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 field data from the Zürich Organic Fertilization Experiment to test whether radiocarbon measurements could reduce uncertainty in soil organic carbon dynamic models. By comparing five nested model structures (ranging from two to three decomposing pools, with and without inert pools and substrate feedback mechanisms) and varying the relative weight of total SOC versus radiocarbon constraints, the authors demonstrated that radiocarbon data substantially influences parameter estimates and that the weighting of these two data streams is critical for model outcomes. The findings suggest that incorporating radiocarbon data alongside total carbon measurements can better constrain SOC models and reduce equifinality in parameter estimation.

UK applicability

The methodological framework is directly applicable to UK arable systems and long-term field experiments (such as those at Rothamsted Research), as temperate cropland SOC dynamics are similar. The approach could improve calibration of SOC models used for UK agricultural and climate policy, though site-specific validation would be needed.

Key measures

Soil organic carbon concentration; soil radiocarbon (¹⁴C) age measurements; kinetic decomposition parameters; model structure comparison; Bayesian parameter uncertainty quantification

Outcomes reported

The study examined soil organic carbon (SOC) dynamics and kinetic parameters in a long-term cropland experiment by utilizing SOC and radiocarbon time series across five different model structures. The research quantified how weighting total SOC and radiocarbon data differently in model calibration affected estimated kinetic parameters and model outcomes.

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