  Microbial biomass carbon (MBC), a crucial soil labile carbon fraction, is the
most active component of the soil organic carbon (SOC) that regulates
bio-geochemical processes in terrestrial ecosystems. Some studies in the
literature ignore the effect of microbial population growth on carbon
decomposition rates. In reality, we might expect that the decomposition rate
should be related to the population of microbes in the soil and have a positive
relationship with the size of the microbial biomass pool. In this study, we
explore the effect of microbial population growth on the accuracy of modelling
soil carbon sequestration by developing and comparing two soil carbon models
that consider a carrying capacity and limit to the growth of the microbial
pool. We apply our models to three datasets, two small and one large datasets,
and we select the best model in terms of having the best predictive performance
through two model selection methods. Through this analysis we reveal that
commonly used complex soil carbon models can over-fit in the presence of both
small and large time-series datasets, and our simpler model can produce more
accurate predictions. We conclude that considering the microbial population
growth in a soil carbon model improves the accuracy of a model in the presence
of a large dataset.
