The use of stable carbon isotopes for estimating soil organic matter turnover rates

Abstract

In natural ecosystems, soil organic carbon (C) is derived almost exclusively from the residues of plants growing in situ. In agroecosystems, it has at least two origins: one is the remains from the previous native vegetation, and the other is the remains of the crop and the decomposition of its residues. Where vegetation has changed from plants with the C3 photosynthetic pathway to C4 pathway or vice versa, changes in the natural abundance of 13C in soil organic matter (SOM) over time can be used to identify sources of organic C in soil and to determine the turnover rate of SOM. For example, large areas of C3 tropical forest have been replaced with C4 pasture or cropland. Changes in the δ 13C values of soil organic C in these areas reflect soil organic matter turnover rate, and provide insight regarding the functional role of tropical ecosystems in the global C cycle. This paper illustrates how the stable isotope 13C can be used to estimate SOM turnover rates and the sensitivity of different models and different model parameters, using a chronosequence of forest and pastures of different ages from the Brazilian Amazon. A single-compartment exponential decay model and a two-compartment model in which SOM was divided into stable and labile components yielded similar estimates of soil C turnover time at the surface but divergent estimates at depth. The one-compartment model gave the least variable estimates of model parameters and turnover times and was also relatively insensitive to individual C stocks in single pastures of a particular age. Estimates of soil stable and labile C pools obtained using changes in forest soil δ 13C with depth differed from estimates obtained using the chronosequence. This suggests that upon burning and pasture creation, a portion of the previously stable soil C pool is rendered less stable. Model r 2 was a poor criterion for selecting an appropriate soil C turnover model to apply to chronosequence data. In the absence of substantial justification for segregating SOM into different compartments based on lability, modeling should be done with the simplest models possible.