Using the Agricultural Policy/Environmental eXtender to develop and validate physically based indices for the delineation of critical management areas

作者：Mudgal A^*; Baffaut C; Anderson S H; Sadler E J; Kitchen N R; Sudduth K A; Lerch R N

来源：Journal of Soil and Water Conservation, 2012, 67(4): 284-299.

DOI：10.2489/jswc.67.4.284

• APEX
• critical areas
• index
• modeling
• targeting

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 xKsat divided by SL) and the Claypan Index (CPI; CD divided by 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.

• 出版日期2012-8