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Water Quality Response to Changes in Agricultural Land Use Practices at Headwater Streams in Georgia
Citation:
Brown, R. D., M. MOLINA, A. Oladeinde, T. F. BOHRMANN, C. Fitzgerald, G. Myrthil, AND C. K. WONG. Water Quality Response to Changes in Agricultural Land Use Practices at Headwater Streams in Georgia. Presented at 73rd Annual Meeting of the Association of Southeastern Biologists, Athens, GA, April 04 - 07, 2012.
Impact/Purpose:
see description
Description:
Poorly managed agricultural watersheds may be one of the most important contributors to high levels of bacterial and sediment loadings in surface waters. We investigated two cattle farms with differing management schemes to compare how physicochemical and meteorological parameters influence contaminant loadings in headwater streams. Farm A employs a high-intensity cattle rotation with intermittent direct stream contact. Farm B allows unrestricted access to the stream. Rain event and biweekly baseflow samples were collected along the study reach for each farm. Samples were analyzed for E. coli, Enterococci, Total Suspended Solids (TSS) and Turbidity. Farm B had significantly higher base flow loading rates for enterococci (p=0.003), E. coli (p=0.001) and TSS (p=0.018), as well as significantly higher storm flow loading rates for enterococci (p=0.035) when compared to Farm A. At Farm A, TSS positively correlated with turbidity (R2=0.80), E. coli (R2=0.47) and enterococci (R2=0.46). In Farm B, a positive correlation was only observed between TSS and turbidity (R2=0.69), despite the strong correlation observed between E. coli and enterococci in Farms A (R2=0.58) and B (R2=0.88). Our results suggest that the difference in management practices studied here does not alter the relationship between measures of sediment density, but may have an effect on the relationship between sediment and microbial contaminants. Rain intensity was found to strongly correlate with all contaminants, while a weaker correlation was observed with total amount of rain. Additional multiple regression analysis will be conducted using real-time weather data to construct a model aiming to predict the concentrations of contaminants in stream water.
