Innovative approaches in soil carbon sequestration modelling for better prediction with limited data.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
08 Feb 2024
08 Feb 2024
Historique:
received:
18
11
2023
accepted:
01
02
2024
medline:
8
2
2024
pubmed:
8
2
2024
entrez:
7
2
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 38326402
doi: 10.1038/s41598-024-53516-z
pii: 10.1038/s41598-024-53516-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3191Subventions
Organisme : Australian Research Council Discovery Project
ID : DP200102101
Informations de copyright
© 2024. The Author(s).
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