Modeling carbon dynamics and social drivers of bioenergy agroecosystems

Meeting society's energy needs through bioenergy feedstock production presents a significant and urgent challenge, as it can aid in achieving energy independence goals and mitigating climate change. With federal biofuel production standards to be met within the next decade, and with no commercial scale production or markets currently in place, many questions regarding the sustainability and social feasibility of bioenergy still persist. Clarifying these uncertainties requires the incorporation of biogeochemical, biophysical, and socioeconomic modeling tools.;Chapter 2 validated the biogeochemical cycling model AGRO-BGC by comparing model estimates with empirical observations from corn and perennial C4 grass systems across Wisconsin and Illinois. AGRO-BGC, in its first application to an annual cropping system, was found to be a robust model for simulating carbon dynamics of an annual cropping system.;Chapter 3 investigated the long-term implications of bioenergy feedstock harvest on soil productivity and erosion in annual corn and perennial switchgrass agroecosystems using AGRO-BGC and the soil erosion model RUSLE2. Modeling environments included biophysical landscape characteristics and management practices of bioenergy feedstock production systems. This study found that intensifying aboveground residue harvest reduces soil productivity over time, and the magnitude of these losses is greater in corn than in switchgrass systems. Results of this study will aid in the design of sustainable bioenergy feedstock management practices.;Chapter 4 provided evidence that combining biophysical crop canopy characteristics with satellite-derived vegetation indices offers suitable estimates of crop canopy phenology for corn and soybeans in Southwest Wisconsin farms. LANDSAT based vegetation indices, when combined with a light use efficiency model, provide yield estimates in agreement with farmer reports, providing an efficient and accurate means of estimating crop yields from satellite data.;The most important stakeholder in bioenergy sustainability and feasibility research is the farmer. Chapter 5 identified and measured the influence of bioenergy feedstock choice drivers using logistic regression choice models constructed from survey and geospatial data. The strongest choice drivers among farmers willing to participate in a proposed bioenergy feedstock production program included socioeconomic, biophysical, and environmental attitudes. Outcomes of this research will be useful in designing further bioenergy policy and economic incentives.