|Stocker (ctr), Matthew|
|YAKIREVICH, ALEXANDER - Ben Gurion University Of Negev|
|GUBER, ANDREY - Michigan State University|
|MARTINEZ, GONZALO - Universidad De Cordoba|
|BLAUSTEIN, RYANN - University Of Florida|
|WHELAN, GENE - Us Environmental Protection Agency (EPA)|
|Goodrich, David - Dave|
Submitted to: Water, Air, and Soil Pollution
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/18/2018
Publication Date: 5/24/2018
Citation: Stocker, M.D., Yakirevich, A., Guber, A., Martinez, G., Blaustein, R., Whelan, G., Shelton, D.R., Pachepsky, Y.A., Goodrich, D.C. 2018. Functional evaluation of three manure-borne indicator bacteria release models with multiyear field experiment data. Water, Air, and Soil Pollution. 229:181. https://doi.org/10.1007/s11270-018-3807-0.
Interpretive Summary: Transport of manure-borne microorganisms from manured fields and pastures to freshwater sources has to be assessed in order to assess the risks of microbial contamination of irrigation and recreation water sources. The assessment can be done with the existing USDA-ARS overland flow and transport computer model KINEROS2/STWIR. The KINEROS2/STWIR allows for the inclusion of a module to simulate gradual release of manure-borne microbes under rainfall and irrigation. Several modules have been proposed to simulate such release, however their performance at the field scale is currently unknown. We evaluated three of the most popular release modules based on their performance using KINEROS2/STWIR. Experimental data were collected for six consecutive years during simulated rainfall after bovine manure spreading at the USDA-ARS OPE3 field site in Beltsville MD. We found that the Bradford-Schijven and Vadas-Kleinman-Sharpley release models had definite advantages over the exponential release model. Results of this work can be beneficial for environmental microbial safety modeling and risk assessment in that they provide guidance on the selection of release modules for field-scale manure-borne microorganism transport simulations.
Technical 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. Although several release submodels have been proposed, they have only been evaluated and compared to 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 the OPE3 field site at the Beltsville Agricultural Research Center. 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 pre- and post-irrigation, respectively, and manure, soil, and edge-of-field runoff samples were analyzed for E. coli. Model performance was evaluated with the Akaike criterion, R2 and RMSE values. The exported percentage of manure-borne E. coli varied from0.1% to 10% in most cases, generally reflecting the lag time between initiation of irrigation and edge-of-field runoff due to infiltration, interflow, and surface storage. 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. Results from this work show that each submodel was suitable in simulating E. coli removal; however two-parametric submodels were ultimately preferred over the single parameter submodel.