Verifying inventory predictions of animal methane emissions with meteorological measurements

The paper examines the strengths and weaknesses of a range of meteorological flux measurement techniques that might be used to verify predictions of greenhouse gas inventories. Recent research into emissions of methane (CH₄) produced by enteric fermentation in grazing cattle and sheep is used to illustrate various methodologies. Quantifying this important source presents special difficulties because the animals constitute moving, heterogeneously distributed, intermittent, point sources. There are two general approaches: one, from the bottom up, involves direct measurements of emissions from a known number of animals, and the other, from the top down, infers areal emissions of CH₄ from its atmospheric signature. A mass-balance method proved successful for bottom-up verification. It permits undisturbed grazing, has a simple theoretical basis and is appropriate for flux measurements on small plots and where there are scattered point sources. The top-down methodologies include conventional flux-gradient approaches and convective and nocturnal boundary-layer (CBL and NBL) budgeting schemes. Particular attention is given to CBL budget methods in both differential and integral form. All top-down methodologies require ideal weather conditions for their application, and they suffer from the scattered nature of the source, varying wind directions and low instrument resolution. As for mass-balance, flux-gradient micrometeorological measurements were in good agreement with inventory predictions of CH₄ production by livestock, but the standard errors associated with both methods were too large to permit detection of changes of a few per cent in emission rate, which might be important for inventory, regulatory or research purposes. Fluxes calculated by CBL and NBL methods were of the same order of magnitude as inventory predictions, but more improvement is needed before their use can be endorsed. Opportunities for improving the precision of both bottom-up and top-down methodologies are discussed.