Uncertainty assessment of the breath methane concentration method to determine methane production of dairy cows

Abstract

The breath methane concentration method uses the methane concentrations in the cow's breath during feed bin visits as a proxy for the methane production rate. The objective of this study was to assess the uncertainty of a breath methane concentration method in a feeder and its capability to measure and rank cows' methane production. A range of controlled methane fluxes from a so-called artificial reference cow were dosed in a feed bin, and its exhaled air was sampled by a tube inside the feeder and analyzed. The artificial reference cow simulates the lungs, respiratory tract, and rumen of a cow and releases a variable methane flux to generate a concentration pattern in the exhaled breath that closely resembles a real cow's pattern. The strength of the relation between the controlled methane release rates of the artificial reference cow and the measured methane concentrations was analyzed by linear regression, using the coefficient of determination (R 2 ) and the residual standard error as performance indicators. The effect of error sources (source-sampling distance, air turbulence, and cow's head movement) on this relation was experimentally investigated, both under laboratory and barn conditions. From the laboratory to the dairy barn at the 30-cm sampling distance, the R 2 -value decreased from 0.97 to 0.37 and the residual standard error increased from 75 to 86 ppm as a result of barn air turbulence, the latter increasing to a theoretical 94 ppm if modeled variability due to cow's head movement was accounted for as well. In practice, the effect of these random errors can be compensated by sampling strategies including repeated measurements on each cow over time, thus increasing the distinctive power between cows. However, systematic errors that may disturb the relation between concentration and production rate, such as cow variation in air exhalation rate and air flow patterns around sampling locations that differ between barns, cannot be compensated by repeated measurements. As a result, the methane concentrations of breath air will vary between cows with the same methane production. We conclude that the capability of the breath concentration measurement method to adequately measure and rank methane production rates among cows is highly uncertain and requires further investigation into variation sources with a systematic nature.

Enteric methane emission from Jersey cows during the spring transition from indoor feeding to grazing

“…The study by van Engelen et al (2018) obtained records in the Lely A4 Astronaut (Lely, Maassluis, the Netherlands), which has a more open feeding bin than the Delaval AMS used in our study. One possibility is the dilution of gases due to differences in feed bin dimensions or in environmental factors, such as variable wind speed between installations (Wu et al, 2018).…”

Assessment of methane emission traits in ewes using a laser methane detector: genetic parameters and impact on lamb weaning performance

“…Methane traits as defined in our study reflect "pure" CH 4 emissions, whereas CH 4 predictions by Chagunda et al (2009b) depend on body trait or physiological characteristics. Hence, in quantitative genetic studies, and following a deterministic CH 4 prediction strategy, the estimated heritability does not fully reflect the individual CH 4 genetic background (Yin et al, 2015). In genome-wide association studies, Manzanilla-Pech et al (2016) found an overlap for 19 % of SNP markers being significantly associated with DMI, body weight and individual CH 4 production in dairy cattle.…”

“…In such a context, approaches based on feed supplements were unsuitable under grazing conditions (Baker, 1999). Predictions of CH 4 via deterministic modeling usually require a large amount of input data, e.g., DMI, dietary or milk components, which are difficult to record (Kabreab et al, 2006;Yin et al, 2015). Further indirect methods for CH 4 emission predictions based on the ruminal microbiome composition but associations between CH 4 production and microbiome characteristics were inconsistent (Shi et al, 2014;Ellison et al, 2017).…”

Letter to the Editor: A response to

“…In addition to effects of external conditions, sniffer methane measurements are affected by random errors such as those caused by differences in sampling distance and the cow's head movement (e.g., Wu et al, 2018). These are indeed factors that will increase variation in sniffer methane measurements.…”

“…For example, cows showing restless behavior during milking, which is partly due to genetic differences between cows, might have lower sniffer methane levels because of sampling distance. Knowledge of variation in sniffer methane measurements due to such systematic errors is limited and certainly requires further investigation, as was pointed out by Wu et al (2018). However, even if genetic differences in sniffer methane partly reflect differences in other traits, this does not disqualify the use of sniffer methane measurements for selective breeding.…”