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Assessment of manure phosphorus export through turfgrass sod production in Erath County, Texas
Authors:Munster C L  Hanzlik J E  Vietor D M  White R H  McFarland A
Institution:Department of Biological and Agricultural Engineering, Texas A&M University, 127 Hobgood Building, College Station, TX 77843-2474, USA. c-munster@tamu.edu
Abstract:A best management practice (BMP) for exporting manure phosphorus (P) in turfgrass sod from the North Bosque River (NBR) watershed in central Texas was assessed using a geographic information system (GIS). The NBR watershed has a mandate to reduce the total annual P load to the NBR by 50% as a result of total maximum daily load regulation. Since dairy waste applications to fields are identified as the major nonpoint source of P to the river, innovative BMPs, such as export of manure P in turfgrass, will be needed to achieve the 50% reduction. However, methods are needed to evaluate the feasibility of these innovative management practices prior to their implementation. A geospatial database of suitable turfgrass production sites was developed for Erath County using GIS. Erath County largely encompasses the upper portion of the NBR watershed. Information from field experiments, production practices, and ground-truthing was used to search, analyze, and verify a geospatial database developed from national and regional sources. The integration and analyses of large databases supports the search by turf producers for sites suitable for turfgrass sod production in Erath County. In addition, GIS enables researchers and regulators to estimate manure P exports and reduced P loading due to implementation of the manure export BMP on a county scale. Under optimal conditions 198,000 kg manure P yr(-1) could be used and 114,840 kg manure P yr(-1) exported from the NBR watershed through implementation of a system using dairy manure to produce turfgrass sod. This is the equivalent of the manure P applied from 10,032 dairy cows yr(-1) and exported from 5808 dairy cows yr(-1). Application of GIS to large-scale planning and decision-making transcends traditional field-scale applications in precision agriculture.
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