|Lee, Joon Hee|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 2/4/2014
Publication Date: 7/13/2014
Citation: Lee, J., Archer, D.W. 2014. Simulating soil organic carbon change in oilseed cropping systems in North Dakota. Meeting Abstract. Paper number 14188717. ASABE-CSBE Joint Meeting Abstracts, Montreal, Quebec, Canada. July 13-16, 2014.
Technical Abstract: An important driver for the adoption of renewable fuels is reduction in greenhouse gas (GHG) emissions, and the quantity of greenhouse gas reductions achieved is used in determining the fuel feedstocks and conversion pathways that can be used for fuels to meet the U.S. renewable fuels standard. Estimating of GHG emissions from biofuel feedstock production cropping systems is an important component in quantifying GHG emissions through entire process from feed stock to final product use for oilseed based renewable fuels. Soil Organic Carbon (SOC) change is key measure for calculating GHG emission from cropping systems because increase of SOC is regarded as CO2 deposition from atmosphere to soil. Even though many researchers have simulated long term impacts of cropping system on SOC, the calibration and validation for C dynamic parameters using long-term soil profile data have been limited. The objective of this study is to model long-term SOC change under impact of Brassica oil seed cropping systems with calibration and validation of soil C dynamics parameters in the EPIC model. Microbial decay rate coefficient (Mde), which adjusts soil water-temperature-oxygen equation, was calibrated and validated using soil profile data in 1983, 1991 and 2001 from long-term soil quality studies with spring wheat-winter wheat-sunflower rotation under no-tillage conducted at Mandan, ND since 1983. We used default crop growth parameters for sunflower, published winter wheat growth parameters from several areas of the Northern Great Plains region, and spring wheat growth parameters from field scale crop yield and management data at Mandan, ND. The calibration criterion for Mde was the weighted average of absolute differences of SOCs (g C/kg) between observation and estimation in over all soil layers in 1991. The optimum Mde was 1.2 with a weighted average absolute difference of SOC of 1.0 g C/kg. The SOC changes for 7-year simulation ranged with Mde from - 0.19 t C/ha (for Mde=1.5) to 5.56 t C/ha (for Mde=0.5). Validation was conducted with a 17-year run from 1984-2000 and comparing measured and simulated SOCs in the surface soil layer (0- 8 cm depth) in spring of 2001 prior to planting. The SOCs (g C/kg) was 11 % over- estimated in the validation run with optimum Mde. The 3-year average crop yields from 1998,1999 and 2000 were 19%, 52%, 33% over estimated for spring wheat, winter wheat and sunflower respectively compared to observations on the same field as the SOC validation. After calibration and validation, SOC and crop yields were modeled for 20 major SSURGO soil map units in Ward County, ND, one of pilot counties being evaluated for potential oilseed supply for hydrotreated renewable jet fuel production. The simulation was conducted under two rotation scenarios, canola-spring wheat-spring wheat and continuous spring wheat with no-tillage. The soil parameters from SSURGO were initialized by 100 year run with continuous spring wheat before imposing the two rotation scenario treatments. We used spring wheat and canola growth parameters calibrated and validated using field scale yield and management data from Mandan, ND. Initial results from 50 years simulation indicate SOCs decreased with most scenarios except canola-spring wheat-spring wheat rotation in 2 soil map units. However, SOC reduction was less for the canola rotation relative to the continuous spring wheat rotation on all soil map units. The differences of SOC changes between two rotations varied across soil map units from 172 to 212 kg C/ha/yr. The estimated average annual yields were 2.0 t/ha, dry basis for continuous spring wheat, and 2.1 and 1.6 t/ha, dry basis for spring wheat and canola with canola-spring wheat-spring wheat rotation.