Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 8/1/2008
Publication Date: 10/5/2008
Citation: Hoilett, N.O., Eivazi, F., Adisa, S., Nkongolo, N., Kremer, R.J. 2008. Enzymes, Total Organic Carbon, Microbial Biomass, and Greenhouse Gas Efflux in a Central Missouri Soybean Field [abstract]. ASA-CSSA-SSSA Annual Meeting Abstracts. ASA-CSSA-SSSA Annual Meeting. October 5-9, 2008, Houston, TX. 2008 CD-ROM. Interpretive Summary:
Technical Abstract: Carbon and nitrogen enter the atmosphere primarily as carbon dioxide (CO2) and nitrous oxide (N2O), respectively, partly due to anthropogenic effects of industrial and agricultural processes. The effects of these greenhouse gases (GHG) on global climate change and the environment require a better understanding of processes that govern GHG efflux. Soil physical and chemical properties influence emission/consumption of GHG; however there is limited information on the relationship among soil microbial activities, management practices, and GHG efflux characteristics. Our objective was to examine the influence of soil microbial activity on the spatial distribution of GHG sources from a soybean field. Soil air samples were collected from 20 gas-collection chambers installed equidistantly within a 0.5-ha plot established on a Waldron silty clay (Aeric fluvaquent) in a soybean field; soil samples (10-cm depth) were collected adjacent to each chamber. Laboratory assessments of soils included microbial biomass by total organic carbon (TOC) and substrate induced respiration; and activities of glucosidase and urease, enzymes involved in C and N cycling. Greenhouse gas components determined in air samples were correlated with changes in microbial biomass, TOC, and enzymatic activity. Because soil microorganisms are integral to nutrient cycling and other biological, physical, and chemical processes, an assessment of microbial properties will provide valuable information on their relationship with GHG effluxes. Information developed from this study will be useful in constructing predictive models of GHG effluxes from different agroecosystems to determine potential contributions to overall GHG emissions and to climate change.