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ARS Home » Southeast Area » Florence, South Carolina » Coastal Plain Soil, Water and Plant Conservation Research » Research » Publications at this Location » Publication #172873


item HE, X
item Vanotti, Matias

Submitted to: Journal of Environmental Quality
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/23/2006
Publication Date: 7/6/2006
Citation: He, X., Izaurralde, R.C., Vanotti, M.B., Williams, J.R., Thomson, A.M. 2006. Simulating long-term and residual effects of nitrogen fertilization on corn yields, soil carbon sequestration, and soil nitrogen dynamics. Journal of Environmental Quality. 35:1608-1619.

Interpretive Summary: The continued accumulation of CO2 and other greenhouse gases, such as methane and nitrous oxide, in the atmosphere is cause of great concern because of their anticipated impact on global climate. Soil carbon sequestration (SCS), achievable through adoption of no-tillage management and conversion of agricultural land to native vegetation, among other practices, has emerged as an important technology to mitigate the rate of increase of atmospheric CO2 and, thus, attenuate the impacts of climate change. The Environmental Policy Integrated Climate (EPIC) model is a field-scale agroecosystem model capable of simulating crop growth as a function of weather, soil and management conditions (e.g., tillage, fertilization, irrigation, crop rotations) as well as many other processes related to managed ecosystems (e.g., wind and water erosion, water balance, pesticide fate, etc.). The model was enhanced with algorithms to describe the coupled cycling of C and N in soils. This improvement provides the capability to simulate soil C and N dynamics as affected by weather-soil-management processes and soil erosion. In this work we evaluated the ability of the enhanced EPIC model to simulate corn (Zea mays L.) yields, soil organic C (SOC) dynamics, SCS rates, and soil microbial biomass C and N, and net N mineralization measured in a 34-yr-long experiment conducted in Arlington, WI. Overall, our results suggest that the model adequately simulated average crop yields but did not fully capture their interannual variability. Our results also show realistic depictions of SOC dynamics, accurate estimations of SCS rates, and credible simulations of microbial biomass dynamics. Simulations of net N mineralization rates were realistic but lower than those determined from leaching-incubation experiments of disturbed soil samples. Improvements in EPIC's ability to predict interannual variability of crop yields may lead to improved estimates of SCS.

Technical Abstract: Soil C sequestration (SCS) has the potential to attenuate increasing atmospheric CO2 and mitigate greenhouse warming. Understanding of this potential and complex soil C process is aided by the use of simulation models. We evaluated the ability of the EPIC model to simulate corn (Zea mays L.) yields and soil organic C (SOC) at Arlington, WI, during 1958-1991. Corn was grown continuously on a Typic Argiudoll with 3 N levels: LTN1 (control), LTN2 (medium), and LTN3 (high). The LTN2 N rate started at 56 kg/ha (1958), increased to 92 kg/ha (1963), and reached 140 kg/ha (1973). The LTN3 N rate was maintained at twice the LTN2 level. In 1984, each plot was divided into 4 subplots receiving N at 0, 84, 168 and 252 kg/ha. Five treatments were used for model evaluation. Percent errors of mean yield predictions during 1958-1983 decreased as N rate increased (LTN1 = -5.0%, LTN2 = 3.5%, and LTN3 = 1.0%). Percent errors of mean yield predictions during 1985-1991 were larger than during the first period. Simulated and observed mean yields during 1958-1991 were highly correlated (R2 = 0.961**). Simulated SOC agreed well with observed values with percent errors from -5.8% to 0.5% in 1984 and from -5.1% to 0.7% in 1990. EPIC captured the dynamics of SOC, SCS, and microbial biomass. Simulated net N mineralization rates were lower than those from lab incubations. Improvements in EPIC’s ability to predict annual variability of crop yields may lead to improved estimates of SCS.