Short communication: turbidity as an indicator of Escherichia coli presence in water troughs on cattle farms

Identification of Contamination Sources and Assessment of Risk Factors Associated with the Occurrence of Escherichia coli O157:H7 on Small-scale Cow-calf Operations in Oklahoma and Louisiana

Escherichia coli O157:H7 is a human pathogen that exists as part of the commensal microflora of cattle and is shed in animal feces. Little is known about the effect of management practices on its occurrence and transmission on small-scale cow-calf operations. Identification of risk factors associated with farm practices could help implement effective measures to control E. coli O157:H7. This study quantified the risk of E. coli O157:H7 occurrence associated with cow-calf farm practices using risk modeling. Management practices of small-scale cow-calf operations in OK and LA were assessed through survey-based research. Fecal, water, sediments and water-trough-swab samples were collected to determine the incidence of E. coli O157:H7, and potential on-farm contamination sources and risk factors identified. Association between the occurrence of pathogen and farm practices was determined using two risk assessment models (I and II). Model I determined the association of E. coli O157:H7 occurrence with water source, water container, feed, cattle breed, and herd density, while Model II determined its association with farm cleanliness. For both models, logistic regression was followed using a two-step approach, univariable and multivariable analysis. In OK and LA, E. coli O157:H7 was present in 5.8% and 8.8% fecal, 4.4% and 9.4% water, 10.3% and 9.6% sediments, and 1.5% and 10.6% water-trough-swab samples, respectively. In Model I, univariable analysis identified water container and feed, whereas multivariable analysis identified feed as a significant risk factor. In Model II, the univariable analysis found cleanliness of cattle-contact areas, such as, alleyways, water-trough, chute and equipment, to be a significant risk factor. In multivariable analysis, only the cleanliness of water-trough was identified to be a significant risk factor. Results from the study could aid in the development of on-farm best management practices for the reduction of E. coli O157:H7.

Establishing the lower bacterial concentration threshold for different optical counting techniques

This work compares several physical and optical techniques used in fundamental research and industrial applications to detect bacteria in water. Optical techniques such as, UV-absorbance spectroscopy, laser particle counting, turbidimetry and Z-Sizer light scattering, and a direct observational physical technique, the plate count method, were compared when measuring the concentration of E.coli in tenfold dilution from a stock solution. Estimates of the detection threshold limit of E.coli for the different optical counting techniques and the relationship between colony-forming units (CFU) and tenfold dilutions was established. Optical methods have generated interest due to the rapid response of just minutes, non-destructive approach and minimal sample preparation but their use is still limited to concentrations of up to 4 Log E.coli/mL. In contrast, the plate count method is still a reliable technique for water quality analysis despite its long response time of 24 h.

A risk-oriented evaluation of biofilm and other influencing factors on biological quality of drinking water for dairy cows

Despite the importance of livestock drinking water quality on animal physiology, welfare, and performance, influences such as biofilm formation on trough surfaces on microbial water quality are rarely researched. The objective of this study was to assess the microbial quality of water offered to lactating dairy cows and identify risk factors for poor water quality. We further aimed to determine the impact of biofilm formation on water quality and evaluate rapid test systems to score the hygiene status of dairy troughs on the farm. A total of 105 troughs located on 24 typical Western German dairy farms were sampled. Samples of livestock drinking water and biofilm were analyzed for aerobic total viable count (TVC), coliform count (CC), Escherichia coli, methicillin-resistant Staphylococcus aureus (MRSA), and other bacteria resistant to 3rd generation cephalosporins (CRB). Surface protein- and adenosine triphosphate (ATP)-rapid tests were evaluated to detect biofilm formation. The influence of 22 selected fixed and variable trough characteristics on impaired livestock drinking water quality was evaluated by calculating odds ratios. The average TVC, CC, and E. coli counts were 4.4 ± 0.06 (mean ± SD), 1.7 ± 0.1, and 0.6 ± 0.1 log10 cfu per mL, respectively. CC was detectable in 94.3% of all water samples and E. coli in 48.6%. MRSA was found in pooled livestock drinking water samples of a single farm and CRB on three farms, suggesting that troughs might function as a reservoir of antibiotic-resistant bacteria, thereby contributing to an exchange of antibiotic-resistant bacteria between animals. Risk factors for the impairment of at least one microbial quality criteria (TVC, CC, or E. coli) increased significantly (P < 0.05) when using high-volume troughs, other trough materials than stainless steel, a lower distance to the milking parlor, heavy visible soiling, biofilm formation, and high ambient and high water temperatures. CC (r = 0.46; P < 0.001) and E. coli (r = 0.31; P < 0.01) of water samples correlated with their equivalent in biofilm and with the results of rapid tests on trough surfaces (0.31 > r > 0.19; P < 0.05). Addressing the identified risk factors could be an approach to ensure sufficient biological quality of livestock drinking water.

Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to Predict E. coli Levels in Agricultural Water

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.

Irrigation water quality and its impact on the physicochemical and microbiological contamination of vegetables produced from market gardening: a case of the Vea Irrigation Dam, U.E.R., Ghana

The rationale for this study was to assess the physicochemical and bacteriological qualities of the Vea irrigation water and resultant effects on the quality of fresh vegetables produced in the area and associated implications for consumers' health. A total of 45 water samples were collected from the reservoir and canals. Also, 16 vegetable samples comprising four samples each of tomatoes, carrots, spring onions, and cabbages were collected from four farms with installed irrigation systems fed by the Vea Dam. The irrigation water samples were analyzed for total coliform (TC) and fecal coliform (FC), Escherichia coli, pH, and turbidity, while the samples of vegetables were analyzed for TC and FC, and E. coli. The results showed that except for pH, the bacterial loads and turbidity of the sampled vegetables and irrigation water were above the standards of the WHO and the International Commission on Microbiological Specifications for Food. Comparatively, the samples of cabbage recorded the highest levels of microbial contamination. The study suggests that the water should be treated before being used for irrigation; consumers should ensure that vegetables are properly washed and cooked/treated before consumption; and periodic monitoring and assessment should be done to ensure that the adverse effects of these activities are forestalled.