Escherichia coli* (STEC) in soil, water, and amendment samples

STUDY TYPE

cohort

SPECIALTY

infectious_disease

SUMMARISED BY

AI pipeline

FIDELITY CHECK

88% · B

ORIGINAL PAPER

PMC10117993

A longitudinal study to examine the influence of farming practices and environmental factors on pathogen prevalence using structural equation modeling

Frontiers in Microbiology

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ABSTRACT

The contamination of fresh produce with foodborne pathogens has been an on-going concern with outbreaks linked to these commodities. Evaluation of farm practices, such as use of manure, irrigation water source, and other factors that could influence pathogen prevalence in the farming environment could lead to improved mitigation strategies to reduce the potential for contamination events. Soil, water, manure, and compost were sampled from farms in Ohio and Georgia to identify the prevalence of Salmonella , Listeria monocytogenes ( Lm ), Campylobacter , and Shiga-toxin-producing Escherichia coli (STEC), as well as Arcobacter , an emerging human pathogen. This study investigated agricultural practices to determine which influenced pathogen prevalence, i.e., the percent positive samples. These efforts identified a low prevalence of Salmonella , STEC, and Campylobacter in soil and water (< 10%), preventing statistical modeling of these pathogens. However, Lm and Arcobacter were found in soil (13 and 7%, respectively), manure (49 and 32%, respectively), and water samples (18 and 39%, respectively) at a comparatively higher prevalence, suggesting different dynamics are involved in their survival in the farm environment. Lm and Arcobacter prevalence data, soil chemical characteristics, as well as farm practices and weather, were analyzed using structural equation modeling to identify which factors play a role, directly or indirectly, on the prevalence of these pathogens. These analyses identified an association between pathogen prevalence and weather, as well as biological soil amendments of animal origin. Increasing air temperature increased Arcobacter and decreased Lm . Lm prevalence was found to be inversely correlated with the use of surface water for irrigation, despite a high Lm prevalence in surface water suggesting other factors may play a role. Furthermore, Lm prevalence increased when the microbiome’s Simpson’s Diversity Index decreased, which occurred as soil fertility increased, leading to an indirect positive effect for soil fertility on Lm prevalence. These results suggest that pathogen, environment, and farm management practices, in addition to produce commodities, all need to be considered when developing mitigation strategies. The prevalence of Arcobacter and Lm versus the other pathogens suggests that multiple mitigation strategies may need to be employed to control these pathogens.

CITATION EXPORT

Martine Ferguson, Chiun-Kang Hsu, Christopher Grim et al.. A longitudinal study to examine the influence of farming practices and environmental factors on pathogen prevalence using structural equation modeling. Frontiers in Microbiology. (2023). https://doi.org/10.3389/fmicb.2023.1141043

AUTHORS

17 · 4 CENTRES

Martine Ferguson, Chiun-Kang Hsu, Christopher Grim, Michael Kauffman, Karen Jarvis, James B. Pettengill, Uma S. Babu, Lisa M. Harrison, Baoguang Li, Alice Hayford, Kannan V. Balan, Josefina P. Freeman, Gireesh Rajashekara, Erin K. Lipp, Ralph Scott Rozier, Anne Marie Zimeri, Laurel S. Burall

1Office of Analytics and Outreach, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, MD, United States
2Office of Applied Safety and Research Assessment, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, Laurel, MD, United States
3Center for Food Animal Health, The Ohio State University, Wooster, OH, United States
4Department of Environmental Health Science, University of Georgia, Athens, GA, United States