Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6297157 | Ecological Modelling | 2013 | 9 Pages |
Abstract
Current soil organic matter (SOM) models are empirical in nature by employing few conceptual SOM pools that have a specific turnover time, but that are not measurable and have no direct relationship with soil structural properties. Most soil particles are held together in aggregates and the number, size and stability of these aggregates significantly affect the size and amount of organic matter contained in these aggregates, and its susceptibility to decomposition. While it has been shown that soil aggregates and their dynamics can be measured directly in the laboratory and in the field, the impact of soil aggregate dynamics on SOM decomposition has not been explicitly incorporated in ecosystem models. Here, we present AggModel, a conceptual and simulation model that integrates soil aggregate and SOM dynamics. In AggModel, we consider unaggregated and microaggregated soil that can exist within or external to macroaggregated soil. Each of the four aggregate size classes contains particulate organic matter and mineral-associated organic matter fractions. We used published data from laboratory incubations to calibrate and validate 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 fractions. After calibration, AggModel explained more than 60% of the variation in aggregate masses and over 70% of the variation in aggregate-associated carbon. The model estimated the turnover time of macroaggregates as 31 and 181 days for microaggregates. Sensitivity analysis of AggModel parameterization supported the notion that macroaggregate turnover rate has a strong control over microaggregate masses and, hence, carbon sequestration. In conclusion, AggModel successfully incorporates the explicit representation for the turnover of soil aggregates and their influence on SOM dynamics and can form the basis for new SOM modules within existing ecosystem models.
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Authors
M. Segoli, S. De Gryze, F. Dou, J. Lee, W.M. Post, K. Denef, J. Six,