RT Journal Article SR Electronic T1 Using the Agricultural Policy/Environmental eXtender to develop and validate physically based indices for the delineation of critical management areas JF Journal of Soil and Water Conservation FD Soil and Water Conservation Society SP 284 OP 299 DO 10.2489/jswc.67.4.284 VO 67 IS 4 A1 A. Mudgal A1 C. Baffaut A1 S.H. Anderson A1 E.J. Sadler A1 N.R. Kitchen A1 K.A. Sudduth A1 R.N. Lerch YR 2012 UL http://www.jswconline.org/content/67/4/284.abstract AB Targeting critical management areas (CMAs) within cropped fields is essential to maximize production while implementing alternative management practices that will minimize impacts on water quality. The objective of this study was to develop physically based indices to identify CMAs in a 35 ha (88 ac) field characterized by a restrictive clay layer occurring within the upper 15 to 100 cm (6 to 40 in) and under a corn (Zea mays L.)–soybean (Glycine max L.) crop rotation since 1991. Thirty-five subareas were defined based on slope, depth to claypan (CD), and soil mapping units. The Agricultural Policy/Environmental eXtender (APEX) model was calibrated and validated from 1993 to 2002 using measured runoff, sediment, and atrazine loads, and crop yields. CMAs were delineated based on simulated subarea runoff, sediment, and atrazine loads. Correlation analysis was performed between simulated output by subarea and physical parameters, including CD, surface saturated hydraulic conductivity (Ksat), and subarea slope (SL). Two indices were developed, the Conductivity Claypan Index (CCI; CD ×Ksat ÷SL) and the Claypan Index (CPI; CD ÷ SL), to correlate with simulated crop yields, runoff, atrazine, and sediment loads. Together, these indices captured 100% of CMAs for simulated runoff and sediment yield and 60% of CMAs for simulated atrazine in surface runoff, as predicted by APEX. These critical areas also matched lower corn productivity areas. Management scenarios were simulated that differentiated the management of the CMAs from the rest of the field. Indices, such as these, for identifying areas of higher environmental risk and lower productivity could provide objective criteria for effective targeting of best management practices.