Simulating Long-Term Impacts of Winter Rye Cover Crop on Hydrologic Cycling and Nitrogen Dynamics for a Corn-Soybean Crop System

Abstract

Planting winter cover crops into corn-soybean rotations is a potential approach for reducing subsurface drainage and nitrate-nitrogen (NO3-N) loss. However, the long-term impact of this practice needs investigation. We evaluated the RZWQM2 model against comprehensive field data (2005-2009) in Iowa and used this model to study the long-term (1970-2009) hydrologic and nitrogen cycling effects of a winter cover crop within a corn-soybean rotation. The calibrated RZWQM2 model satisfactorily simulated crop yield, biomass, and N uptake with percent error (PE) within ±15% and relative root mean square error (RRMSE) <30% except for soybean biomass and rye N uptake. Daily and annual drainage and annual NO3-N loss were simulated satisfactorily, with Nash-Sutcliffe efficiency (NSE) >0.50, ratio of RMSE to standard error (RSR) <0.70, and percent bias (PBIAS) within ±25% except for the overestimation of annual drainage and NO3-N in CTRL2. The simulation in soil water storage was unsatisfactory but comparable to other studies. Long-term simulations showed that adding rye as a winter cover crop reduced annual subsurface drainage and NO3-N loss by 11% (2.9 cm) and 22% (11.8 kg N ha-1), respectively, and increased annual ET by 5% (2.9 cm). Results suggest that introducing winter rye cover crops to corn-soybean rotations is a promising approach to reduce N loss from subsurface drained agricultural systems. However, simulated N immobilization under the winter cover crop was not increased, which is inconsistent with a lysimeter study previously reported in the literature. Therefore, further research is needed to refine the simulation of immobilization in cover crop systems using RZWQM2 under a wider range of weather conditions.

Effects of Subsurface Drainage Systems on Water and Nitrogen Footprints Simulated with RZWQM2

“…These rate coefficients were also used to simulate an acceptable rate of annual soil N mineralization for the general region, which was presumed to be within the range of 112 to 504 kg N ha -1 (Helmers and Castellano, 2015). Qi et al (2011) for a similar system in northeast Iowa. The inter-pool transfer coefficients were slightly adjusted based on those calibrated by Ma et al (2007a) and Thorp et al (2007), including adjusting the slow residue to intermediate SOM to 0.2, the fast residue to fast SOM to 0.5, the fast SOM to intermediate SOM to 0.5, and the intermediate SOM to slow SOM to 0.7.…”

“…Previously parameterized crop models for maize (IB1 068 Dekalb 521) and soybean (990002 M Group 2) were used, and some minor parameter adjustments were carried out following methods described by Ma et al (2006) and Thorp et al (2007). Parameters were initialized with values used for simulations in north-central Iowa by Qi et al (2011) and adjusted to match observed annual grain yields (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) as well as aboveground biomass and harvest index (2011)(2012)(2013)(2014)(2015). To simulate nine years of corn yield measurements for SERF, it was necessary to simulate two maize hybrids with minor differences in maturity length because the relative hybrid maturity ratings of corn hybrids planted at SERF ranged from 106 to 113.…”

“…The soil root growth factor (SRGF) was adjusted for the maize and soybean models in order to fit the N concentration in grain to a reasonable level and to measured values for 2011-2015 for corn and 2012-2015 for soybean. For depths of 5,15,30,40,60,90, and 120 cm, the SRFG was set to 1, 1, 0.32, 0.2, 0.12, 0.12, and 0.0 for soybeans and to 1, 1, 0.8, 0.4, 0.27, 0.1, and 0.06 for corn, respectively, by making minor adjustments from settings in past corn and soybean system modeling work by Qi et al (2011), Malone et al (2010, and Ma et al (2006). These SRGF parameter settings simulated average rooting depths of around 90 cm for soybeans and 115 cm for corn.…”

“…Additionally, setting a sufficient drainable porosity and high K sat value in the upper layer minimized runoff by increasing infiltration into the soil profile. To correctly simulate evapotranspiration (ET), albedo coefficients were adjusted based on simulations by Qi et al (2011) and Thorp et al (2007) to fit ET within acceptable ranges for the region, including the albedo of dry soil, wet soil, mature crop, and [b] Corn 1 is the calibrated parameter set for simulation of a shorter maturity group, and Corn 2 is for simulation of a longer maturity group. fresh residue, which were set to 0.2, 0.1, 0.25, and 0.8, respectively.…”

“…Simulation studies for both controlled drainage and shallow drainage have been successful in correctly predicting the reduction in drainage as well as excess water stress from higher water tables, which can generate yield loss in some soils and climates (Luo et al, 2010;Singh et al, 2006). The Root Zone Water Quality Model (RZWQM) (Ahuja et al, 2000a) has proven successful in simulating crop rotation and yield, daily and annual drainage volume, as well as nitrogen (N) dynamics, such as NO 3 -N loss in artificial subsurface drain flow (Ma et al, 2007b;Qi et al, 2011). With RZWQM, Fang et al (2012) successfully simulated the effect of a controlled drainage system on monthly tile drainage volume, N loss, and flow-weighted annual NO 3 -N concentration (FWANC) and found that the percent reduction in N loss compared to an unmanaged system was higher with increased annual rainfall.…”