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. Rather, use of these models requires manual interaction and data transfer. As a result, it is currently not feasible to use these computational tools to perform detailed sustainable agricultural residue availability assessments across large spatial domains or to consider a broad range of land management practices. This paper 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. This enables the exploration of the detailed sustainable residue harvest scenarios needed to establish sustainable residue availability. Using this computational tool, an assessment study of residue availability for the state of Iowa was performed. This study included all soil types in the state of Iowa, four representative crop rotation schemes, variable crop yields, three tillage management methods, and five residue removal methods. 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. However, when considering the economics and logistics of residue harvest, yields below 2.25 Mg ha−1 are generally considered to not be viable for a commercial bioenergy system. Applying this constraint, the total agricultural residue resource available in Iowa under current management practices is 19 million Mg. Previously published results have shown residue availability from 22 million Mg to over 50 million Mg in Iowa.
This paper was presented at the 2012 International Congress on Environmental Modelling and Software in Leipzig, Germany on July 15, 2012.
Abstract: Agricultural residues are the largest near term source of cellulosic biomass for bioenergy production, but removing agricultural residues sustainably requires considering the critical roles that residues play in the agronomic system. Determination of sustainable removal rates for agricultural residues has received significant attention and integrated modeling strategies have been built to evaluate sustainable removal rates considering soil erosion and organic matter constraints. However, the current integrated model, comprised of the agronomic models WEPS, RUSLE2, and SCI, does not quantitatively assess the impacts of residue removal on soil organic carbon and long term crop yields. Furthermore, it does not evaluate the impact of residue removal on greenhouse gas emissions, specifically N2O and CO2 gas fluxes from the soil surface. The DAYCENT model simulates several important processes for determining agroecosystem performance. These processes include daily nitrogen gas flux, daily CO2 flux from soil respiration, soil organic carbon and nitrogen, net primary productivity, and daily water and nitrate leaching. Each of these processes is an indicator of sustainability when evaluating emerging cellulosic biomass production systems for bioenergy. This paper couples the DAYCENT model with the existing integrated model to investigate additional environment al impacts of agricultural residue removal. The integrated model is extended to facilitate two - way coupling between DAYC ENT and the existing framework. The extended integrated model, including DAYCENT, is applied to investigate additional environmental impacts from a recent sustainable agricultural residue removal dataset. Results show some differences in sustainable removal rates compared to previous results for a case study county in Iowa , US . The extended integrated model also predict s that long term yields will decrease .32% – 1.43 % under sustainable residue removal management practices.