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Title: Decision Support Tool For Optimizing Best Management Practices In The US Corn Belt

Author
item CAUSARANO, HECTOR - VISITING SCIENTIST
item Doraiswamy, Paul
item McCarty, Gregory
item Stern, Alan
item MILAK, SUSHIL - SSAI

Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 3/24/2008
Publication Date: 4/23/2008
Citation: Causarano, H., Doraiswamy, P.C., McCarty, G.W., Stern, A.J., Milak, S. 2008. Decision support tool for optimizing best management practices in the US Corn Belt [abstract]. 2008 BARC Poster Day.

Interpretive Summary:

Technical Abstract: Direct measurements combined with system modeling are used to estimate the impacts of management practices on crop yields and soil organic carbon (SOC). We developed a decision support tool that integrates the Environmental Policy Integrated Climate (EPIC) model with soil, climate, land use, and management data. Model simulations are conducted at a grid-cell level of 2.6 km2 (1 mi2) in croplands of Iowa, Illinois and Indiana. The tool can be used by policymakers for environmental impact evaluations of cropping practices in the US Corn Belt, and by agricultural producers for field-level crop management decisions. The EPIC model accurately simulates state-wide averages of corn (Zea mays L.) and soybean (Glycine max L. Merr.) yields (R2 = 0.87). Estimated current SOC stocks in the top 20 cm vary considerably (11–157 Mg C ha-1) and are largely controlled by tillage practices, clay content, slope and elevation. Provided that current trends in adoption of conservation tillage continues, SOC changes are estimated at -0.28 to 0.52 Mg C ha-1 yr-1 depending on topography. The uncertainties of model results are 26–30% for regional-level simulations, but with site-specific data reduces to 8–11%. 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 calibration and validation. Overall, combining land use maps with EPIC proved valid for predicting impacts of management practices on crop yields and SOC. Our decision support tool can be used to optimize best management practices scenarios for the 22.2 Mha of croplands in Iowa, Indiana and Illinois.