|CAUSARANO, H - Universidad Nacional De Asuncion
|NORFLEET, M - Natural Resources Conservation Service (NRCS, USDA)
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 4/29/2009
Publication Date: 7/6/2009
Citation: Franzluebbers, A.J., Causarano, H.J., Norfleet, M.L. 2009. Effectiveness of the soil conditioning index to predict soil organic carbon sequestration in the southeastern USA [abstract]. International Symposium on Soil Organic Matter Dynamics, July 6-9, 2009, Colorado Springs, CO.
Interpretive Summary: .
Technical Abstract: Rapid and reliable assessment of the potential of various agricultural management systems to sequester soil organic C is needed to promote conservation and help mitigate greenhouse gas emissions. A growing database is emerging from detailed field experiments on how conservation agricultural systems can sequester soil organic C. Unfortunately, many results appear to be site-, soil- and cropping system-specific, resulting in uncertainty of how to predict the effect of management in different environments, soil types, and crop management systems. The soil conditioning index is a relatively simple model used by the USDA Natural Resources Conservation Service that can predict relative changes in soil organic C based on three important conditions: (1) organic material grown or added to the soil, (2) field operations that alter organic material placement in the soil profile and that stimulate organic matter breakdown, and (3) erosion that removes and sorts surface soil organic matter. Our objective was to develop a quantitative relationship between (1) published soil organic C data derived from field experiments under various management systems throughout the southeastern USA and (2) index values predicted from those management systems using the soil conditioning index. Data will be analyzed for the strength of overall relationship, as well as for identifying unique relationships for certain management conditions. This information will be essential to validate the use of the modeling approach across the diverse set of conditions prevalent in the southeastern USA.