Summary
This paper addresses a critical challenge in soil greenhouse gas flux measurement: the decision whether to apply nonlinear regression models to static chamber data, which can introduce bias if measurement artefacts are misinterpreted as true nonlinearity. The authors present kappa.max, a reproducible decision procedure that improves accuracy and precision by dynamically determining when nonlinear fitting is justified. The accompanying R package enables researchers to simulate, visualise and optimise their specific measurement systems, reducing arbitrary uncertainty in flux estimates.
UK applicability
The kappa.max method is directly applicable to UK soil science and agricultural research using static chamber approaches for GHG monitoring. UK policy on agricultural emissions monitoring (particularly under CAP and farm carbon accounting schemes) could benefit from standardised, reproducible flux calculation methods to improve the reliability of national GHG inventories.
Key measures
Bias and uncertainty in N₂O flux estimates; performance comparison between linear, nonlinear (Hutchinson-Mosier regression) and kappa.max flux calculation methods
Outcomes reported
The study developed and validated the kappa.max method, a dynamic flux calculation scheme that improves the trade-off between bias and uncertainty when deciding between linear and nonlinear models for static chamber N₂O flux estimates. The method was implemented as an R software package with simulation, visualisation and optimisation tools.
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