Tuesday, 8 November 2005 - 9:30 AM
132-6

Assessing the Usefulness of Simple Mathematical Models to Describe Soil Carbon Dynamics.

Armen R. Kemanian1, David R. Huggins2, Valipuram S. Manoranjan1, and Claudio O. Stockle1. (1) Washington State University, Smith Hall, Biological Systems Engineering Department, Pullman, WA 99164-6120, (2) USDA-ARS, 247 Johnson Hall, Pullman, WA 99164-6420

The rate of C storage in the soil (Cs) depends on the balance between the inputs of C from organic residues (Ci) and the outputs due to microbial oxidation of organic matter. We hypothesize that simple mathematical models can be effective at representing soil carbon dynamics. We present empirical models of soil C dynamics of the general form Csdt = h(Cs)Ci - k(Cs)Cs, where h and k are the residue humification and apparent soil C decomposition coefficients. The simplest solution to this equation results from assuming that h and k are constant and hence at steady state Cs = hCi/k. This implies that the storage capacity of the soil is linearly dependent on Ci. There is evidence, however, that soils have a finite capacity to store C. In addition, the Cs constituents have different turnover rates i.e. k is a weighted average of the individual k's of each soil C fraction. Therefore, we developed models in which h and k are functions of Cs. Making h a function of Cs provides a simple way of representing a “Cs carrying capacity”. Making k a function of Cs provides a means for accelerating the turnover rate as Cs increases, with the underlying assumption that the higher the Cs content the lower the soil recalcitrant C fraction. If properly parameterized, a single mathematical function can be used for different soil layers provided that texture does not change dramatically and that C inputs from roots at different depths are known. The effect of residue composition on h such as variations in lignin content can also be accommodated. These simple models can be useful to predict Cs dynamics and provide feedback information to improve existing mechanistic models.

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