Submitted to: Soil and Water Conservation Society
Publication Type: Abstract Only
Publication Acceptance Date: January 19, 2010
Publication Date: July 17, 2010
Citation: Mudgal, A., Baffaut, C., Anderson, S.H., Sadler, E.J., Kitchen, N.R. 2010. Delineation of Critical Areas and Developing Best Management Scenarios for a Field Using Simulation Model APEX [abstract]. Soil and Water Conservation Society, July 18-21, 2010, St. Louis, Missouri. Available: http://www.swcs.org/documents/filelibrary/10ac/2010_Oral_Presentation_Abstracts_566DF5164F928.pdf Technical Abstract: Targeting critical management areas (CMAs) within fields is essential to maximize cultivation area while implementing management practices to minimize impacts on water quality. The objective of this study was to develop a physically-based index to identify CMAs in a 32-ha field. The field was characterized by a claypan, a restrictive clay layer occurring within the upper 30 to 50 cm. The field was under a corn -soybean crop rotation since 1991 with a V-notch weir installed at the outlet for measurement of runoff, sediment and atrazine transport. Thirty-five subareas were defined based on slope, depth to claypan, and soil mapping units. The Agricultural Policy Environmental Extender (APEX) model was calibrated and validated from 1993 to 2002 for runoff, sediment and atrazine loads. Simulated output by subarea was correlated with physical parameters including depth to claypan (CD), surface saturated hydraulic conductivity (Ksat) and subarea slope (SL). Two indices were developed, CD*Ksat /SL, and CD/SL, which correlated with runoff (r = -0.77), and atrazine and sediment loads (r=-0.55), respectively. These indices captured 100 % of CMAs due to runoff and sediment yield and 75 % of CMAs due to atrazine load, as predicted by APEX. These critical areas were also areas with lower productivity. Management scenarios were simulated that differentiated the management of the CMAs from the rest of the field. Indices such as those for identifying and managing areas of higher environmental risk and lower productivity could prove beneficial for effective implementation of best management practices.