English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Soil carbon accounting and prediction play a key role in building decisionsupport systems for land managers selling carbon credits, in the spirit of theParis and Kyoto protocol agreements. Land managers typically rely oncomputationally complex models fit using sparse datasets to make these accountsand predictions. The model complexity and sparsity of the data can lead toover-fitting, leading to inaccurate results when making predictions with newdata. Modellers address over-fitting by simplifying their models and reducingthe number of parameters, and in the current context this could involveneglecting some soil organic carbon (SOC) components. In this study, weintroduce two novel SOC models and a new RothC-like model and investigate howthe SOC components and complexity of the SOC models affect the SOC predictionin the presence of small and sparse time series data. We develop modelselection methods that can identify the soil carbon model with the bestpredictive performance, in light of the available data. Through this analysiswe reveal that commonly used complex soil carbon models can over-fit in thepresence of sparse time series data, and our simpler models can produce moreaccurate predictions.

Modelling and predicting soil carbon sequestration: is current model structure fit for purpose?