354-1 Modeling Measurable Soil Organic Matter Pools.

See more from this Division: S03 Soil Biology & Biochemistry
See more from this Session: Carbon, Nitrogen, and Microbial Responses to Cropping and Management Systems
Wednesday, October 19, 2011: 1:00 PM
Henry Gonzalez Convention Center, Room 006B
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Moran Segoli, Plant Sciences, University of California – Davis, Davis, CA, Steven De Gryze, Terra Global Capital, San Francisco, CA, Mac Post, Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN and Johan Six, Plant Sciences, University of California – Davis, Davis, CA
Models of soil organic matter (SOM) dynamics are used for predicting the effects of management and climate change on soil carbon stabilization and soil nutrient availability. Current models are based on a few conceptual SOM pools that are not measurable. Each SOM pool is associated with a specific turnover time that represents the stability of the material transformed or respired. However, there is ample evidence that the physical aggregation of soil has a significant effect on SOM dynamics. It has been shown that these physical aggregations and their dynamics can be measured directly in the laboratory and in the field, but they have not been explicitly incorporated in models. Here, we present a simulation model that integrates soil aggregate dynamics with SOM dynamics. In the model we consider unaggregated and microaggregated soil that can exist within or external to macroaggregated soil. The organic matter inside of each aggregate class is divided into particulate organic matter, mineral-associated organic matter fraction and an inert organic matter pool. We used empirical data from laboratory and field experiments to estimate the biological and environmental effects on the rate of formation and breakdown of macroaggregates and microaggregates, and the organic matter dynamics within these different aggregate classes. The simulation model was validated with long-term field data. The advantage of a model that is based on measurable SOM fractions is that its internal structure can be validated by field data. Furthermore, models that are based on mechanistic processes have the potential advantage of being more robust and, therefore, providing predictions to a larger array of scenarios, including scenarios that cannot be manipulated in field conditions.
See more from this Division: S03 Soil Biology & Biochemistry
See more from this Session: Carbon, Nitrogen, and Microbial Responses to Cropping and Management Systems
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