  Soil carbon accounting and prediction play a key role in building decision
support systems for land managers selling carbon credits, in the spirit of the
Paris and Kyoto protocol agreements. Land managers typically rely on
computationally complex models fit using sparse datasets to make these accounts
and predictions. The model complexity and sparsity of the data can lead to
over-fitting, leading to inaccurate results when making predictions with new
data. Modellers address over-fitting by simplifying their models and reducing
the number of parameters, and in the current context this could involve
neglecting some soil organic carbon (SOC) components. In this study, we
introduce two novel SOC models and a new RothC-like model and investigate how
the SOC components and complexity of the SOC models affect the SOC prediction
in the presence of small and sparse time series data. We develop model
selection methods that can identify the soil carbon model with the best
predictive performance, in light of the available data. Through this analysis
we reveal that commonly used complex soil carbon models can over-fit in the
presence of sparse time series data, and our simpler models can produce more
accurate predictions. The published version of this study is available in
Scientific Reports
(https://www.nature.com/articles/s41598-024-53516-z/<10.1038/s41598-024-53516-z>)
