Innovative Approaches in Soil Carbon Sequestration Modelling for Better Prediction with Limited Data

TL;DR

This study introduces new soil organic carbon models and model selection methods to improve prediction accuracy in sparse data scenarios, revealing that simpler models often outperform complex ones in over-fitting prevention.

Contribution

The paper presents two novel SOC models, a RothC-like model, and a model selection approach tailored for sparse datasets, enhancing prediction reliability.

Findings

Simpler models outperform complex ones with sparse data.

Complex models tend to over-fit small datasets.

Model selection improves prediction accuracy.

Abstract

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>)