The effect of biologically mediated decay rates on modelling soil carbon sequestration in agricultural settings

TL;DR

This paper investigates how incorporating microbial population growth into soil carbon models affects their accuracy, finding that simpler models often outperform complex ones, especially with smaller datasets.

Contribution

The study introduces and compares two microbial growth-aware soil carbon models, demonstrating that simpler models can be more accurate and less prone to overfitting.

Findings

Microbial growth consideration improves model accuracy with large datasets.

Complex models tend to overfit small and large datasets.

Simpler models outperform complex ones in predictive performance.

Abstract

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.