235-2 Predicting and Comparing Soil Organic Carbon Changes in Corn- and Prairie-Based Cropping Systems over 50 Years.

See more from this Division: ASA Section: Climatology & Modeling
See more from this Session: Model Applications in Field Research: I
Tuesday, November 4, 2014: 1:20 PM
Renaissance Long Beach, Renaissance Ballroom I
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Ranae Dietzel1, Sotiris V Archontoulis1 and Matt Liebman2, (1)Iowa State University, Ames, IA
(2)1401 Agronomy Hall, Iowa State University, Ames, IA
Increasing C in the soil and thus decreasing C in the atmosphere is desirable for soil health and environmental quality.  Reconstructed prairies may have the potential to increase soil organic carbon (SOC) relative to predominant corn-based systems, but slow changes in SOC are difficult to detect in soils already high in C, such as those found in the Midwestern United States.  We used the APSIM model and forecasted climate data to predict changes in SOC over the next fifty years.  The model was calibrated and tested with six years of data from unfertilized reconstructed prairie, N-fertilized reconstructed prairie, continuous corn with and without a cover crop, and a corn-soybean rotation grown near Ames, IA.  Empirical data available for calibration included baseline soil measurements (SOC, pH, particle size analysis), daily soil moisture and temperature measurements to 45 cm, aboveground biomass and grain yields, belowground biomass to 1 m, C and N content of above- and belowground biomass, and bi-weekly CO2 measurements.  The establishment of prairies led to a large increase in the root C pool, but most of this C remained in the root C pool for the first ~20 years of the simulation before gradually transitioning to the soil C pool.  At the same time, corn-based treatments supplied a small amount of C to the soil C pool in the form of roots.  After 50 years, prairies had only slightly more soil C than corn-based treatments, but much more root C than corn-based treatments.
See more from this Division: ASA Section: Climatology & Modeling
See more from this Session: Model Applications in Field Research: I
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