Functional Evaluation of Three Manure-Borne Indicator Bacteria Release Models with Multiyear Field Experiment Data.

Abstract

Modeling the fate and transport of Escherichia coli is of substantial interest because of how this organism serves as an indicator of fecal contamination in microbial water quality assessment. The efficacy of models used to assess the export of E. coli from agricultural fields is dependent, in part, on submodels they utilize to simulate E. coli release from land-applied manure and animal waste. Although several release submodels have been proposed, they have only been evaluated and compared with data from laboratory or small plot E. coli release experiments. Our objective was to evaluate and compare performances of three manure-borne bacteria release submodels at field-scale: exponential release (EM), two-parametric Bradford and Schijven (B-S), and two-parametric Vadas-Kleinman-Sharpley (VKS); each was independently incorporated and tested as a submodel within the export model KINEROS2/STWIR, using E. coli. Dairy manure was uniformly applied via surface broadcasting once a year for six consecutive years on a 0.28 ha experimental field site. Two irrigation events followed each application: the first immediately followed the initial application and the second occurred one week later. Manure and soil samples were collected before and after irrigation, respectively, and manure, soil, and edge-of-field runoff samples were analyzed for E. coli. Model performance was evaluated with the Akaike criterion, coefficients of determination (R2), and root mean squared errors (RMSE) values. The percentage of exported manure-borne E. coli varied from 0.1% to 10% in most cases, generally reflecting the lag time between initiation of irrigation and initiation ofedge-of-field runoff. The export model performed better when using the VKS submodel which was preferred in 55% of cases. The B-S and EM submodels were preferred in 27% and 18% of cases, respectively. Two-parametric submodels were ultimately preferred over the single parameter submodel.