Optimizing chamber methods for measuring nitrous oxide emissions from plot-based agricultural experiments

Nitrous oxide emissions (N2O) from agricultural land are spatially and temporally variable. Most emission measurements are made with small (MUCH LESS-THAN 1 m(2) area) static chambers. We used N2O chamber data collected from multiple field experiments across different geo-climatic zones in the UK and from a range of nitrogen treatments to quantify uncertainties associated with flux measurements. Data were analysed to assess the spatial variability of fluxes, the degree of linearity of headspace N2O accumulation and the robustness of using ambient air N2O concentrations as a surrogate for sampling immediately after closure (T-0). Data showed differences of up to more than 50-fold between the maximum and minimum N2O flux from five chambers within one plot on a single sampling occasion, and that reliability of flux measurements increased with greater numbers of chambers. In more than 90% of the 1970 cases where linearity of headspace N2O accumulation was measured (with four or more sampling points), linear accumulation was observed; however, where non-linear accumulation was seen this could result in a 26% under-estimate of the flux. Statistical analysis demonstrated that the use of ambient air as a surrogate for T-0 headspace samples did not result in any consistent bias in calculated fluxes. Spatial variability has the potential to result in erroneous flux estimates if not taken into account, and generally introduces a far larger uncertainty into the calculated flux (commonly orders of magnitude more) than any uncertainties introduced through reduced headspace sampling or assumption of linearity of headspace accumulation. Hence, when deploying finite resources, maximizing chamber numbers should be given priority over maximizing the number of headspace samplings per enclosure period.