SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation

Abstract

Maintaining soil organic matter (SOM) is crucial for healthy and productive agricultural soils and requires understanding at the process level, including the role of SOM protection by soil aggregates and the connection between microbial growth and aggregate formation. We developed the Soil Aggregation through Microbial Mediation (SAMM) model, to represent this important connection. The pools of SAMM are fully measurable, and we calibrated and evaluated it against data from a long-term bare fallow experiment in a tropical sandy soil. This experiment received additions of plant litter of different compositions, which resulted in twice the soil carbon stocks in the best treatment compared to the control (about 8 vs. 4 t C ha−1 in 0–15 cm soil depth) after 25 years. As hypothesized, the SAMM model effectively represented the microbial growth response after the addition of litter and the subsequent formation and later destabilization of aggregates. The low correlations between different calibrated model parameters (r<0.5 for all parameters; r>0.4 for only 4 of 22) showed that SAMM is parsimonious. SAMM was able to capture differences between treatments in soil organic carbon (Nash–Sutcliffe modeling efficiency (EF) of 0.68), microbial nitrogen (EF of 0.24), and litter carbon (EF of 0.80). The amount of carbon within the aggregates (EF of 0.60) and in the free silt and clay fraction (EF of 0.24) was also simulated very well to satisfactorily. Our model results suggested that in spite of the sandy soil, up to 50 % of carbon stocks were stabilized through aggregate protection mechanisms; and that microbial and physical aggregate formation coexists. A version of the SAMM model without aggregate protection (SAMMnoAgg) initially failed to stabilize soil organic carbon (EF decreased to −3.68) and the simulation of microbial nitrogen worsened (EF of 0.13). By recalibrating SAMMnoAgg, it was possible to partially correct for the lack of aggregate protection by reducing the rate of mineral-attached carbon decomposition by about 85 % (EF of 0.68, 0.75, and 0.18 for SOC, litter carbon, and microbial nitrogen, respectively). However, the slightly better evaluation statistics of SAMM (e.g., Akaike information criterion of 5351 vs. 5554) suggest that representing aggregate dynamics in SOM models can be beneficial and necessary to understand the mechanism behind SOM dynamics. Our results indicate that current models without aggregate formation partly compensate for the absence of aggregate protection by lowering the turnover rates of other pools. Thus, they remain suitable options where data on aggregate associated carbon are not available.