347 Enteric Methane: Current Measurement and Assessment Techniques

Abstract

Abstract Enteric methane (ECH4) emissions from ruminants can be measured directly or indirectly using various techniques and recent reviews have discussed advantages and disadvantages of these techniques. The GLOBAL NETWORK project (GN; an international consortium of animal scientists) examined techniques for measuring ECH4, including respiration chambers, the sulfur hexafluoride tracer (SF6) technique, and techniques based on short-term measurements of gas concentrations in samples of exhaled air. The latter category includes automated head chambers (i.e., the GreenFeed system; GF), use of carbon dioxide as a marker, and (handheld) laser methane detection. The conclusion from this analysis was that “there is no ‘one size fits all’ method for measuring ECH4 emission by individual animals” and appropriate and frequent calibrations and recovery tests are necessary with all methods. The team also concluded that the need for screening large numbers of animals (for example, for genomic studies), does not justify the use of measurement methods that are inaccurate. Timing of sampling/data collection is critical for the spot-sampling techniques, such as GF. It is a well-established fact that ECH4 emission is closely related to animal’s dry matter intake (DMI) and feeding patterns. Therefore, data collection using GF has to be sufficiently long and frequent, during both day and night hours, to fully represent the diurnal patter of ECH4 emission. The in vitro gas production and analysis technique can be used to screen feed additives or other ECH4 mitigation treatments, but data must be always confirmed/supported by animal (preferably long-term) studies. ECH4 emission can be also predicted based on dietary or animal variables. Large databases developed by the GN project have confirmed that DMI is driving ECH4, but other factors, such as dietary neutral detergent fiber, milk yield and composition (dairy cows), or dietary forage inclusion and animal’s body weight (beef cattle) can improve prediction accuracy.