Methane (CH4) is the second-most important greenhouse gas in terms of its total contribution to global warming, and animal agriculture accounts for a significant share of its emissions. This article discusses the specific challenges of measuring CH4 emissions from systems of animal production, and the solutions that micrometeorological methods can offer. The methods considered include mass-budget methods, eddy covariance, gradient methods, inverse-dispersion modelling (IDM), and tracer-ratio methods that rely on atmospheric transport. The actual CH4 sources of animal agriculture include the animals themselves, manure storage and treatment facilities, and pasture soils. For each of these, the specific measurement challenges are discussed, and the methods employed to overcome them are reviewed. Animals are moving point sources of CH4, and they are managed in grazing or feeding systems, where their density can vary greatly. All the above micrometeorological methods have been used in some configurations to quantify animal CH4 emissions, typically with relative accuracies approaching ±10 %. Further improvement on this is possible with carefully designed setups comparing two animal groups in parallel. Such experiments can provide validation of mitigation approaches that rely on management practices not easily applied in controlled chamber or small-plot conditions. For emissions from manure storage and treatment, the main challenge to micrometeorological methods is usually flow disturbance, which can be overcome with both IDM and mass-budget methods, and these methods are well-suited to quantify the effects of mitigation approaches. For soil emissions, the main challenge is resolution; overall, soils contribute little to the total CH4 emissions from animal agriculture. Micrometeorological methods have also been used to integrate emissions over whole farms and regions, with mobile platforms, to corroborate inventory calculations. A challenge for the future is to develop methods that can determine CH4 emissions from a farm’s animal herd reliably enough to underpin accounting systems.
