The laser methane detector (LMD) has been proposed as a method to characterize enteric methane (CH4) emissions from animals in a natural environment. To validate LMD use, its CH4 outputs (LMD-CH4), were compared against CH4 measured with respiration chambers (chamber-CH4). The LMD was used to measure CH4 concentration (µL/L) in the exhaled air of 24 lactating ewes and 72 finishing steers. In ewes, LMD was used on 1 d for each ewe, for 2-min periods at 5 hourly observation periods (P1 to P5, respectively) after feeding. In steers fed either low- or high-concentrate diets, LMD was used once daily for a 4-min period for 3 d. The week after LMD-CH4 measurement, ewes or steers entered respiration chambers to quantify daily CH4 output (g/d). The LMD outputs consisted of periodic events of high CH4 concentrations superimposed on a background of oscillating lower CH4 concentrations. The high CH4 events were attributed to eructation and the lower background CH4 to respiration. After fitting a double normal distribution to the data set, a threshold of 99% of probability of the lower distribution was used to separate respiration from eructation events. The correlation between mean LMD-CH4 and chamber-CH4 was not high, and only improved correlations were observed after data were separated in 2 levels. In ewes, a model with LMD and DMI (adjusted R(2) = 0.92) improved the relationship between DMI and chamber-CH4 alone (adjusted R(2) = 0.79) and between LMD and chamber-CH4 alone (adjusted R(2) = 0.86). In both experiments, chamber-CH4 was best explained by models with length of eructation events (time) and maximum values of CH4 concentration during respiration events (µL/L; P < 0.01). Correlation between methods differed between observation periods, indicating the best results of the LMD were observed from 3 to 5 h after feeding. Given the short time and ease of use of LMD, there is potential for its commercial application and field-based studies. Although good indicators of quantity of CH4 were obtained with respiration and eructation CH4, the method needed to separate the data into high and low levels of CH4 was not simple to apply in practice. Further assessment of the LMD should be performed in relation to animal feeding behavior and physiology to validate assumptions of eructation and respiration levels, and other sources of variation should be tested (i.e., micrometeorology) to better investigate its potential application for CH4 testing in outdoor conditions.

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The prediction of methane outputs from ruminant livestock data at farm, national, and global scales is a vital part of greenhouse gas calculations. The objectives of this work were to quantify the effect of physiological stage (lactating or nonlactating) on predicting methane (CH4) outputs and to illustrate the potential improvement for a beef farming system of using more specific mathematical models to predict CH4 from cattle at different physiological stages and fed different diet types. A meta-analysis was performed on 211 treatment means from 38 studies where CH4, intake, animal, and feed characteristics had been recorded. Additional information such as type of enterprise, diet type, physiological stage, CH4 measurement technique, intake restriction, and CH4 reduction treatment application from these studies were used as classificatory factors. A series of equations for different physiological stages and diet types based on DMI or GE intake explained 96% of the variation in observed CH4 outputs (P<0.001). Resulting models were validated with an independent dataset of 172 treatment means from 20 studies. To illustrate the scale of improvement on predicted CH4 outputs from the current whole-farm prediction approach (Intergovernmental Panel on Climate Change [IPCC]), equations developed in the present study (NewEqs) were compared with the IPCC equation {CH4 (g/d)=[(GEI×Ym)×1,000]/55.65}, in which GEI is GE intake and Ym is the CH4 emission factor, in calculating CH4 outputs from 4 diverse beef systems. Observed BW and BW change data from cows with calves at side grazing either hill or lowland grassland, cows and overwintering calves and finishing steers fed contrasting diets were used to predict energy requirements, intake, and CH4 outputs. Compared with using this IPCC equation, NewEqs predicted up to 26% lower CH4 on average from individual lactating grazing cows. At the herd level, differences between equation estimates from 10 to 17% were observed in total annual accumulated CH4 when applied to the 4 diverse beef production systems. Overall, despite the small number of animals used it was demonstrated that there is a biological impact of using more specific CH4 prediction equations. Based on this approach, farm and national carbon budgets will be more accurate, contributing to reduced uncertainty in assessing mitigation options at farm and national level.