Abstract

In vivo methane (CH4) measurements are required to refine livestock CH4 emission factors and pursue global mitigation strategies. The aim of this review was to summarise methodology used in publications between 1995 and 2018 regarding variations in technical procedures reported within studies using respiration chambers (RC), the sulphur hexafluoride tracer (SF6) and spot-sampling with the GreenFeed (GF) head chamber for determining CH4 emissions from ruminants. A total of 397 peer reviewed studies were surveyed. Information was summarized to provide percentage distribution and ranges where appropriate. Measurements were primarily with respiration chambers (55 %), and SF6 (38 %) and GF was used in 7 % of studies. Evaluation of each study demonstrated variation in reported technical procedures, affecting confidence, data quality and reports would benefit from standardisation. With studies using RC, measurement duration ranged from <1 to 8 days, and gas recovery through the whole system was reported only in 32 % of studies and ranged from 85 to 107%. With SF6, number of measurement days were variable (2 to>30 days) and 28 % of studies sampled only for 2−4 days. The calibration of SF6 release rate from permeation tubes was less than 6 weeks in 14 % of studies and gas sampling rate into evacuated collection canisters was reported in only 29 % of studies. For GF, most studies reported feed allowance settings (70–83 %) but less than half reported actual voluntary visit data by animals to the GF unit, which can be very different from the maximum visits defined by the user. For example, number of visits per animal per day (reported in 40 % of the studies) were half of the possible visits (3.2 vs 6.1 visits/day). Also, the actual visit duration for ‘valid’ data acquisition was only reported in 30 % of studies and the lower limit value (2 min) was shorter than recommended minimum of 3 min. Consistency, standardisation and reporting important variables associated with each of the three techniques would improve defensibility of data and increase confidence in global values derived by integrating data from all sources.

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