Institute: Maryland
Year Established: 2011 Start Date: 2011-06-01 End Date: 2012-05-30
Total Federal Funds: $22,500 Total Non-Federal Funds: $47,825
Principal Investigators: Alba Torrents, Cathleen Hapeman
Project Summary: The Choptank River, a tributary of the Chesapeake Bay, is surrounded by various agricultural practices and has been under scrutiny for impaired water quality. The majority contributor to the poor water quality of this river is speculated to be these agricultural facilities and farms, particularly the husbandry operations. According to the Environmental Protection Agency’s Guidance for Federal Land Management in the Chesapeake Bay Watershed (2010b), agriculture is responsible for approximately 43% of nitrogen (N), 45% of phosphorus (P), and 60% of the sediment loads released into the Bay. Of this, approximately 17% of N and 19% of P load comes from chemical fertilizers, and 19% of N and 26% of P load comes from manure (U.S. Environmental Protection Agency, 2010b). About 60% of land use in the Choptank River watershed is devoted to agriculture, producing corn, soybean, wheat, and barley; much of this supports small- and medium-sized animal feeding operations, mostly poultry with some dairy and horse husbandry. Manure from poultry houses is routinely used as a fertilizer on agricultural fields. Though mitigation practices have been put in place to control runoff from the agricultural fields and husbandry lots, surface water pollution still occurs. Potential pollutants from these agricultural activities, especially poultry farming, include sediment, pesticides, nutrients, antibiotics, heavy metals, and non-indigenous microorganisms. A survey of a small section of a subwatershed in the Choptank River watershed has been conducted for the past two agricultural growing seasons, raising many questions including some with respect to transport mechanisms, naturally occurring and anthropogenic arsenic sources, and pollutant accumulation in surface waters as they run through both small urban and agricultural lands. Sampling sites have already been selected and characterized for the survey based on simplicity of the stream layout, presence of a poultry facility, and ease of access to the sites. They are located upstream and downstream of the poultry facility, the outlet of the subwatershed, and three branches (two large, one small) of the stream for further exploration. Water and sediment samples will be collected during the active agricultural season under baseflow conditions, though a storm event is also hoped to be captured. Water samples will be tested for arsenic, nitrogen, phosphorus, E. coli and Enterococcus as bacterial indicators of contamination/natural reservoirs, antibiotics, and pesticides. Water quality parameters, such as pH, temperature, and conductivity will also be measured at each site. All of the factors being measured in the water column are indicators of runoff, where arsenic and the antibiotics may be directly correlated to runoff from poultry houses. Sediment samples will be tested for E. coli, Enterococcus, and antibiotics. One more sampling year is required to have sufficient data to answer the questions posed by the surveys previously conducted. The objectives of this environmental survey are to determine if a single poultry operation has a measurable effect on the surrounding environment, particularly water quality, to test for the appearance of agriculturally related chemicals downstream of a poultry operation, assess the potential relative contributions of poultry-related chemicals to a single operation, observe the mitigation practices used to prevent surface water contamination, and to compare findings to other agriculturally related contributions. The survey also addresses the issue of whether suspected contaminants are actually present and whether seasonality and on-the-ground practices affect measured concentrations. Poultry/no poultry effects will also be assessed since some sampling sites are located near a small suburban town. There is also future potential for flow and pollutant distribution modeling.