Nigel Hoilett1, Frieda Eivazi1, Nsalambi Nkongolo2, and Robert Kremer3. (1) Lincoln University, University of Missouri, 302 Abnr Building, Columbia, MO 65211, (2) 830 Chestnut Street, Lincoln University, Lincoln University, Gis Laboratory 307 Founders Hall, Jefferson City, MO 65102-0029, (3) USDA-ARS, University of Missouri, 302 ABNR Bldg., Columbia, MO 65211-7250
Carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) concentrations are increasing at annual rates of 0.5%, 0.75% and 0.75% respectively. Worldwide concerns with greenhouse gases and their effect on global climate change and the environment requires better understanding of the processes that govern greenhouse gas efflux. Documented research has established links between soil physical and chemical properties with emission/consumption of greenhouse gasses; however a need exists for closer examination of the relationship among soil microbial properties, management practices, and greenhouse gas efflux. This study is designed to determine the spatial distribution of greenhouse gases from soil, microorganisms and microbial activity within three land use types: a forest, grassland, and a soybean field. Laboratory assessments of field samples collected from each site included determination of microbial population by most probable number (MPN) methods; microbial biomass by total organic carbon (TOC) and substrate induced respiration (SIR) measurements; and enzyme activity by beta-glucosidase and urease assays. Greenhouse gas efflux data collected at each site will be correlated with changes in microbial population, and diversity, and enzymatic activity to determine the relationships among microorganisms and greenhouse gas effluxes. Since soil microorganisms are integral to nutrient cycling, biological, physical, and chemical processes in the soil, an assessment of microbial properties will provide valuable information on the relationship between soil microbial properties and greenhouse gas effluxes. The information gathered from this study will be useful in constructing predictive models of greenhouse gas effluxes from different ecosystems relative to greenhouse gas emissions; and, by extension, global warming.