EPIC Tile Flow and Nitrate Loss Predictions for Three Minnesota Cropping Systems

Abstract

Subsurface tile drains are a key source of nitrate N (NO3-N) losses to streams in parts of the north central USA. In this study, the Erosion Productivity Impact Calculator (EPIC) model was evaluated by comparing measured vs. predicted tile flow, tile NO3-N loss, soil profile residual NO3-N, crop N uptake, and yield, using 4 yr of data collected at a site near Lamberton, MN, for three crop rotations: continuous corn (Zea mays L.) or CC, corn-soybean [Glycine max (L.) Merr.] or CS, and continuous alfalfa (Medicago sativa L.) or CA. Initially, EPIC was run using standard Soil Conservation Service (SCS) runoff curve numbers (CN2) for CC and CS; monthly variations were accurately tracked for tile flow (r2 = 0.86 and 0.90) and NO3-N loss (r2 = 0.69 and 0.52). However, average annual CC and CS tile flows were underpredicted by -32 and -34%, and corresponding annual NO3-N losses were underpredicted by -11 and -52%. Predicted average annual tile flows and NO3-N losses generally improved following calibration of the CN2; tile flow underpredictions were -9 and - 12%, whereas NO3-N losses were 0.6 and -54%. Adjusting a N parameter further improved predicted CS NO3-N losses. Predicted monthly tile flows and NO3-N losses for the CA simulation compared poorly with observed values (r2 values of 0.27 and 0.19); the annual drainage volumes and N losses were of similar magnitude to those measured. Overall, EPIC replicated the relative impacts of the three cropping systems on N fate.

Development of a Canadian Agricultural Nitrogen Budget (CANB v2.0) model and the evaluation of various policy scenarios

“…For this reason, computer simulation models are often used to predict the impacts of changes in soil and crop management practices on the fate of nitrogen in the environment. For example, nitrogen models that have been used in agricultural sustainability studies include siteand field-oriented research models (Probert et al 1998;Hartkamp et al 1999), management-and/or policy-oriented models (Svendsen et al 1995;Delgado et al 2002), and regional models (Chung et al 2001). Policy scenarios and/or legislative measures in Europe have been evaluated by simulating nitrogen in agro-ecosystems (Kersebaum 1995;Børgesen et al 2001).…”

Historical Development and Applications of the EPIC and APEX models

“…Roloff, de Jong, and Nolin (1998a) found that EPIC adequately simulated mean and annual soybean yields for two sites located at Barrhaven, Ontario, and St. Antoine, Quebec, but corn yields estimated by EPIC were less accurate. Mean and annual corn, soybean, and alfalfa yields estimated by EPIC generally reflected corresponding yields measured within continuous corn, corn-soybean, and continuous alfalfa rotations during the 1990-93 period near Lamberton, Minnesota, although errors of 25%-50% occurred for 4 of the 12 annual predicted yields (Chung et al 2001). Perez-Quezada et al (2003) found that EPIC replicated yield variability measured for wheat, tomatoes, beans, and sunflowers grown in a commercial field in the Sacramento Valley, California, but the model was weaker at reproducing yields measured at specific points in the field.…”

“…However, less accurate soil N and crop N uptake results were reported in EPIC validation studies by Chung et al (2001), Warner (1997a), and Warner et al (1997b). Generally accurate predictions of leached N below the root zone or in tile flow, as compared with or implied by measured data, were found by Engelke and Fabrewitz (1991), Jackson et al (1994), Richter and Benbi (1996), Cavero et al (1997), Flowers, Easterling, and Hauck (1998), Cavero et al (1999), and Chung et al (2002).…”

“…Example applications include assessment of sediment and nutrient losses as a function of different tillage systems, crop rotations, and fertilizer rates King, Richardson, and Williams 1996); nutrient losses from livestock manure applications (Edwards et al 1994;Pierson et al 2001); nitrate-nitrogen (NO 3 -N) losses through subsurface tile drainage (Chung et al 2001;Chung et al 2002); nutrient cycling as a function of cropping system (Cavero et al 1999;Bernardos et al 2001); soil loss due to wind erosion (Potter et al 1998;Bernardos et al 2001); climate change impacts on crop yield and/or soil erosion (Favis-Mortlock et al 1991;Brown and Rosenberg 1999); losses from field applications of pesticides (Williams, Richardson, and Griggs 1992;Sabbagh et al 1992); irrigation impacts on crop yields (Cabelguenne, Jones, and Williams 1995;Rinaldi 2001); estimation of soil temperature (Potter and Williams 1994;Roloff, de Jong, and Nolin 1998a); and soil carbon sequestration as a function of cropping and management systems (Lee, Phillips, and Liu 1993;Apezteguía, Izaurralde, and Sereno 2002). The flexibility of EPIC has also led to its adoption within several integrated economic and environmental modeling systems that have been used to evaluate agricultural policies at the farm, watershed, and/or regional scale (e.g., Taylor, Adams, and Miller 1992;Bernardo et al 1993;Foltz, Lee, and Martin 1993;Babcock et al 1997).…”

Simulating Long‐Term and Residual Effects of Nitrogen Fertilization on Corn Yields, Soil Carbon Sequestration, and Soil Nitrogen Dynamics

“…It has also been used to estimate soil erosion (Kelly et al, 1996;Poudel et al, 2000) and N uptake (Cavero et al, 1999). The water balance component in EPIC has been used to predict soil water content (Costantini et al, 2002), irrigation timing and amount (Rinaldi, 2001), runoff and P losses (Pierson et al, 2001), and nitrate leaching (Chung et al, 2001). With increased awareness of climate change issues, EPIC has been increasingly used to simulate the impacts of climate change and elevated atmospheric CO 2 on agricultural production and ecosystem processes (Stockle et al, 1992a(Stockle et al, , 1992bFavis-Mortlock et al, 1991;McKenney et al, 1992;Easterling et al, 1996;Lee et al, 1996;Phillips et al, 1996;Brown and Rosenberg, 1997;Dhakhwa et al, 1997;Brown and Rosenberg, 1999;Brown et al, 2000;Tan and Shibasaki, 2003;Izaurralde et al, 2003).…”