Parametrization consequences of constraining soil organic matter models by total carbon and radiocarbon using long-term field data

Summary

This modelling study used >60 years of controlled field data from the Zürich Organic Fertilization Experiment to explore how radiocarbon constraints improve parameterisation of soil organic carbon dynamics models. By applying five nested model structures derived from the ICBM framework and systematically varying the weight given to total SOC versus ¹⁴C data during Bayesian calibration, the authors demonstrated that radiocarbon information substantively reduces parameter uncertainty and influences model outcomes. The work highlights the importance of explicitly considering the relative importance of multiple data streams when calibrating SOC models.

UK applicability

The methodological approach is directly applicable to UK arable systems and existing long-term field experiments (e.g. Rothamsted). Findings on parameter uncertainty and model structural choices will inform SOC monitoring and prediction frameworks relevant to UK soil health policy and carbon sequestration targets.

Key measures

Soil organic carbon (SOC) concentration, radiocarbon (¹⁴C) ages, kinetic decomposition parameters, model parameter uncertainty (Bayesian calibration), model structural uncertainty across five nested model variants

Outcomes reported

The study quantified how incorporating radiocarbon (¹⁴C) measurements alongside total soil organic carbon data affects parameter uncertainty and model structure selection in SOC dynamics models. The research demonstrated that the relative weighting of these two data streams substantially influences estimated kinetic parameters and model outcomes.

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