Advances in Methane Emission Estimation in Livestock: A Review of Data Collection Methods, Model Development and the Role of AI Technologies

This review examines the significant role of methane emissions in the livestock industry, with a focus on cattle and their substantial impact on climate change. It highlights the importance of accurate measurement and management techniques for methane, a potent greenhouse gas accounting for 14-16% of global emissions. The study evaluates both conventional and AI-driven methods for detecting methane emissions from livestock, particularly emphasizing cattle contributions, and the need for region-specific formulas. Sections cover livestock methane emissions, the potential of AI technology, data collection issues, methane's significance in carbon credit schemes, and current research and innovation. The review emphasizes the critical role of accurate measurement and estimation methods for effective climate change mitigation and reducing methane emissions from livestock operations. Overall, it provides a comprehensive overview of methane emissions in the livestock industry by synthesizing existing research and literature, aiming to improve knowledge and methods for mitigating climate change. Livestock-generated methane, especially from cattle, is highlighted as a crucial factor in climate change, and the review underscores the importance of integrating precise measurement and estimation techniques for effective mitigation.

Approaches for predicting dairy cattle methane emissions: from traditional methods to machine learning

Measuring dairy cattle methane (CH4) emissions using traditional recording technologies is complicated and expensive. Prediction models, which estimate CH4 emissions based on proxy information, provide an accessible alternative. This review covers the different modeling approaches taken in the prediction of dairy cattle CH4 emissions and highlights their individual strengths and limitations. Following the guidelines set out by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA); Scopus, EBSCO, Web of Science, PubMed and PubAg were each queried for papers with titles that contained search terms related to a population of "Bovine," exposure of "Statistical Analysis or Machine Learning," and outcome of "Methane Emissions". The search was executed in December 2022 with no publication date range set. Eligible papers were those that investigated the prediction of CH4 emissions in dairy cattle via statistical or machine learning (ML) methods and were available in English. 299 papers were returned from the initial search, 55 of which, were eligible for inclusion in the discussion. Data from the 55 papers was synthesized by the CH4 emission prediction approach explored, including mechanistic modeling, empirical modeling, and machine learning. Mechanistic models were found to be highly accurate, yet they require difficult-to-obtain input data, which, if imprecise, can produce misleading results. Empirical models remain more versatile by comparison, yet suffer greatly when applied outside of their original developmental range. The prediction of CH4 emissions on commercial dairy farms can utilize any approach, however, the traits they use must be procurable in a commercial farm setting. Milk fatty acids (MFA) appear to be the most popular commercially accessible trait under investigation, however, MFA-based models have produced ambivalent results and should be consolidated before robust accuracies can be achieved. ML models provide a novel methodology for the prediction of dairy cattle CH4 emissions through a diverse range of advanced algorithms, and can facilitate the combination of heterogenous data types via hybridization or stacking techniques. In addition to this, they also offer the ability to improve dataset complexity through imputation strategies. These opportunities allow ML models to address the limitations faced by traditional prediction approaches, as well as enhance prediction on commercial farms.

Genetic parameters for repeatedly recorded enteric methane concentrations of dairy cows

Animal breeding techniques offer potential to reduce enteric emissions of ruminants to lower the environmental impact of dairy farming. The aim of this study was to estimate the heritability and repeatability of methane (CH₄) concentrations, using the largest data set from long-term repeatedly recorded CH₄ on cows to date, and to evaluate (1) the accuracy of breeding values for different CH₄ traits, including using visits or weekly means, and (2) recording strategies (with varying numbers of records and recorded daughters per sire). The data comprised of long-term recording of CH₄ and carbon dioxide (CO₂), from 1,746 Holstein Friesian cows, on 14 commercial dairy farms throughout the Netherlands. Emissions were recorded in 10- to 35-s intervals, between 64 and 436 d, depending on farms. From each robot visit, CH₄ and CO₂ concentrations were summarized into various traits, averaged per visit and per week: mean, median, mean log, and mean CH₄/CO₂ ratio. Genetic parameters were estimated with animal repeatability models, using a restricted maximum likelihood procedure, and a relationship matrix based on genotypes and pedigree. The heritability was equal for mean and median CH₄ per visit (0.13) but lower for logCH₄ and CH₄/CO₂ (0.07 and 0.01, respectively). Phenotypic and genetic correlations were high (≥0.78) between the CH₄ traits, apart from the genetic correlations with the CH₄/CO₂ trait, which were negative. To achieve a minimum reliability of 50% for the estimated breeding value of a bull, 25 records on mean CH₄, measured on 10 different daughters, were sufficient. Although the heritability and repeatability were higher for weekly (0.32 and 0.68, respectively) than for visit mean CH₄ (0.13 and 0.30, respectively), the reliabilities of estimated breeding values from visit or weekly means were equal; thus, we found no advantage in averaging records to weekly means for genetic evaluations.

Detection of Methane Eructation Peaks in Dairy Cows at a Robotic Milking Station Using Signal Processing

Simple Summary

The objective of this study was to investigate the use of signal processing to detect eructation peaks in methane (CH₄) released by dairy cows during robotic milking using three gas analysers. This study showed that signal processing can be used to detect CH₄ eructations and extract spot measurements from individual cows whilst being milked. There was a reasonable correlation between the gas analysers studied. Measurement of eructations using a signal processing approach can provide a repeatable and accurate measurement of enteric CH₄ emissions from cows with different gas analysers.

Abstract

The aim of this study was to investigate the use of signal processing to detect eructation peaks in CH₄ released by cows during robotic milking, and to compare recordings from three gas analysers (Guardian SP and NG, and IRMAX) differing in volume of air sampled and response time. To allow comparison of gas analysers using the signal processing approach, CH₄ in air (parts per million) was measured by each analyser at the same time and continuously every second from the feed bin of a robotic milking station. Peak analysis software was used to extract maximum CH₄ amplitude (ppm) from the concentration signal during each milking. A total of 5512 CH₄ spot measurements were recorded from 65 cows during three consecutive sampling periods. Data were analysed with a linear mixed model including analyser × period, parity, and days in milk as fixed effects, and cow ID as a random effect. In period one, air sampling volume and recorded CH₄ concentration were the same for all analysers. In periods two and three, air sampling volume was increased for IRMAX, resulting in higher CH₄ concentrations recorded by IRMAX and lower concentrations recorded by Guardian SP (p < 0.001), particularly in period three, but no change in average concentrations measured by Guardian NG across periods. Measurements by Guardian SP and IRMAX had the highest correlation; Guardian SP and NG produced similar repeatability and detected more variation among cows compared with IRMAX. The findings show that signal processing can provide a reliable and accurate means to detect CH₄ eructations from animals when using different gas analysers.

A Portable Device for Methane Measurement Using a Low-Cost Semiconductor Sensor: Development, Calibration and Environmental Applications

Methane is a major greenhouse gas and a precursor of tropospheric ozone, and most of its sources are linked to anthropogenic activities. The sources of methane are well known and its monitoring generally involves the use of expensive gas analyzers with high operating costs. Many studies have investigated the use of low-cost gas sensors as an alternative for measuring methane concentrations; however, it is still an area that needs further development to ensure reliable measurements. In this work a low-cost platform for measuring methane within a low concentration range was developed and used in two distinct environments to continuously assess and improve its performance. The methane sensor was the Figaro TGS2600, a metal oxide semiconductor (MOS) based on tin dioxide (SnO₂). In a first stage, the monitoring platform was applied in a small ruminant barn after undergoing a multi-point calibration. In a second stage, the system was used in a wastewater treatment plant together with a multi-gas analyzer (Gasera One Pulse). The calibration of low-cost sensor was based on the relation of the readings of the two devices. Temperature and relative humidity were also measured to perform corrections to minimize the effects of these variables on the sensor signal and an active ventilation system was used to improve the performance of the sensor. The system proved to be able to measure low methane concentrations following reliable spatial and temporal patterns in both places. A very similar behavior between both measuring systems was also well noticeable at WWTP. In general, the low-cost system presented good performance under several environmental conditions, showing itself to be a good alternative, at least as a screening monitoring system.

Feeding Forage Mixtures of Ryegrass (Lolium spp.) with Clover (Trifolium spp.) Supplemented with Local Feed Diets to Reduce Enteric Methane Emission Efficiency in Small-Scale Dairy Systems: A Simulated Study

Simple Summary

The present study simulated the effects of different dairy cow diets based on local feeding strategies on enteric methane (CH₄) emissions and surpluses of crude protein (CP) in small-scale dairy systems (SSDS). Our study evaluated five scenarios of supplementation (S): without supplementation (control diet), meaning no supplements were provided, only pasture (S1); pasture supplemented with 4.5 kg dry matter (DM)/cow/day of commercial concentrate (CC) (S2); supplemented with 200 g DM/kg per milk produced of CC (S3); supplemented with ground maize grains and wet distiller brewery grains (S4); and S4 plus maize silage (S5). In addition, two pasture managements (cut-and-carry versus grazing) and two varieties of legumes (red clover vs. white clover) were considered. The results suggest that methane emissions and large nitrogen surpluses in the diet are affected by the type of supplementation given to cows, in addition to the management and chemical composition of the pastures offered. In SSDS, it is possible to formulate diets with local inputs to reduce excess nutrients and dependence on external inputs, increasing feed efficiency and reducing costs (excess of CP in the diet) and CH₄ emissions.

Abstract

In cattle, greenhouse gas (GHG) emissions and nutrient balance are influenced by factors such as diet composition, intake, and digestibility. This study evaluated CH₄ emissions and surpluses of crude protein, using five simulated scenarios of supplementation in small-scale dairy systems (SSDS). In addition, two pasture managements (cut-and-carry versus grazing) and two varieties of legumes (red clover vs. white clover) were considered. The diets were tested considering similar milk yield and chemical composition; CH₄ emission was estimated using Tier-2 methodology from the Intergovernmental Panel on Climate Change (IPCC), and the data were analyzed in a completely randomized 5 × 2 × 2 factorial design. Differences (p < 0.05) were found in predicted CH₄ emissions per kg of milk produced (g kg⁻¹ FCM 3.5%). The lowest predicted CH₄ emissions were found for S3 and S4 as well as for pastures containing white clover. Lower dietary surpluses of CP (p < 0.05) were observed for the control diet (1320 g CP/d), followed by S5 (1793 g CP/d), compared with S2 (2175 g CP/d), as well as in cut-and-carry management with red clover. A significant correlation (p < 0.001) was observed between dry matter intake and CH₄ emissions (g⁻¹ and per kg of milk produced). It is concluded that the environmental impact of formulating diets from local inputs (S3 and S4) can be reduced by making them more efficient in terms of methane kg⁻¹ of milk in SSDS.

Biogas Plant Exploitation in a Middle-Sized Dairy Farm in Poland: Energetic and Economic Aspects

Although cow manure is a valuable natural fertilizer, it is also a source of extreme greenhouse gas emissions, mainly methane. For this reason, this study aims to determine the impact of investments in a biogas plant on the energy and economic aspects of the operation of a dairy farm. A farm with a breeding size of 600 livestock units (LSU) was adopted for the analysis. In order to reach the paper’s aim, the analysis of two different scenarios of dairy farm functioning (conventional–only milk production, and modern–with biogas plant exploitation) was conducted. The analysis showed that the investment in biogas plant operations at a dairy farm and in using cow manure as one of the main substrates is a more profitable scenario compared to traditional dairy farming. Taking into account the actual Polish subsidies for electricity produced by small biogas plants, the scenario with a functioning biogas plant with a capacity of 500 kW brings €332,000/a more profit compared to the conventional scenario, even when taking into account additional costs, including the purchase of straw to ensure a continuous operation of the installation. Besides, in the traditional scenario, building a biogas plant allows for an almost complete reduction of greenhouse gas emissions during manure storage.

The Effect of Dairy Cattle Housing Systems on the Concentrations and Emissions of Gaseous Mixtures in Barns Determined by Fourier-Transform Infrared Spectroscopy

The aim of this study was to determine the concentrations and emissions of greenhouse and odorous gases in different types of dairy cattle housing systems with the use of Fourier-transform infrared (FTIR) spectroscopy. The study was performed in autumn and winter in four types of dairy cattle barns with different process and technical systems (free-stall, deep litter – FS-DL; free-stall, sub-floor manure storage – FS-SFM; free-stall, litter in stalls – FS-LS; tie-stall, litter in stalls – TS-LS) in northern Poland. Analyses of gaseous mixtures in barn air were conducted by infrared spectrometry with the multi-component Gasmet DX4030 analyzer. A total of 200 measurement spectra were acquired and subjected to qualitative and quantitative analyses with the Calcmet Professional program with a library of reference spectra for 200 chemical compounds. The results of the study indicate that housing systems and the technological solutions applied in barns exert a considerable influence on the production of greenhouse and odorous gases. Free-stall housing with slatted floors and sub-floor manure storage appears to be the optimal solution for reducing the animals’ exposure to the presence of the analyzed chemical compounds in air, improving animal welfare and minimizing GHG emissions to the environment (considering the optimal ventilation rate). It should be noted that the concentrations of other potentially harmful compounds, for which the maximum safe levels have been specified, were also relatively low in the remaining systems, which points to the observance of high sanitary standards and the use of efficient ventilation systems in the evaluated barns.

Genome-Wide Association Studies for Methane Production in Dairy Cattle

Genomic selection has been proposed for the mitigation of methane (CH₄) emissions by cattle because there is considerable variability in CH₄ emissions between individuals fed on the same diet. The genome-wide association study (GWAS) represents an important tool for the detection of candidate genes, haplotypes or single nucleotide polymorphisms (SNP) markers related to characteristics of economic interest. The present study included information for 280 cows in three dairy production systems in Mexico: 1) Dual Purpose (n = 100), 2) Specialized Tropical Dairy (n = 76), 3) Familiar Production System (n = 104). Concentrations of CH₄ in a breath of individual cows at the time of milking (MEIm) were estimated through a system of infrared sensors. After quality control analyses, 21,958 SNPs were included. Associations of markers were made using a linear regression model, corrected with principal component analyses. In total, 46 SNPs were identified as significant for CH₄ production. Several SNPs associated with CH₄ production were found at regions previously described for quantitative trait loci of composition characteristics of meat, milk fatty acids and characteristics related to feed intake. It was concluded that the SNPs identified could be used in genomic selection programs in developing countries and combined with other datasets for global selection.

Comparison of Methods to Measure Methane for Use in Genetic Evaluation of Dairy Cattle

Partners in Expert Working Group WG2 of the COST Action METHAGENE have used several methods for measuring methane output by individual dairy cattle under various environmental conditions. Methods included respiration chambers, the sulphur hexafluoride (SF6) tracer technique, breath sampling during milking or feeding, the GreenFeed system, and the laser methane detector. The aim of the current study was to review and compare the suitability of methods for large-scale measurements of methane output by individual animals, which may be combined with other databases for genetic evaluations. Accuracy, precision and correlation between methods were assessed. Accuracy and precision are important, but data from different sources can be weighted or adjusted when combined if they are suitably correlated with the 'true' value. All methods showed high correlations with respiration chambers. Comparisons among alternative methods generally had lower correlations than comparisons with respiration chambers, despite higher numbers of animals and in most cases simultaneous repeated measures per cow per method. Lower correlations could be due to increased variability and imprecision of alternative methods, or maybe different aspects of methane emission are captured using different methods. Results confirm that there is sufficient correlation between methods for measurements from all methods to be combined for international genetic studies and provide a much-needed framework for comparing genetic correlations between methods should these become available.

Estimating Herd-Scale Methane Emissions from Cattle in a Feedlot Using Eddy Covariance Measurements and the Carbon Dioxide Tracer Method

Measurements of methane (CH) emissions from ruminants could provide invaluable data to reduce uncertainties in the global CH budget and to evaluate mitigation strategies to lower greenhouse gas emissions. The main objective of this study was to evaluate a new CO tracer (COT) approach that combined CH and CO atmospheric concentrations with eddy covariance (EC) CO flux measurements to estimate CH emissions from cattle in a feedlot. A closed-path EC system was used to measure CH and CO fluxes from a feedlot in Kansas. The EC flux measurements were scaled from landscape to animal scale using footprint analyses. Emissions of CH from the cattle were also estimated using the COT approach and measured CO and CH concentration, and scaled EC CO fluxes. The CH and CO concentration ratios showed a distinct diel trend with greater values during the daytime. Average monthly CH emission estimates using the COT approach ranged from 72 to 127 g animal d, which was consistent with the values reported in other studies that had similar animal characteristics. The COT method CH emission estimates showed good agreement with scaled CH EC fluxes (slope = 0.9 and = 0.8) for cold and dry months. However, the agreement between the two techniques was significantly reduced (slope = 1.5 and = 0.6) during wet and warm months. On average, the COT method CH emission estimates were 3% greater than the EC CH emissions. Overall, our results suggest that the COT method can be used to estimate enteric feedlot CH emissions.

Technical note: Interchangeability and comparison of methane measurements in dairy cows with 2 noninvasive infrared systems

This study aimed to compare measurements of methane (CH₄) and carbon dioxide (CO₂) concentrations in the breath of dairy cows kept in commercial conditions using the Fourier-transform infrared spectroscopy (FTIR) and nondispersive infrared spectroscopy (NDIR) methods. The measurement systems were installed in an automated milking system. Measurements were carried out for 5 d using both systems during milkings. The measurements were averaged per milking, giving 467 observations of CH₄ and CO₂ concentrations of 44 Holstein Friesian cows. The Pearson correlation between observations from the 2 systems was 0.86 for CH₄, 0.84 for CO₂, and 0.88 for their ratio. The repeatability of FTIR (0.53 for CH₄, 0.57 for CO₂, and 0.28 for their ratio) was somewhat higher than that of NDIR (0.57 for CH₄, 0.47 for CO₂, and 0.25 for their ratio). The coefficient of individual agreement was 0.98 for CH₄, 0.89 for CO₂, and 0.89 for their ratio; the concordance correlation coefficient was 0.48 for both gases and 0.24 for their ratio. We showed that FTIR and NDIR give similar results in commercial farm conditions. They can therefore be used interchangeably to generate a larger data set, which could then be further used for genetic evaluation.

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

Organic dairy cows in Denmark are often kept indoors during the winter and outside at least part time in the summer. Consequently, their diet changes by the season. We hypothesized that grazing might affect enteric CH₄ emissions due to changes in the nutrition, maintenance, and activity of the cows, and they might differentially respond to these factors. This study assessed the repeatability of enteric CH₄ emission measurements for Jersey cattle in a commercial organic dairy herd in Denmark. It also evaluated the effects of a gradual transition from indoor winter feeding to outdoor spring grazing. Further, it assessed the individual-level correlations between measurements during the consecutive feeding periods (phenotype × environment, P × E) as neither pedigrees nor genotypes were available to estimate a genotype by environment effect. Ninety-six mixed-parity lactating Jersey cows were monitored for 30 d before grazing and for 24 d while grazing. The cows spent 8 to 11 h grazing each day and had free access to an in-barn automatic milking system (AMS). For each visit to the AMS, milk yield was recorded and logged along with date and time. Monitoring equipment installed in the AMS feed bins continuously measured enteric CH₄ and CO₂ concentrations (ppm) using a noninvasive "sniffer" method. Raw enteric CH₄ and CO₂ concentrations and their ratio (CH₄:CO₂) were derived from average concentrations measured during milking and per day for each cow. We used mixed models equations to estimate variance components and adjust for the fixed and random effects influencing the analyzed gas concentrations. Univariate models were used to precorrect the gas measurements for diurnal variation and to estimate the direct effect of grazing on the analyzed concentrations. A bivariate model was used to assess the correlation between the 2 periods (in-barn vs. grazing) for each gas concentration. Grazing had a weak P × E interaction for daily average CH₄ and CO₂ gas concentrations. Bivariate repeatability estimates for average CH₄ and CO₂ concentrations and CH₄:CO₂ were 0.77 to 0.78, 0.73 to 0.80, and 0.26, respectively. Repeatability for CH₄:CO₂ was low (0.26) but indicated some between-animal variation. In conclusion, grazing does not create significant shifts compared with indoor feeding in how animals rank for average CH₄ and CO₂ concentrations and CH₄:CO₂. We found no evidence that separate evaluation is needed to quantify enteric CH₄ and CO₂ emissions from Jersey cows during in-barn and grazing periods.

Heritability of methane emissions from dairy cows over a lactation measured on commercial farms

Methane emission is currently an important trait in studies on ruminants due to its environmental and economic impact. Recent studies were based on short-time measurements on individual cows. As methane emission is a longitudinal trait, it is important to investigate its changes over a full lactation. In this study, we aimed to estimate the heritability of the estimated methane emissions from dairy cows using Fourier-transform infrared spectroscopy during milking in an automated milking system by implementing the random regression method. The methane measurements were taken on 485 Polish Holstein-Friesian cows at 2 commercial farms located in western Poland. The overall daily estimated methane emission was 279 g/d. Genetic variance fluctuated over the course of lactation around the average level of 1,509 (g/d), with the highest level, 1,866 (g/d), at the end of the lactation. The permanent environment variance values started at 2,865 (g/d) and then dropped to around 846 (g/d) at 100 d in milk (DIM) to reach the level of 2,444 (g/d) at the end of lactation. The residual variance was estimated at 2,620 (g/d). The average repeatability was 0.25. The heritability level fluctuated over the course of lactation, starting at 0.23 (SE 0.12) and then increasing to its maximum value of 0.3 (SE 0.08) at 212 DIM and ending at the level of 0.27 (SE 0.12). Average heritability was 0.27 (average SE 0.09). We have shown that estimated methane emission is a heritable trait and that the heritability level changes over the course of lactation. The observed changes and low genetic correlations between distant DIM suggest that it may be important to consider the period in which methane phenotypes are collected.

Effect of Feeding System on Enteric Methane Emissions from Individual Dairy Cows on Commercial Farms

This study investigated the effects of feeding system on diurnal enteric methane (CH4) emissions from individual cows on commercial farms. Data were obtained from 830 cows across 12 farms, and data collated included production records, CH4 measurements (in the breath of cows using CH4 analysers at robotic milking stations for at least seven days) and diet composition. Cows received either a partial mixed ration (PMR) or a PMR with grazing. A linear mixed model was used to describe variation in CH4 emissions per individual cow and assess the effect of feeding system. Methane emissions followed a consistent diurnal pattern across both feeding systems, with emissions lowest between 05:00 and 08:59, and with a peak concentration between 17:00 and 20:59. No overall difference in emissions was found between feeding systems studied; however, differences were found in the diurnal pattern of CH4 emissions between feeding systems. The response in emissions to increasing dry matter intake was higher for cows fed PMR with grazing. This study showed that repeated spot measurements of CH4 emissions whilst cows are milked can be used to assess the effects of feeding system and potentially benchmark farms on level of emissions.

The impact of divergent breed types and diets on methane emissions, rumen characteristics and performance of finishing beef cattle

This study was undertaken to further develop our understanding of the links between breed, diet and the rumen microbial community and determine their effect on production characteristics and methane (CH4) emissions from beef cattle. The experiment was of a 2×2 factorial design, comprising two breeds (crossbred Charolais (CHX); purebred Luing (LU)) and two diets (concentrate-straw or silage-based). In total, 80 steers were used and balanced for sire within each breed, farm of origin and BW across diets. The diets (fed as total mixed rations) consisted of (g/kg dry matter (DM)) forage to concentrate ratios of either 500 : 500 (Mixed) or 79 : 921 (Concentrate). Steers were adapted to the diets over a 4-week period and performance and feed efficiency were then measured over a 56-day test period. Directly after the 56-day test, CH4 and carbon dioxide (CO2) emissions were measured (six steers/week) over a 13-week period. Compared with LU steers, CHX steers had greater average daily gain (ADG; P<0.05) and significantly (P<0.001) lower residual feed intake. Crossbred Charolais steers had superior conformation and fatness scores (P<0.001) than LU steers. Although steers consumed, on a DM basis, more Concentrate than Mixed diet (P<0.01), there were no differences between diets in either ADG or feed efficiency during the 56-day test. At slaughter, however, Concentrate-fed steers were heavier (P<0.05) and had greater carcass weights than Mixed-fed steers (P<0.001). Breed of steer did not influence CH4 production, but it was substantially lower when the Concentrate rather than Mixed diet was fed (P<0.001). Rumen fluid from Concentrate-fed steers contained greater proportions of propionic acid (P<0.001) and lower proportions of acetic acid (P<0.001), fewer archaea (P<0.01) and protozoa (P=0.09), but more Clostridium Cluster XIVa (P<0.01) and Bacteroides plus Prevotella (P<0.001) than Mixed-fed steers. When the CH4 to CO2 molar ratio was considered as a proxy method for CH4 production (g/kg DM intake), only weak relationships were found within diets. In conclusion, although feeding Concentrate and Mixed diets produced substantial differences in CH4 emissions and rumen characteristics, differences in performance were influenced more markedly by breed.

Assessing individual differences in enteric methane emission among beef heifers using the GreenFeed Emission Monitoring system: effect of the length of testing period on precision

The GreenFeed Emission Monitoring system was used to measure individual greenhouse gas (GHG) emissions while recording feed intake of beef heifers. That technique provides spot-measures of methane (CH4) and carbon dioxide (CO2) fluxes at each visit to the GreenFeed feeder. A sampling variance is attached at each spot-measure due to circadian variation in GHG emission. Averaging spot-measures is required for reducing that sampling error when evaluating GHG emissions of individual cattle. The objective of the present study was to evaluate the length of test period and number of spot-measures for precisely assessing differences among beef heifers. The within-individual (σ2r) and across-individual (σ2i) variances of GHG-flux measures were estimated for 124 Charolais beef heifers fed a roughage diet during an 8-week test period, following 3–4 weeks of adaptation. High repeatability coefficients (>0.77) were obtained with 4-week test averages and ~100 spot-measures for CH4 and CO2 fluxes. Equivalent repeatability was obtained for dry matter intake (DMI). Lower repeatability (<0.7) was obtained for combined traits, namely, CH4/CO2, CH4/DMI and CO2/DMI. Higher precision would have been obtained if the first 2 weeks were not used but considered as further adaptation. In that case, about 50 spot-measures recorded during a 2-week test would be sufficient for a precise individual measure of CH4 emissions. For genetic evaluation, test duration of 5 weeks may be recommended for the simultaneous recording of CH4 emission and feed intake.

The GreenFeed system for measurement of enteric methane emission from cattle

Methane measurements from cattle would benefit from an improved capability to measure a larger number of animals, with a lower requirement for specialist technical knowledge, and minimal human interference. The GreenFeed (GF) system (C-Lock Inc., Rapid City, SD, USA) estimates daily methane production (DMP, g/day) by measuring gas concentrations and airflow over 3–7 min from cattle when they visit a GF unit. Although few data are collected per animal per day, over many days of GF visitation estimates of DMP can be established. Published GF estimates of DMP are in agreement with DMP measured by respiration chambers, but there are inconsistencies in comparisons based on estimates using the sulfur hexafluoride tracer method. Circadian patterns of methane emission from cattle suggest spot-sampling of emissions by GF should be distributed over 24 h, or weighted to avoid bias associated with clustering of GF visits at specific times. Up to half of cattle grazing temperate pastures choose not to use GF on a daily basis, so consideration must be given to the number of animals and duration of sampling as well as the proportion and representation of animals using GF for estimating DMP, especially for ranking individuals. All systems for determining DMP from animals constrain the data in some way, and the suitability of the GF system will be affected by the experimental objectives and design. For example, compared with the respiration chamber and sulfur hexafluoride tracer techniques, it takes more time and animals to undertake a treatment comparison of DMP using GF due to higher within-day and within-animal variance, especially if some avoid GF or do not visit each day.