Repeatability and Reproducibility of Measures of Bovine Methane Emissions Recorded using a Laser Detector

Abstract

Methane (CH4) emissions represent a worldwide problem due to their direct involvement in atmospheric warming and climate change. Ruminants are among the major players in the global scenario of CH4 emissions, and CH4 emissions are a problem for feed efficiency since enteric CH4 is eructed to the detriment of milk and meat production. The collection of CH4 phenotypes at the population level is still hampered by costly and time-demanding techniques. In the present study, a laser methane detector was used to assess repeatability and reproducibility of CH4 phenotypes, including mean and aggregate of CH4 records, slope of the linear equation modelling the aggregate function, and mean and number of CH4 peak records. Five repeated measurements were performed in a commercial farm on three Simmental heifers, and the same protocol was repeated over a period of three days. Methane emission phenotypes expressed as parts per million per linear meter (ppm × m) were not normally distributed and, thus, they were log-transformed to reach normality. Repeatability and reproducibility were calculated as the relative standard deviation of five measurements within the same day and 15 measurements across three days, respectively. All phenotypes showed higher repeatability and reproducibility for log-transformed data compared with data expressed as ppm × m. The linear equation modelling the aggregate function highlighted a very high coefficient of determination (≥0.99), which suggests that daily CH4 emissions might be derived using this approach. The number of CH4 peaks resulted as particularly diverse across animals and therefore it is a potential candidate to discriminate between high and low emitting animals. Results of this study suggest that laser methane detector is a promising tool to measure bovine CH4 emissions in field conditions.

Measuring Livestock CH4 Emissions with the Laser Methane Detector: A Review

“…However, it is unclear whether this phenotype is comparable, in a physiological sense, to the one described above [35,38]. Niero et al [42] concluded that repeatability and reproducibility were better for the average of the peaks than for all CH 4 values. However, the same authors acknowledged that the average of the peaks may not be suitable to distinguish between high and low CH 4 emitters due to its lower variability [42].…”

“…LMD measurements were performed at different locations inside buildings: in RC, e.g., [18,36]; in free-stalls, e.g., [12,14]; in a tie-stall [17]; in the milking parlour (according to the author's experience); a weighing facility for sheep [15]; and in individual pens, e.g., [38,42]. A few studies have been conducted on pasture [31,32,43] or in other half or full outdoor locations [19].…”

“…These implausibly high values, e.g., above 2500 ppm × m (according to the author's experience ) or more, depending on the experimental conditions, may occur when the recorded value for "reflection" is below 100 or when the visible pointing laser is reflected from a polished metal surface (according to the author's experience). For this reason, Niero et al [42] deleted all values above three standard deviations from the overall mean. As with the exclusion of entire recordings, profiles can be visually inspected for isolated, very high peaks that are not part of the regularly occurring pattern of peaks but with the above limitations.…”

“…Within a profile there are many low (respiration, Figure 2) and few high CH 4 values (eructation, Figure 2), so the data do not follow a normal distribution. In some studies, therefore, all CH 4 values were log e -transformed (natural logarithm) to achieve a normal distribution and homogeneity of variances [42], or only the peak respiratory values ("minipeaks," see "Separation of Eructation and Respiration") were log e -transformed [15]. Bruder et al [33] found that when all values above the 95th (238 ppm) or 99th percentile (562 ppm) were deleted from their data set, a normal distribution could be achieved without logtransforming the data.…”

“…These peaks in the time series of CH 4 values can be identified using an automated, self-written computer script or Excel macro (Microsoft Corporation, Redmond, WA, USA; [38]). Another simple identification method was used by Niero et al [42]: They defined the last decile of the distribution of all CH 4 values as peak values. However, it is unclear whether this phenotype is comparable, in a physiological sense, to the one described above [35,38].…”