The Scientific World Journal

The Scientific World Journal / 2001 / Article
Special Issue

Optimizing Nitrogen Management in Food and Energy Production and Environmental Protection: 2nd International Nitrogen Conference 2001

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Research Article | Open Access

Volume 1 |Article ID 304197 |

C.L. Walthall, T.J. Gish, C.S.T. Daughtry, W.P. Dulaney, K.-J.S. Kung, G. McCarty, D. Timlin, J. T. Angier, P. Buss, P. R. Houser, "An Innovative Approach for Locating and Evaluating Subsurface Pathways for Nitrogen Loss", The Scientific World Journal, vol. 1, Article ID 304197, 7 pages, 2001.

An Innovative Approach for Locating and Evaluating Subsurface Pathways for Nitrogen Loss

Academic Editor: Joe Wisniewski


Fundamental watershed-scale processes governing chemical flux to neighboring ecosystems are so poorly understood that effective strategies for mitigating chemical contamination cannot be formulated. Characterization of evapotranspiration, surface runoff, plant uptake, subsurface preferential flow, behavior of the chemicals in neighboring ecosystems, and an understanding of how crop management practices influence these processes are needed. Adequate characterization of subsurface flow has been especially difficult because conventional sampling methods are ineffective for measuring preferential flow of water and solutes. A sampling strategy based on ground-penetrating radar (GPR) mapping of subsurface structures coupled with near real-time soil moisture data, surface topography, remotely sensed imagery, and a geographic information system (GIS) appears to offer a means of accurately identifying subsurface preferential flow pathways. Four small adjacent watersheds draining into a riparian wetland and first-order stream at the USDA-ARS Beltsville Agricultural Research Center, Beltsville, MD are being studied with this protocol. The spatial location of some preferential flow pathways for chemicals exiting these agricultural watersheds to the neighboring ecosystems have been identified. Confirmation of the pathways is via examination of patterns in yield monitor data and remote sensing imagery.

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