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ARS Home » Midwest Area » Morris, Minnesota » Soil Management Research » Research » Publications at this Location » Publication #299569
Title: Assessing the soil carbon, biomass production, and nitrous oxide emission impact of corn stover management for bioenergy feedstock production using DAYCENT

Author
item CAMPBELL, ELEANOR - Colorado State University
item Johnson, Jane
item Jin, Virginia
item Lehman, R - Michael
item Osborne, Shannon
item Varvel, Gary
item PAUSTIAN, KEITH - Colorado State University

Submitted to: BioEnergy Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/13/2014
Publication Date: 2/4/2014
Publication URL: https://handle.nal.usda.gov/10113/58415
Citation: Campbell, E.E., Johnson, J.M., Jin, V.L., Lehman, R.M., Osborne, S.L., Varvel, G.E., Paustian, K. 2014. Assessing the soil carbon, biomass production, and nitrous oxide emission impact of corn stover management for bioenergy feedstock production using DAYCENT. BioEnergy Research. 7(2):491-502.

Interpretive Summary: The leaves, stalk and cobs that remain after corn grain is harvested is called stover. Corn stover is expected to be used as a major non-food feedstock for making ethanol or used a as a substitute for coal or other fossil-fuels. It is important to understand what may happen to crop yields and fields if stover is harvested instead of returned to the field. It is not possible to measure effects in all places or predict into the future using collected data. Therefore, models are useful tools to predict what may happen at sites where data is not collected or to predict long-term future effects. Comparing how well data simulated using a model matches field-collected data is a process used to make sure a model is providing useful prediction. Therefore, empirical data was collected by ARS scientists and university partners at five sites across the country. Weather (annual rainfall and temperature) and soil data from each of these sites are input into a model called DAYCENT to simulate what may happen to crop yield, soil organic matter content, and greenhouse gas emission if corn stover were harvested. The simulated model data was compared to the collected data. In general, the model simulations for crop yield, soil organic matter content, and greenhouse gas emission compared to the values measured. Therefore, the DAYCENT model can be a useful tool for developing sustainable guidelines for harvest guidelines that protect future food production capacity and avoid undesired environmental consequences. This information provides a unique opportunity to improve process models; it also provides guidance to the bioenergy industry, producers and the general public including policy-makers of the benefits and risks associated with plant-based energy. Thereby leading to informed decisions. This research contribute to the USDA-ARS-REAP project.

Technical Abstract: Harvesting crop residue needs to be managed such that agroecosystem health and productivity are protected. DAYCENT, a process-based modeling tool, may be suited to accommodate region-specific factors and provide regional predictions for a broad array of agroecosystem impacts associated with corn stover harvest. Grain yield, soil C, and N2O emission data collected at Corn Stover Regional Partnership experimental sites were used to test DAYCENT performance modeling the impacts of corn stover removal. DAYCENT estimations of annual grain and stover yields were correlated (adjusted r2=0.32, p<0.0001; adjusted r2=0.53, p<.001), respectively, with measured yields but DAYCENT had a tendency to overestimate low and underestimate high measured values. Measured and simulated grain yields, across years and sites, did not differ as a function residue removal. Modeled and measured SOC change for all sites were correlated (adjusted r2=0.54, p<0.0001), but DAYCENT tended to overestimate SOC loss with full residue removal and conventional tillage. Simulated and measured SOC change did not vary by residue removal rate. Simulated annual N2O flux was close to measured values at low rates (=2 kg N2O-N ha-1 yr-1), but substantially underestimated with high emission rates (> 3 kg N2O-N ha-1 yr-1). Overall DAYCENT performed well at simulating SOC change and stover yields, reasonably well simulating grain yields, and variably estimating N2O emissions. DAYCENT appears to be a useful tool for predictive modeling of corn stover removal impacts on a regional basis, in order to support recommendation of sustainable practices to advance bioenergy industry based on corn stover feedstock material.