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ARS Home » Northeast Area » University Park, Pennsylvania » Pasture Systems & Watershed Management Research » Research » Publications at this Location » Publication #322162
Title: Water quality ramifications of manure storage and daily haul during winter and early spring

Author
item LIU, JIAN - Pennsylvania State University
item Veith, Tameria - Tamie
item Kleinman, Peter
item BEEGLE, DOUGLAS - Pennsylvania State University

Submitted to: American Society of Agricultural and Biological Engineers
Publication Type: Abstract Only
Publication Acceptance Date: 4/23/2015
Publication Date: N/A
Citation: N/A

Interpretive Summary: An interpretive summary is not required.

Technical Abstract: Manure storage is supported by the United States Natural Resources Conservation Service (NRCS) as a nutrient management strategy for controlling air and water quality. Daily haul is still a popular practice on the small farms in northeastern USA but receives criticism over the impact of spreading during winter on frozen soils or snow cover. However, in some cases daily haul provides more opportunity to adjust manure application based on weather and soil conditions. In particular, in the late winter and early spring, spreading six months’ worth of manure has a higher risk of creating an extreme nutrient loss event during snow melt under high intensity rains. If the farm uses daily haul, then adjustments or short-term storage may be possible during the high-risk periods. This study uses the Soil and Water Assessment Tool (SWAT) to compare the two nutrient management strategies across multiple years of weather data. The case study watershed, Dressler Run (2.6 km2), a sub-watershed of Anderson Creek watershed (202 km2) is located in the Appalachian Plateau in Central Pennsylvania. Monitoring data (climate, hydrologic, water quality, soil properties, land use, and nutrient management) from the Dressler Run watershed will be used to confirm the ability of SWAT to represent the processes controlling P transfers from land to water. If needed, SWAT model routines will be adjusted to represent specific processes. By using SWAT to generate predictions at the field scale and over annual time frames, the risks and benefits of each strategy will be better understood.