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Unpublished Paper
An investigation of sustainable agricultural residue availability for energy applications
Graduate Theses and Dissertations
  • David Jon Muth, Jr., Iowa State University
Degree Type
Date of Award
Degree Name
Doctor of Philosophy
Mechanical Engineering
First Advisor
Kenneth M Bryden

An integrated modeling strategy is developed to determine the potential for sustainable agricultural residue removal potential for bioenergy production. Agricultural residues have been identified as a significant potential resource for bioenergy production, but serious questions remain about the sustainability of harvesting residues. Agricultural residues play an important role in limiting soil erosion from wind and water and in maintaining soil organic carbon. Because of this, multiple factors must be considered when assessing sustainable residue harvest limits. Validated and accepted modeling tools for assessing these impacts include the Revised Universal Soil Loss Equation Version 2 (RUSLE2), the Wind Erosion Prediction System (WEPS), and the Soil Conditioning Index. Currently, these models do not work together as a single integrated model. This dissertation presents an integrated modeling strategy that couples existing datasets with the RUSLE2 water erosion, WEPS wind erosion, and Soil Conditioning Index soil carbon modeling tools to create a single integrated residue removal modeling system. Using this computational tool, a series of studies were performed. The first investigates agricultural residue removal potential for the state of Iowa. The key conclusions of this study are that under current management practices and crop yields nearly 26.5 million Mg of agricultural residue are sustainably accessible in the state of Iowa, and that through the adoption of no till practices residue removal could sustainably approach 40 million Mg. The next study provides a spatially comprehensive assessment of sustainable agricultural residue removal potential across the US. This type of assessment is needed to support development and investment decisions for an emerging bioenergy industry. Earlier assessments determining the quantity of agricultural residue that could be sustainably removed for bioenergy production at the regional and national scale faced a number of computational limitations. These limitations included the number of environmental factors, the number of land management scenarios, and the spatial fidelity and spatial extent of the assessment. The study presented here provides estimates of county average and state totals of sustainably available agricultural. The results of the assessment show that in 2011 over 150 million metric tons of agricultural residues could have been sustainably removed across the US. Projecting crop yields and land management practices out to 2030, the assessment determines that over 207 million metric tons of agricultural residue could be sustainably removed for bioenergy production at that time. The next study develops a computational strategy that utilizes data inputs from multiple spatial scales to investigate how variability within individual fields can impact sustainable residue removal. Increased availability of sub-field scale datasets such as grain yield data, high fidelity digital elevation models, and soil characteristic data provides an opportunity to investigate the impacts of sub-field scale variability on sustainable agricultural residue removal. Using three representative fields in Iowa, this paper contrasts the results of current Natural Resources Conservation Services (NRCS) conservation management planning analysis with sub-field scale analysis for rake and bale removal of agricultural residue. The results of the comparison show that the field average assumptions used in the NRCS conservation management planning may lead to unsustainable residue removal decisions for significant portions of some fields. The final study examines the potential of a conceptual variable rate residue removal equipment configuration capable of on-the-fly residue removal rate adjustments from 0%-80% by modeling residue removal at thirteen removal rate levels: 0% and 25%-80% at 5% increments. Three Iowa fields with diverse soil, slope, and grain yield characteristics were examined, and the sustainable removal rate of agricultural residue using the conceptual variable rate removal equipment was 2.35, 7.69, and 5.62 Mg ha-1. In contrast, the sustainable removal rates using rake and bale removal for the entire field were 0.0, 6.40, and 5.06, respectively.

Copyright Owner
David Jon Muth Jr
Date Available
File Format
File Size
198 pages
Citation Information
David Jon Muth. "An investigation of sustainable agricultural residue availability for energy applications" (2012)
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