Paper No. 35-8
Presentation Time: 8:00 AM-5:30 PM
ARE RIPARIAN BUFFERS EFFECTIVE IN IMPROVING WATER QUALITY OF FARMING SYSTEMS: A CASE STUDY IN SHENANDOAH VALLEY, VA
The stream ecosystem health of the Shenandoah Valley of Virginia is greatly influenced by the agronomic land use and farming practices in the region. To improve the ecosystem health of the nation’s waters, the Conservation Reserve Enhancement Program by U.S. Department of Agriculture has suggested the landowners to install a riparian buffer-an area between the pollutant source and the water stream which is often vegetated with grasses and some trees. The overall goal of the present study is to determine the impact of riparian buffer systems on the water quality of selected farms in the Shenandoah Valley region. Water and sediment samples were collected from three locations along the buffer area of each of the six streams (upstream, midstream, and downstream). Water samples were analyzed for main cations (Varian Atomic Absorption Spectroscopy) and anions (Dionex High Performance Ion Chromatography). Sediment samples were analyzed for particle size distribution (laser diffraction methods) and organic carbon (loss on ignition method). Additionally, the GIS software (ArcGIS Pro 3.0) was used to quantify environmental/landscape factors to include but not limited to slope, elevation, distance to the farm from the stream. Statistical analysis was performed to develop linear models to determine the impact of riparian buffer characteristics and other farm characteristics on the variability of stream health of those farming systems. The results revealed that some of the highly significant factors on the water quality include length of the buffer, age of the buffer, size of the farm, buffer area as a percentage of the total farm area, farm type (crop, livestock), and fence presence. While larger farms produce larger quantities of pollutants of nitrite, sulfate, calcium, and sodium in stream water, overall, riparian buffers have a positive outcome in improving water quality. This work was supported by an NSF-REU grant (# 1950370) at James Madison University in Summer 2023.