Submitted to: Meeting Abstract
Publication Type: Abstract only
Publication Acceptance Date: 6/2/2008
Publication Date: 10/5/2008
Citation: Causarano, H.J., Doraiswamy, P.C., Izaurralde, R.C., McCarty, G.W., Milak, S., Stern, A.J. 2008. Simulating soil organic carbon in the US Corn Belt and associated uncertainties [abstract]. American Society of Agronomy. 2008 CDROM. Interpretive Summary:
Technical Abstract: Increasing soil organic carbon (SOC) on degraded lands has positive impacts on soil quality and productivity, and is an accepted approach to offset CO2 emissions in the short term. Direct measurements combined with system modeling are used to estimate the impacts of management practices on SOC at the regional scale but few studies report on estimations uncertainty. Sources of uncertainty are model errors, model initialization, measurement errors, data and model aggregation, and abrupt changes in the system that are not adequately simulated. We evaluated the Environmental Policy Integrated Climate (EPIC) model for its ability to simulate SOC and estimated the uncertainty of simulation results. A detailed and computationally intensive approach for integrating the EPIC model with soil, climate, land use, and management data was developed to conduct simulations at a grid-cell level of 2.59 km2 in croplands of Iowa, Illinois and Indiana. Uncertainties were 26–30% when simulations were conducted at the regional scale but reduced to 8–11% with site-specific simulations. Estimated current SOC stocks in the top 20 cm varied considerably (11–157 Mg C ha-1) and were largely controlled by tillage practices, clay content, slope and elevation. Provided that current trends in adoption of conservation tillage continues, SOC changes were estimated at -0.28 to 0.52 Mg C ha-1 yr-1 depending on topography. We calculated that SOC would increase from 660 Tg C in 2008 to 700 Tg C in 2018 (3.4 Tg C yr-1), but based on the uncertainty, we were not able to conclude that these croplands are creating a net sink for atmospheric CO2. More data on spatial and temporal variation in SOC are needed to improve model accuracy. Overall, combining land use maps with EPIC proved valid for predicting impacts of management practices on crop yields and SOC.