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

Abstract

Abstract: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. Key words: APEX-critical areas-index-modeling-targetingThere have been many efforts and improvements to minimize the impact of nonpoint source (NPS) pollutants on various water bodies, including implementation of different management practices. While working at small scales (e.g., plots or fields), these practices demonstrate positive results in relation to the reduction of NPS pollution. However, at larger scales (e.g., watersheds), many studies have shown very little water quality improvement following implementation of best management practices (BMPs) (Park et al. 1994;Inamdar et al. 2002;Simpson and Weammert 2008;Meals et al. 2010). Many factors could diminish the impact of BMPs at the watershed outlet, including not targeting critical areas, hydrologic lag time (i.e., the time between implementation of BMPs and detectable changes in water quality), insufficient number of BMPs, insufficient area protected by these BMPs, maintenance of BMPs, type of BMPs, and similar factors (Meals et al. 2010).Critical management areas (CMAs) can be defined as areas that require special attention because of high potential for surface runoff and NPS pollutants, possibly also leading to lower crop yields. Delineation of CMAs would minimize one factor of uncertainty for better placement of BMPs. Once ...

Improved APEX Model Simulation of Buffer Water Quality Benefits at Field Scale

“…Water, sediment, and nutrients are routed through subareas in transit to the edge-of-field. Because of its capabilities in simulating diverse conservation practices (Gassman et al, 2010;Tuppad et al, 2010;Mudgal et al, 2012), including buffers and filter strips , APEX was used for conservation assessments by the national Conservation Effects Assessment Project (CEAP) of the USDA Natural Resources Conservation Service (https://www.nrcs.usda.gov/ wps/portal/nrcs/main/national/technical/nra/ceap/; Santhi et al, 2014).…”

Long-Term Agroecosystem Research in the Central Mississippi River Basin: Goodwater Creek Experimental Watershed and Regional Nutrient Water Quality Data

“…The sideslopes have shallow depths to the claypan and overall poor crop growth conditions as a result of the long‐term erosion patterns on the field (Lerch et al, 2005), leading to economic losses, especially for corn (Kitchen et al, 2005b). These same areas also disproportionately contribute to sediment and herbicide loss within the field (Mudgal et al, 2012). These findings represented the convergence of two research paths—one studying the factors affecting crop growth and profitability across landscapes (Kitchen et al, 1999, 2005a, 2005b) and the other studying soil and landscape factors controlling the hydrologic transport of contaminants (Ghidey et al, 1997; Ghidey and Alberts, 1999; Lerch et al, 2005; Mudgal et al, 2012).…”

Long-Term Agroecosystem Research in the Central Mississippi River Basin: Goodwater Creek Experimental Watershed and Regional Herbicide Water Quality Data

“…Thus, the claypan soil watersheds of MLRA 113 and similar watersheds within MLRA 109 have exceptionally high herbicide concentrations and relative loads compared with other areas of the Corn Belt (Lerch and Blanchard, 2003;USEPA, 2007;Lerch et al, 2011b). Management challenges for restrictive layer soils include development and validation of metrics (e.g., depth to restrictive layer, clay content, saturated hydraulic conductivity, slope) for identification of vulnerable areas within watersheds (Mugdal et al, 2012) that can provide the basis for targeting conservation practices and development of practices that can simultaneously manage for erosion control and reductions in soil-applied herbicide transport.…”