Evaluating antimicrobial resistance in dairy farming: understanding real world interactions within the wastewater slurry environment

Lanyon, Christopher W. (2021) Evaluating antimicrobial resistance in dairy farming: understanding real world interactions within the wastewater slurry environment. PhD thesis, University of Nottingham.

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Agriculture is a known source of environmental antimicrobial resistance (AMR). However, the ecological and human-health impact of antibiotic use and the related AMR in animal husbandry is poorly understood. One possible vector of AMR from agriculture to the environment is dairy slurry. Slurry stores are repositories of bovine faeces and urine, as well as waste milk, used footbath contents, parlour washings and other farm wastewater. As antibiotics, bacteria and antimicrobial resistance genes are present in bovine excreta and waste milk, and footbaths and farm wastewater may contain copper, zinc, formalin and other biocides, this creates an environment in which selective pressure may cause the proliferation and spread of AMR. Dairy slurry is often spread on arable and crop lands, potentially acting as a source of AMR into the environment, but also into the human and bovine food chains.

Mathematical modelling is a useful predictive tool, but can also be used to understand complex phenomena and identify key processes, especially in circumstances where experimental analysis is costly or time consuming. In this thesis, the prevalence of AMR and antibiotics in dairy slurry is investigated through mathematical modelling.

The prevalence of antibiotic resistant Escherichia coli in dairy slurry stores throughout Great Britain was investigated by the Should We Worry About Slurry (SWWAS) project. SWWAS was devised in 2016 by Christopher Lanyon and Emma Gregson, and was awarded funding by the EPSRC Bridging the Gaps initiative in order to survey farms across Great Britain. The project was an investigation into the presence of extended-spectrum beta-lactamase (ESBL) producing E. coli in slurry stores, alongside antibiotic residue concentrations, heavy metal and mineral concentrations and farm practice data. The goal of the project was to study AMR and farm practices across GB, but also to generate an understanding of data collection practices in agriculture and to inform on how data is synthesised and utilised in mathematical modelling. ESBL E. coli were found on nine of the 15 farms and antimicrobial resistance genes not previously reported in agriculture were also found. Of nineteen antibiotics screened for, seven were detected. Eight metals known to be AMR co-selective agents were found in the farm slurry. Zinc and Copper were found above recorded minimum co-selective concentrations in similar media. Correlations were found between the detection of ESBL E. coli and herd size, arsenic concentration and reported use of neomycin and kanamycin.

Informed by the literature and data from the SWWAS project, three models of antimicrobial resistance in a slurry tank environment were developed. The first model uses ordinary differential equations to describe the persistence and spread of AMR E. coli interacting with bacteriolytic and bacteriostatic antibiotics in a slurry store. The model incorporates bacterial growth and death, horizontal gene transfer, antibiotic degradation, and tank filling and emptying effects. The model simulated various conditions: a sensitivity analysis was performed and inference was performed to predict parameter values for a working dairy farm. Through this analysis it is shown that the tank emptying regime and type of antibiotics used affect the development of multidrug-resistant bacteria.

The second model builds on the first, but additionally incorporates a spatial dimension and settling dynamics. A model of solids settling in a slurry tank was established, then sorption dynamics were used to determine the available concentration of antibiotics throughout the tank. Lastly, the population dynamics of bacteria were simulated to determine the effect of settling on the makeup of the bacterial population. This model illustrates the antibiotic removal effects of sorption and degradation and the resulting effects on the development of AMR. Once again sensitivity analysis was performed to determine how the settling parameters affect the model outputs.

The final model is a partial differential equation (PDE) model of a slurry tank, which incorporates fluid flow dynamics. This model was based on an advection-diffusion-reaction system and simulated in one and two dimensions. Finite difference and finite element frameworks for modelling were established for the one and two dimensional problems, respectively. Similar to the settling model, the PDE model indicates that antibiotic is removed as it moves through the tank and that multidrug resistant bacteria become the dominant phenotype. However, instabilities in the simulations make it difficult to draw conclusions from this model.

The three models developed in this thesis, alongside data from the SWWAS project, highlight particular avenues for future research: the use of bacteriolytic antibiotics, tank filling and emptying regimes, and the effects of sorption on antibiotic removal in dairy slurry. It is key that more data is collected to truly assess the risks that the prevalence and spread of AMR in dairy slurry storage pose to the human and environmental resistomes.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: King, John R.
Gomes, Rachel L.
Stekel, Dov J.
Kypraios, Theodore
Keywords: Antimicrobial resistance, mathematical modelling, agriculture, dairy farming, slurry
Subjects: Q Science > QA Mathematics > QA299 Analysis
S Agriculture > S Agriculture (General)
Faculties/Schools: UK Campuses > Faculty of Science > School of Mathematical Sciences
Item ID: 65567
Depositing User: Lanyon, Christopher
Date Deposited: 04 Aug 2021 04:42
Last Modified: 04 Aug 2021 04:42
URI: http://eprints.nottingham.ac.uk/id/eprint/65567

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