Animalwaste BMP Impacts on Sediment and Nutrient Losses in Runoff From the Owl Run Watershed

Abstract

N onpoint source pollution is transported primarily by runoff from urban, agricultural, mining areas, and construction sites. Agricultural activities are being increasingly blamed for deterioration of surface and ground water resources in the United States. Significant progress has been made in developing technologies for controlling point sources, while until recently nonpoint sources of pollution had been relatively neglected. Runoff carries sediment, organic matter, bacteria, pesticides, metals, nutrients, and other chemicals. Nutrients, primarily nitrogen (N) and phosphorus (P), can be a major problem because they can cause eutrophic algae growth which may reduce oxygen availability and increase turbidity in water bodies. Livestock systems, which utilize pastureland for grazing animals and cropland for disposal of manure waste, are one segment of agricultural production for which the extent of nonpoint source pollution is neither clearly defined nor the effectiveness of best management practices (BMPs) adequately demonstrated. In 1983, a study by the U.S. Environmental Protection Agency on the decline in water quality of the Chesapeake Bay indicated that point and nonpoint sources of pollution were among the main causes of the Bay's decline (USEPA, 1983). In particular, the study indicated that nonpoint sources contributed about 67% of the N and 39% of the P entering the Bay. Furthermore, agriculture was estimated to be responsible for 60 and 27% of the N and P loadings from nonpoint sources, respectively. Consequently, in December 1987, the Governors of Pennsylvania, Maryland, and Virginia, the mayor of the District of Columbia, and the Administrator of the EPA pledged to address nonpoint sources as well as other sources of pollution to restore and protect the Chesapeake Bay. This commitment, known as the Chesapeake Bay Agreement of 1987, requires the signatory States to implement cost-sharing programs targeted at reducing nonpoint source pollution of the Bay and its tributaries. Virginia's agricultural cost-sharing program was initiated in response to the Chesapeake Bay program and was designed to encourage voluntary implementation of BMPs such as conservation tillage and installation of animal waste facilities by farmers. Animal waste management involves both storage and proper utilization of waste as fertilizer on agricultural land in order to improve crop production and reduce transport of pollutants by runoff. Timing, method, and rate of application are controllable management factors that influence both the effectiveness of the animal waste as a fertilizer and the degree to which runoff pollution is prevented (Sharpley et al., 1994). While there is documented evidence on the water quality advantages of BMPs on field-size plots, the effectiveness of BMPs, especially animal waste management practices, on large watersheds with varying topography, land use, soils, and geology is relatively unknown. Thus, a comprehensive nonpoint source monitoring program was undertaken in 1986 to quantify

Measuring conservation program best management practice implementation and maintenance at the watershed scale

“…Official records have been utilized to compare the impacts of different densities of BMP use on measured water quality parameters at the subwatershed level (Yates et al 2007) and to compare water quality conditions at the farm and watershed scale between pre-versus post-BMP imple- mentation time periods (Brannan et al 2000;Currens 2002;Gitau et al 2008). Although standard watershed process models such as SWAT, AnnAGNPS, and GLEAMS have mainly been used to simulate the effects of hypothetical patterns of BMP usage (Arnold et al 1998;Heathman et al 2008; Thomas et al 2007), a growing number of researchers have worked to incorporate information about the types and locations of actual BMP behaviors into these process models at both the field and watershed levels and to compare simulated versus measured water quality outcomes associated with these real-world conservation behaviors (Bracmort et al 2006, Easton et al 2008 Rao et al 2009;.…”

Real-Time Forecast of Hydrologically Sensitive Areas in the Salmon Creek Watershed, New York State, Using an Online Prediction Tool

“…As summarized in Table 4, the wetness class-based (e.g., wetness class 2 equals A f -values in the range of 0.1-0.2) comparison of A f -values predicted based on the 24 h projections with A f,t -values agreed in more than 50% of the days in all classes and in more than 67% of the days when large storm events occurred resulting in runoff contributions from 40% to 60% (wetness classes [4][5][6]. The percentage of days where A f -values predicted based on the 48 h projections with A f,t -values agreed is lower, indicating that the 48 h precipitation forecast of the GFS MOS projections is, in general, quantitatively less reliable.…”

“…The overall effective storage of the watershed was calibrated to baseflow-separated runoff (S = 20 cm). Based on S the maximum effective soil moisture storage, σ e,j , for each wetness class was determined using Equation (5). The baseflow recession coefficients for the summer (May-October) and winter season (November-April) were calibrated from baseflow separated streamflow [17] and equaled α s = 0.06 (day ) by maximizing the Nash-Sutcliffe efficiency over the calibration period using a nonlinear optimization algorithm (e.g., Microsoft Excel Solver).…”

“…During the last 30 years various environmental standards (e.g., NRCS 590 standard, Phosphorus Index) and watershed management practices have been implemented in an attempt to reduce NPS of surface water bodies but have been found in practice to be highly variable in their effectiveness [5][6][7]. This is partly because the effectiveness of watershed management practices is based on simplified watershed-scale models that neither consider the spatial variability of natural landscapes [8][9][10] nor the temporal dynamics of nutrient and pollutant transport processes from the landscape to the stream [11][12][13].…”

A multivariate analysis of covariance to determine the effects of near-stream best management practices on nitrogen and phosphorus concentrations on a dairy farm in the New York Conservation Effects Assessment Project watershed

“…Although the overall concentrations of N and P generally decrease after installation of best management practices (BMPs) (Bishop et al 2005;Brannan et al 2000;Lee et al 2000;Inamdar et al 2001;Gitau et al 2004), it is often unclear which BMPs are most effective, and there is a large range in efficiency of individual BMPs on overall water quality (Gitau et al 2005). For instance, in the NYC source watersheds, milkhouse buffer strips were only effective in reducing P levels for a five-to ten-year period following installation (Kim et al 2006).…”