We compared linear regression with a diffusion‐based model for N 2 O flux estimation using non‐steady‐state chamber gas concentrations from a long‐term study of N cycling in a managed grass pasture on sandy soil in southern Texas. Of 2224 chamber deployments, 449 met criteria established for using the diffusion‐based model, which yielded flux estimates that averaged 54% larger than linear regression ( n = 3). Although they represented only about 20% of all chamber deployments, this group included most of the data with greatest influence on the magnitude and dynamics of total N 2 O exchange at our site; e.g., of the 263 fluxes >10 g N ha −1 d −1 , 192 (or 73%) were included. Apparently, application of a linear model to nonlinear chamber concentration data represents a potentially serious source of measurement bias that may influence not only summary statistics for the experiment, but also larger scale budgets based partially or wholly on those data.
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Anthony et al. (1995) studied this question.