Performing Department
Ag & Biosystems Engineering
Non Technical Summary
Our project will inform the effectiveness of mitigation strategies in reducing risk to human health in agro-ecosystems by developing an adaptable framework, CAMRADES, Connecting Antimicrobial Resistance, Agricultural Decisions, And Environmental Systems. We will integrate predictive models of AMR transport and associated risk to human health, and improve stakeholder understanding of AMR, potential risks and mitigation strategies.Objective 1. Characterize and predict transport of AMR targets via surface and subsurface pathways in agro-ecosystems.Objective 2. Integrate results into a novel adaptable framework (CAMRADES) to assess risk associated with transport of AMR through agricultural environments to humans as a result of agricultural practices.Objective 3. Improve knowledge of AMR-related risks and inspire adoption of practices among food producers to combat AMR-related health and food safety risks associated with agro-ecosystems.Our framework integrates detection of antibiotics, AMR genes and indicator organisms and exposure pathways in the environment with hydrologic and water quality monitoring. A key output of this effort will be a predictive model of antibiotic and AMR sources, fate, and transport, which will be integrated with a novel risk assessment. Finally, to maximize realized AMR risk reduction, combinations of mitigation strategies will be informed by potential efficacy scenarios encompassing barn, manure, and land management strategies and disseminated using the recognized and trusted iAMResponsibleTM Project platform as well as Livestock Extension Programs. Our project will cumulate with an AMR Summit and AMR reduction strategy, a science and technology-based framework to reduce AMR in shared water resources. We will work closely with stakeholders to provide optimal flexibility to meet producer needs.
Animal Health Component
0%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Goals / Objectives
Our goal is to assess the effectiveness of various mitigation strategies for reducing risk to human health in agro-ecosystems by developing an adaptable framework, Connecting AntiMicrobial Resistance Agricultural Decisions and Environmental Systems, CAMRADES. We will integrate predictive models of AMR transport and associated risk to human health, and improve stakeholder understanding of AMR, potential risks and mitigation strategies, along with motivating adoption of research-based practices to protect human health. Supporting ObjectivesCharacterize and predict transport of AMR targets via surface and subsurface pathways in agro-ecosystems.Integrate results into a novel adaptable framework (CAMRADES) to assess risk associated with transport of AMR through agricultural environments to humans as a result of agricultural practices.Improve knowledge of AMR-related risks and inspire adoption of practices among food producers to combat AMR-related health and food safety risks associated with agro-ecosystems.
Project Methods
3.0 Approach The foundation of the proposed work is the development of CAMRADES, which is built upon the synthesis of both gene, antibiotic, and bacterial datasets from monitoring efforts, the integration of AMR datasets into SWAT, and connecting AMR datasets to human health risks. In its implementation, CAMRADES will allow for the integration of both feasibility and mitigation efficacy and the evaluation of the impacts of different farm management decisions on the introduction and mitigation of AMR distribution. Finally, we propose to develop outreach tools to engage diverse producers, policy makers, and educators.Research activities in Obj. 1 will address our research question: How do agro-ecosystem practices influence human AMR risk via environmental pathways? To expand, the subsurface pathway reflects the potential of AMR amplification in the soil and movement into tile or groundwater while the surface pathway is runoff of AMR, which indicates the influence of certain land management decisions (such as manure application). Distinguishing between these pathways is important to understand the effectiveness of interventions, especially those implemented on the landscape. To accomplish this effort, we will build upon existing watershed monitoring and perform additional assessment of two watershed for a systematic study of AMR transport in agroecosystems.Watershed Monitoring Datasets. First, we will compile and expand on extensive monitoring of surface and drainage from two agriculturally dominant watersheds across different land regions in IA and NE for which historical antibiotic and AMR datasets are already available. 1) The Shell Creek watershed drains approximately 120,000 km2 in east-central NE and dominated by agricultural landuse: 46% of the land is in irrigated row crops and the watershed also contains over 1 million head of cattle, swine, and poultry 16 and 2) The BHL watershed is located along the western edge of the Des Moines Lobe landform of Central Iowa, with a drainage area of 5,324 ha and more than 80% of the watershed used for agricultural production including cattle and swine 13, 49, 50. BHL has a diverse range of management practice implementation. In both cases we are building on long-term monitoring projects which have included antibiotic and DNA collection and analysis, providing robust datasets for modification of the predictive model.Here, we propose to expand this effort to include ARG surveillance of a broad diversity of genes previously observed to be associated with manure inputs, allowing for lower limits of detection of AMR. Further, because we have preserved DNA from past monitoring efforts, we will re-run samples using the expanded high throughput qPCR methods described below. This effort parallels a long history of research investigating different management practices in agroecosystems to beneficially manage crop production and human health, with much of the focus being on nutrients, pesticides, and fecal indicator bacteria. Assessment of similar strategies on AMR mitigation is a rapidly developing field and sufficient data now exists at multiple intervention points to assess impacts and risk to human health.Data analysis: Statistical comparisons of resistance gene abundance and target gene analysis will be performed. Additionally, co-occurrence analysis to identify networks of genes that share similar patterns of abundance changes in different conditions (e.g., over time or with and without ARG history) will be used to determine networks of genes that may be correlated 71. Similar methods will be used to assess relationships between ARGs, indicator organisms, and antibiotic concentrations. All data will be tested for normality and transformed if necessary to meet assumptions of normality and equality of variances. Non-detects will be taken as ½ of the limit of detection for antibiotic data and qPCR assays. Correlation analysis will be used to evaluate relationships between AMR and environmental datasets.SWAT integration to predict AMR fate and transport (SWAT-AMR). The SWAT model includes a framework for predicting fecal indicator bacteria transport in agroecosystems, but it has not been developed to simulate AMR. Here we propose to build a new module in SWAT using publicly available management datasets combined with the proposed data collection on ARGs and ARBs in the BHL and Shell Creek watersheds. We will modify the existing SWAT FIB component to predict AMR using ARG and ARB water quality data. The outcome is SWAT-AMR, a watershed-scale model to predict movement of ARG and ARB through environmental pathways in agroecosystems.Risk Assessment Integration Our first step in Obj. 2 is to connect our biophysical model (SWAT-AMR) output (e.g. ARB, ARG) to a human risk assessment. In line with the proposal scope, the following aspects that may affect the risks attributable to antibiotic resistant genes derived from various environmental compartments will be evaluated, including (1) the likelihood of a gene being transferred to human bacteria, (2) the probability of disseminating the gene through the environmental exposure pathways, and (3) severity of the public health problem. The first two aspects determine the probability of exposure, while the last one relates to the severity of consequence. In turn, a number of risk descriptors effecting the above-mentioned aspects will be investigated.Scenario-based AMR risk/ reduction metrics. Next, we will integrate expected AMR reduction (as generated by review of existing practices and informed by our ongoing work) to develop realistic estimates of AMR reduction/transport under current and potential management scenarios. The estimated AMR reduction for a range of practices will be characterized by realm, Barn Management, Manure management, and Land Management. From this we will estimate the ratio of the expected percent AMR Reduction (%R) which will be generated for hundreds of potential scenarios using our CAMRADES model, converging environmental datasets with risk assessment to inform expected impact of management decisions on human health risk.Improve knowledge of AMR-related risks among food producers.In year 5, we will convene an AMR Summit that will bring key stakeholders together to evaluate project results and initiate the development of an AMR action plan. Summit participants will include the leadership of the major stakeholders from the agribusiness community, including producer groups (e.g., Pork Producers, Cattlemen, Farm Bureau), veterinary service providers, manure management technical service providers, and livestock integrators (e.g., JBS, Smithfield Foods). The overall goal of the AMR Summit will be to gain consensus on future actions to address AMR. We will discuss the potential development and implementation of an "AMR Reduction Strategy". These efforts will cumulate in a strategy document showing alternative means of achieving a set reduction in the quantity of AMR transported to waters (e.g., 50%).?