Estimating daily methane production in individual cattle with irregular feed intake patterns from short-term methane emission measurements

A meta-analysis of probiotic interventions to mitigate ruminal methane emissions in cattle: implications for sustainable livestock farming

In recent years, the significant impact of ruminants on methane emissions has garnered international attention. While dietary strategies have been implemented to solve this issue, probiotics gained the attention of researchers due to their sustainability. However, it is challenging to ascertain their effectiveness as an extensive range of strains and doses have been reported in the literature. Hence, the objective of this experiment was to perform a meta-analysis of probiotic interventions aiming to reduce ruminal methane emissions from cattle. From 362 articles retrieved from scientific databases, 85 articles were assessed independently by two reviewers, and 20 articles representing 49 comparisons were found eligible for meta-analysis. In each study, data such as mean, SD, and sample sizes of both the control and probiotic intervention groups were extracted. The outcomes of interest were methane emission, methane yield, and methane intensity. For the meta-analysis, effect sizes were pooled using a fixed effect or a random effect model depending on the heterogeneity. Afterward, sensitivity analyses were conducted to confirm the robustness of the findings. Overall pooled standardized mean differences (SMDs) with their confidence intervals (CIs) did not detect significant differences in methane emission (SMD = -0.04; 95% CI = -0.18-0.11; P = 0.632), methane yield (SMD = -0.08; 95% CI = -0.24-0.07; P = 0.291), and methane intensity (SMD = -0.22; 95% CI = -0.50-0.07; P = 0.129) between cattle supplemented with probiotics and the control group. However, subgroup analyses revealed that multiple-strain bacterial probiotics (SMD = -0.36; 95% CI = -0.62 to -0.11; P = 0.005), specifically the combination of bacteria involved in reductive acetogenesis and propionate production (SMD = -0.71; 95% CI = -1.04 to -0.36; P = 0.001), emerged as better interventions. Likewise, crossbreeds (SMD = -0.48; 95% CI = -0.78 to -0.18; P = 0.001) exhibited a more favorable response to the treatments. Furthermore, meta-regression demonstrated that longer periods of supplementation led to significant reductions in methane emissions (P = 0.001), yield (P = 0.032), and intensity (P = 0.012) effect sizes. Overall, the results of the current study suggest that cattle responses to probiotic interventions are highly dependent on the probiotic category. Therefore, extended trials performed with probiotics containing multiple bacterial strains are showing the most promising results. Ideally, further trials focusing on the use of probiotics to reduce ruminal methane in cattle should be conducted to complete the available literature.

The effect of feeding and visiting behavior on methane and hydrogen emissions of dairy cattle measured with the GreenFeed system under different dietary conditions

The objectives were to investigate the effect of feeding and visiting behavior of dairy cattle on CH4 and H2 production measured with voluntary visits to the GreenFeed system (GF) and to determine whether these effects depended on basal diet (BD) and 3-nitrooxypropanol (3-NOP) supplementation. The experiment involved 64 lactating dairy cattle (146 ± 45 d in milk at the start of trial; mean ± SD) in 2 overlapping crossover trials, each consisting of 2 measurement periods. Cows within block were randomly allocated to 1 of 3 types of BD: a grass silage-based diet consisting of 30% concentrates and 70% grass silage (DM basis), a grass silage- and corn silage-mixed diet consisting of 30% concentrates, 42% grass silage, and 28% corn silage (DM basis), or a corn silage-based diet consisting of 30% concentrates, 14% grass silage, and 56% corn silage (DM basis). Each type of BD was subsequently supplemented with 0 and 60 mg 3-NOP/kg DM in one crossover, or 0 and 80 mg 3-NOP/kg DM in the other crossover. Diets were provided in feed bins which automatically recorded feed intake and feeding behavior, with additional concentrate fed in the GF. All visits to the GF that resulted in a spot measurement of both CH4 and H2 emission were analyzed in relation to feeding behavior (e.g., meal size and time interval to preceding meal) as well as GF visiting behavior (e.g., duration of visit). Feeding and GF visiting behavior was related to CH4 and H2 production measured with the GF, in particular the meal size before a GF measurement and the time interval between a GF measurement and the preceding meal. Relationships between gas production and both feeding and GF visiting behavior were affected both by type of BD and 3-NOP supplementation. With an increase of the time interval between a GF measurement and the preceding meal, CH4 production decreased with 0 mg 3-NOP/kg DM but increased with 60 and 80 mg 3-NOP/kg DM, whereas type of BD did not affect these relationships. In contrast, CH4 production increased with 0 mg 3-NOP/kg DM but decreased with 60 and 80 mg 3-NOP/kg DM upon an increase in the size of the meal preceding a GF measurement. With an increase of the time interval between a GF measurement and the preceding meal, or with a decrease of the size of the meal preceding a GF measurement, H2 production decreased for all treatments, although the effect was generally somewhat stronger for 60 and 80 mg 3-NOP/kg DM than for 0 mg 3-NOP/kg DM. Hence, the timing of GF measurements next to feeding and GF visiting behavior are essential when assessing the effect of dietary treatment on the production of CH4 and H2 in a setting where a spot sampling device such as a GF is used and where the measurements depend on voluntary visits from the cows.

Determination of gas flux and animal performance test duration of growing cattle in confined conditions

Data from three experiments was analyzed to determine the number of visits and days to assess gas flux (CH4, CO2, and O2), dry matter intake (DMI), and average daily gain (ADG) from growing animals under confined conditions. In experiment 1, 213 animals (461 ± 91 kg initial body weight [BW]) were fed a backgrounding diet and evaluated for 60 d. In experiment 2, 169 steers (488 ± 37 kg initial BW) were fed a finishing diet and assessed for 70 d. In experiment 3, 64 steers (514 ± 42 kg initial BW) were fed a finishing diet and evaluated for 80 d. In each experiment, animals were placed in one pen with one Greenfeed and five SmartFeeds to collect gas flux and feed intake simultaneously. Gas flux was analyzed using data from 161 animals from the three experiments with 100 visits for 2 or more min or 3 or more min. Also, metabolic heat production (MHP) was estimated using the individual gas flux. Daily DMI was calculated as the daily feed intake corrected by the dry matter concentration. ADG was computed as the slope of the regression of the shrunk BW (96% BW) throughout each of the experimental periods. The mean gas flux and MHP were estimated for increasing or decreasing 5-visit intervals starting with the first or the last 5 visits and increasing or decreasing until the full 100-visit dataset was utilized, respectively. Intervals of DMI were estimated for increasing or decreasing 5-d intervals starting with the first or the last 5 d and increasing or decreasing until the end of the experimental period, respectively. Intervals of ADG were estimated for increasing or decreasing measurement period intervals until the end of the experimental period, respectively. Pearson and Spearman correlations were computed between the maximum visits or days and each shortened visit or day interval. The minimum number of visits and days was determined when correlations with the total visits were greater than 0.95. The results indicated that the minimum number of visits needed to quantify CO2, O2, and MHP accurately was 40, while CH4 was 60. A visitation length of 2 min or more or 3 min or more did not modify the gas flux determination. Thus, based on the average daily visitation in these experiments, gas flux data could be collected for 25 d. Additionally, the required days to determine DMI was 30, while ADG could not be assessed in a shorter than 60-d period.

Enteric methane emission estimates for the Zimbabwean Sanga cattle breeds of Tuli and Mashona

The effectiveness of methane mitigation in ruminant livestock production systems depends on the accuracy of estimating methane emission factors and providing accurate emission inventories. Following the Paris Climate agreement, it is recommended that countries adopt the Tier-2 approach for estimating enteric methane emissions from ruminants instead of the Tier-1 approach currently used by most countries. This study sought to provide base line enteric methane emission estimates for the Tuli and Mashona Sanga cattle breeds in Zimbabwe using the IPCC Tier-2 model. Using animal characterization data collected from 412 cattle from Grasslands Research Institute and 406 cattle from Makoholi Research Institute, net energy requirements were estimated. From this and the estimate for digestibility, gross energy intake and dry matter intake were estimated. Gross energy intakes and the estimated methane conversion factor were used to estimate enteric methane emissions. Mean emission factors for Tuli were 45.1, 56, 28.5, 28.4 and 20.6 kg CH₄/head/year for cows, bulls, heifers, steers and calves, respectively. For Mashona, they were 47.8, 51.9, 29, 29.1 and 20.7 kgCH₄/head/year for cows, bulls, heifers, steers and calves, respectively. Generally, estimated Tier-2 emission factors were significantly different from the IPCC Tier-1 default emission factors. This study concluded that enteric methane emission factors estimated using the IPCC Tier-2 model offer insights into the controversial use of the default IPCC Tier-1 emission factors.

Phenotypic relationship and repeatability of methane emissions and performance traits in beef cattle using a GreenFeed system

Rumen methanogenesis results in the loss of 6% to 10% of gross energy intake in cattle and globally is the single most significant source of anthropogenic methane (CH4) emissions. The purpose of this study was to analyze greenhouse gas traits recorded in a commercial feedlot unit to gain an understanding into the relationships between greenhouse gas traits and production traits. Methane and carbon dioxide (CO2) data recorded via multiple GreenFeed Emission Monitoring (GEM), systems as well as feed intake, live weight, ultrasound scanning data, and slaughter data were available on 1,099 animals destined for beef production, of which 648 were steers, 361 were heifers, and 90 were bulls. Phenotypic relationships between GEM emission measurements with feed intake, weight traits, muscle ultrasound data, and carcass traits were estimated. Utilization of GEM systems, daily patterns of methane output, and repeatability of GEM system measurements across averaging periods were also assessed. Methane concentrations varied with visit number, duration, and time of day of visit to the GEM system. Mean CH4 and CO2 varied between sex, with mean CH4 of 256.1 g/day ± 64.23 for steers, 234.7 g/day ± 59.46 for heifers, and 156.9 g/day ± 55.98 for young bulls. A 10-d average period of GEM system measurements were required for steers and heifers to achieve a minimum repeatability of 0.60; however, higher levels of repeatability were observed in animals that attended the GEM system more frequently. In contrast, CO2 emissions reached repeatability estimates >0.6 for steers and heifers in all averaging periods greater than 2-d, suggesting that cattle have a moderately consistent CO2 emission pattern across time periods. Animals with heavier bodyweights were observed to have higher levels of CH4 (correlation = 0.30) and CO2 production (correlation = 0.61), and when assessing direct methane, higher levels of dry matter intake were associated with higher methane output (correlation = 0.31). Results suggest that reducing CH4 can have a negative impact on growth and body composition of cattle. Methane ratio traits, such as methane yield and intensity were also evaluated, and while easy to understand and compare across populations, ratio traits are undesirable in animal breeding, due to the unpredictable level of response. Methane adjusted for dry matter intake and liveweight (Residual CH4) should be considered as an alternative emission trait when selecting for reduced emissions within breeding goals.

Global Warming and Dairy Cattle: How to Control and Reduce Methane Emission

Agriculture produces greenhouse gases. Methane is a result of manure degradation and microbial fermentation in the rumen. Reduced CH4 emissions will slow climate change and reduce greenhouse gas concentrations. This review compiled studies to evaluate the best ways to decrease methane emissions. Longer rumination times reduce methane emissions and milk methane. Other studies have not found this. Increasing propionate and reducing acetate and butyrate in the rumen can reduce hydrogen equivalents that would otherwise be transferred to methanogenesis. Diet can reduce methane emissions. Grain lowers rumen pH, increases propionate production, and decreases CH4 yield. Methane generation per unit of energy-corrected milk yield reduces with a higher-energy diet. Bioactive bromoform discovered in the red seaweed Asparagopsis taxiformis reduces livestock intestinal methane output by inhibiting its production. Essential oils, tannins, saponins, and flavonoids are anti-methanogenic. While it is true that plant extracts can assist in reducing methane emissions, it is crucial to remember to source and produce plants in a sustainable manner. Minimal lipid supplementation can reduce methane output by 20%, increasing energy density and animal productivity. Selecting low- CH4 cows may lower GHG emissions. These findings can lead to additional research to completely understand the impacts of methanogenesis suppression on rumen fermentation and post-absorptive metabolism, which could improve animal productivity and efficiency.

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.

Repeatability and ranking of long-term enteric methane emissions measurement on dairy cows across diets and time using GreenFeed system in farm-conditions

The aims of this work were to study on dairy farm conditions: i) the repeatability of long-term enteric CH₄ emissions measurement from lactating dairy cows using GreenFeed (GF); ii) the ranking of dairy cows according to their CH₄ emissions across diets. Forty-five Holstein lactating dairy cows were randomly assigned to 3 equivalent groups at the beginning of their lactation. The experiment was composed of 3 successive periods: i) pre-experimental period (weeks 1 to 5) in which all cows received a common diet; ii) a dietary treatment transition period (weeks 6 to 10); and iii) an experimental period (weeks 11 to 26) in which each group was fed a different diet. Experimental diets were formulated to generate more or less CH₄ production: i) a diet based on ryegrass silage and concentrates, low in starch and lipid, designed to induce high CH₄ emissions (CH4+); ii) a diet based on maize silage and concentrates, rich in starch, designed to induce intermediate CH₄ emissions (CH4int); iii) a diet based on maize silage and concentrates, rich in starch and lipid, designed to induce low CH₄ emissions (CH4-). Gas emissions were individually measured using GF systems. Repeatability of gas emissions, dry matter intake (DMI) and dairy performances measurements was calculated from data averaged over 1, 2, 4, and 8 weeks for each animal. Hierarchical cluster analysis was performed to rank individual animals according to their CH₄ emissions. No significant differences were observed for daily CH₄ emissions (g/day) among diets, because of lower DMI of CH4+ cows. When CH₄ emissions were referred to units of DMI or milk, the differences among diets emerged as significant and persistent over the observed period of lactation. Repeatability values of gas emissions measurements were higher than 0.7 averaged over 8 weeks of measurement, but still higher than 0.6 for CH₄ g/day, CO₂ g/day, CH₄ g/kg milk, and CH₄/CO₂ even averaging only 2 weeks of measurement. The repeatability of CH₄ emissions measurement was systematically lower than those of DMI or dairy performance parameters, like milk and FPCM yield, irrespective of the averaged measurement period. The dairy cow ranking was not stable over time between all individuals or within any of the diets. In our experimental conditions, the GF performance in the long term can be considered reliable in differentiating dairy herds by their CH₄ emissions according to diets with different methanogenic potential, but did not allow the ranking of individual dairy cows within a same diet. Our data highlight the importance of phenotyping animals across environment in which they will be expected to perform.

Methane and carbon dioxide emissions from yearling beef heifers and mature cows classified for residual feed intake under drylot conditions

This study quantified methane (CH4) and carbon dioxide (CO2) production from beef heifers and cows classified for residual feed intake adjusted for off-test backfat thickness (RFIfat) and reared in drylot during cold winter temperatures. Individual performance, daily feed intake, and RFIfat were obtained for 1068 crossbred and purebred yearling heifers (eight trials) as well as 176 crossbred mature cows (six trials) during the winters of 2015–2017 at two locations. A portion of these heifers (147 high RFIfat; 167 low RFIfat) and cows (69 high RFIfat; 70 low RFIfat) was monitored for enteric CH4 and CO2 emissions using the GreenFeed Emissions Monitoring (GEM) system (C-Lock Inc., Rapid City, SD, USA). Low RFIfat cattle consumed less feed [heifers, 7.80 vs. 8.48 kg dry matter (DM) d−1; cows, 11.64 vs. 13.16 kg DM d−1] and emitted less daily CH4 (2.5% for heifers; 3.7% for cows) and CO2 (1.4% for heifers; 3.4% for cows) compared with high RFIfat cattle. However, low RFIfat heifers and cows had higher CH4 (6.2% for heifers; 9.9% for cows) and CO2 yield (7.3% for heifers; 9.8% for cows) per kilogram DM intake compared with their high RFIfat pen mates. The GEM system performed at air temperatures between +20 and −30 °C. Feed intake of heifers and mature cows was differently affected by ambient temperature reduction between +20 and −15 °C and similarly increased their feed intake at temperatures below −15 °C. In conclusion, low RFIfat animals emit less daily enteric CH4 and CO2, due mainly to lower feed consumption at equal body weight, gain, and fatness.

A Review of Enteric Methane Emission Measurement Techniques in Ruminants

To identify relationships between animal, dietary and management factors and the resulting methane (CH₄) emissions, and to identify potential mitigation strategies for CH₄ production, it is vital to develop reliable and accurate CH₄ measurement techniques. This review outlines various methods for measuring enteric CH₄ emissions from ruminants such as respiration chambers (RC), sulphur hexafluoride (SF₆) tracer, GreenFeed, sniffer method, ventilated hood, facemask, laser CH₄ detector and portable accumulation chamber. The advantages and disadvantages of these techniques are discussed. In general, RC, SF₆ and ventilated hood are capable of 24 h continuous measurements for each individual animal, providing accurate reference methods used for research and inventory purposes. However, they require high labor input, animal training and are time consuming. In contrast, short-term measurement techniques (i.e., GreenFeed, sniffer method, facemask, laser CH₄ detector and portable accumulation chamber) contain additional variations in timing and frequency of measurements obtained relative to the 24 h feeding cycle. However, they are suitable for large-scale measurements under commercial conditions due to their simplicity and high throughput. Successful use of these techniques relies on optimal matching between the objectives of the studies and the mechanism of each method with consideration of animal behavior and welfare. This review can provide useful information in selecting suitable techniques for CH₄ emission measurement in ruminants.

Bayesian modeling reveals host genetics associated with rumen microbiota jointly influence methane emission in dairy cows

Reducing methane emissions from livestock production is of great importance for the sustainable management of the Earth’s environment. Rumen microbiota play an important role in producing biogenic methane. However, knowledge of how host genetics influences variation in ruminal microbiota and their joint effects on methane emission is limited. We analyzed data from 750 dairy cows, using a Bayesian model to simultaneously assess the impact of host genetics and microbiota on host methane emission. We estimated that host genetics and microbiota explained 24% and 7%, respectively, of variation in host methane levels. In this Bayesian model, one bacterial genus explained up to 1.6% of the total microbiota variance. Further analysis was performed by a mixed linear model to estimate variance explained by host genomics in abundances of microbial genera and operational taxonomic units (OTU). Highest estimates were observed for a bacterial OTU with 33%, for an archaeal OTU with 26%, and for a microbial genus with 41% heritability. However, after multiple testing correction for the number of genera and OTUs modeled, none of the effects remained significant. We also used a mixed linear model to test effects of individual host genetic markers on microbial genera and OTUs. In this analysis, genetic markers inside host genes ABS4 and DNAJC10 were found associated with microbiota composition. We show that a Bayesian model can be utilized to model complex structure and relationship between microbiota simultaneously and their interaction with host genetics on methane emission. The host genome explains a significant fraction of between-individual variation in microbial abundance. Individual microbial taxonomic groups each only explain a small amount of variation in methane emissions. The identification of genes and genetic markers suggests that it is possible to design strategies for breeding cows with desired microbiota composition associated with phenotypes.

Replacement of Soybean Meal With Soybean Cake Reduces Methane Emissions in Dairy Cows and an Assessment of a Face-Mask Technique for Methane Measurement

The objective of this study was to (a) evaluate the effect of replacing soybean meal (SBM) with soybean cake (SBC) on feeding behavior, rumen fermentation, milk production, nutrient digestibility and CH₄ emissions and (b) investigate whether a face-mask technique could be used to predict daily methane (CH₄) emissions in dairy cattle. The experiment was conducted as a completely randomized design, with 32 crossbred Holstein × Gyr cows (days in milk (DIM): 112 ± 25.1) randomly assigned to the following treatments (n = 8/group) for 75 days: (1) 0% SBC, (2) 6% SBC, (3) 14% SBC, and (4) 23% SBC, in place of SBM on a dry matter (DM) basis. Across the final 4 weeks of the study, CH₄ production was estimated using the proposed face-mask technique subsequent to a respiration chamber measurement for an evaluation of treatment efficacy and face-mask accuracy. There was no effect of SBM replacement by SBC on intake, feeding or drinking behavior (P > 0.21). Total VFA concentration, the individual proportions of VFA and blood metabolites were not altered (P > 0.17) by SBC, however there was a tendency for decreased (P = 0.08) lactate and plasma urea nitrogen (P = 0.07) concentration associated with SBC addition. Fat-corrected milk yield (FCM_4%) and composition was not affected (P > 0.27) by SBC; however, there was a tendency for decreased total milk solids (P = 0.07) and milk fat (P = 0.08) associated with 23% SBC treatment. There was no treatment × technique interaction (P > 0.05) effect on gas measurements. A maximum reduction (P = 0.01) in CH₄ yield (g/kg DM) and intensity (g/kg milk) of 11 and 20%, respectively, was observed for the 14% SBC inclusion. Compared to the week of mask measurements, chambers decreased (P = 0.01) intake (kg/d, %BW) and increased (P = 0.05) FCM_4%. The face-mask method over estimated O₂ consumption by 5%. The face-mask method accurately predicted daily CH₄ emissions when compared to the chamber at the same time-point. However, there was a linear bias of CH₄ outputs so further evaluation of the calculation of total CH₄ from a spot measurement is required.

Comparison of 3 methods for estimating enteric methane and carbon dioxide emission in nonlactating cows

Among techniques for estimating enteric methane (CH4) emission by ruminants, open-circuit respiration chambers (OC), the use of a gas tracer (SF6), and the GreenFeed (GF) device are the most commonly used. In this study, we compared these techniques in 8 dry cows receiving a diet made of 70% hay and 30% concentrates given in limited and constant amounts, in a 15-wk experiment. Two periods in free stalls for SF6 and GF and in chambers for OC were used; in addition, SF6 was determined in chambers for 1 period. Methane emission (g/d) and CH4 yield (g/kg DMI) were higher (P < 0.0001) for OC than for SF6 and GF (367, 310, and 319 g/d for OC, SF6, and GF, respectively). The difference between OC and GF was related to a difference in post-prandial rate of gas emission. The between-animal coefficient of variation of CH4 emission was higher for SF6 than for OC and GF (20.8, 13.5, and 12.0% on average, respectively). Correlation coefficients between OC and SF6 were high and significant for CH4 emission and CH4 yield (r = 0.782 and r = 0.717, respectively; P < 0.05), but not significant between OC and GF, or between SF6 and GF. Correlation coefficients were highly significant for SF6 determined either in free stalls or in chambers (r = 0.908 and 0.903 for CH4 in g/d and g/kg DMI, respectively; P < 0.01). Carbon dioxide (CO2) emission and CO2 yield were similar for GF and OC (10,003 and 9,887 g/d, 752 and 746 g/kg DMI, respectively); CO2 data obtained with SF6 were lower (7,718 g/d and 606 g/kg DMI; P < 0.0001), but this technique is not relevant for CO2 emission determination. Correlation coefficients between OC and GF were not significant for CO2 emission and CO2 yield. This set of results shows that differences between methods are minor for average values, but that individual correlations may limit their interchangeability for determining gas emissions of individual animals. This study also shows the reliability of GF on-farm determination of CH4 and CO2 emissions for groups of animals.

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.

Measuring the respiratory gas exchange by grazing cattle using an automated, open-circuit gas quantification system

Ruminants are a source of enteric CH₄, which has been identified as an anthropogenic greenhouse gas that contributes to climate change. With interest in developing technologies to decrease enteric CH₄ emissions, systems are currently being developed to measure CH₄ emissions by cattle. An issue with grazing cattle is the ability to measure CH₄ emissions in open-air environments. A scientific instrument for this task is an automated, open-circuit gas quantification system (GQS; C-Lock, Inc., Rapid City, SD). The GQS is a head chamber that grazing cattle occasionally visit (3 to 8 min/visit; 3 to 6 visits/d), and while the animal consumes a small portion of bait (0.5 to 1.0 kg/visit), the GQS captures the animal's breath cloud by exhausting air through the GQS. The breath cloud is then analyzed for CH₄, CO₂, and O₂ concentrations. Data are hourly uploaded to a server where it is processed using algorithms to determine total daily fluxes. Several factors affect emission estimates generated by the GQS including the animal's visitation rate, length of sampling period, and airflow through the system. The location of the GQS is an important factor in determining the cattle's willingness to visit. Further, cattle need to be trained to use the GQS, which normally requires 4 to 8 wk. Several researchers have shown that 30 or more visits are required to obtain high-quality estimates of gas fluxes. Once cattle are trained to use the GQS, the bait delivery rate has little effect on the animal's willingness to use the system. Airflow through the GQS is an important factor, but as long as airflow is maintained above 26 L/s the breath-cloud capture seems nearly complete. There is great concern regarding circadian variation in the instantaneous production rates of CH₄ because the GQS normally only spot-samples 2 to 4 times/d. Preliminary analysis has shown that variation in the instantaneous production rates of CH₄ do not vary as greatly with grazing cattle compared with meal-fed cattle. It seems that increasing the visitation length decreases variation in estimated emissions, but there is a diminishing return to increasing visitation length. The GQS is a useful tool for researching the nutrition and emissions of grazing cattle, but great care must be taken to obtain the best quality data possible for use in this high-impact research.

Enteric methane emissions from low- and high-residual feed intake beef heifers measured using GreenFeed and respiration chamber techniques

The objectives of this study were to evaluate the relationship between residual feed intake (RFI; g/d) and enteric methane (CH) production (g/kg DM) and to compare CH and carbon dioxide (CO) emissions measured using respiration chambers (RC) and the GreenFeed emission monitoring (GEM) system (C-Lock Inc., Rapid City, SD). A total of 98 crossbred replacement heifers were group housed in 2 pens and fed barley silage ad libitum and their individual feed intakes were recorded by 16 automated feeding bunks (GrowSafe, Airdrie, AB, Canada) for a period of 72 d to determine their phenotypic RFI. Heifers were ranked on the basis of phenotypic RFI, and 16 heifers (8 with low RFI and 8 with high RFI) were randomly selected for enteric CH and CO emissions measurement. Enteric CH and CO emissions of individual animals were measured over two 25-d periods using RC (2 d/period) and GEM systems (all days when not in chambers). During gas measurements metabolic BW tended to be greater ( ≤ 0.09) for high-RFI heifers but ADG tended ( = 0.09) to be greater for low-RFI heifers. As expected, high-RFI heifers consumed 6.9% more feed ( = 0.03) compared to their more efficient counterparts (7.1 vs. 6.6 kg DM/d). Average CH emissions were 202 and 222 g/d ( = 0.02) with the GEM system and 156 and 164 g/d ( = 0.40) with RC for the low- and high-RFI heifers, respectively. When adjusted for feed intake, CH yield (g/kg DMI) was similar for high- and low-RFI heifers (GEM: 27.7 and 28.5, = 0.25; RC: 26.5 and 26.5, = 0.99). However, CH yield differed between the 2 measurement techniques only for the high-RFI group ( = 0.01). Estimates of CO yield (g/kg DMI) also differed between the 2 techniques ( ≤ 0.03). Our study found that high- and low-efficiency cattle produce similar CH yield but different daily CH emissions. The 2 measurement techniques differ in estimating CH and CO emissions, partially because of differences in conditions (lower feed intakes of cattle while in chambers, fewer days measured in chambers) during measurement. We conclude that when intake of animals is known, the GEM system offers a robust and accurate means of estimating CH emissions from animals under field conditions.

Rapid Communication: Ranking dairy cows for methane emissions measured using respiration chamber or GreenFeed techniques during early, peak, and late lactation

Our objective was to compare the ranking of dairy cows according to their methane (CH) emissions as measured by a respiration chamber (RC) technique and the GreenFeed (GF) technique during 3 periods in second lactation. Two-day CH measurements in a RC performed in wk 3, 14, and 42 of lactation were flanked by GF measurements for 20 (period 1 [P1]), 35 (period 2 [P2]), and 35 (period 3 [P3]) days, respectively, before and after RC measurement. This gave the total duration of CH measurements using the GF system of 40, 70, and 70 d for P1, P2, and P3, respectively. Mean daily CH production (g/d) of the 8 dairy cows was 346, 439, and 430 using the RC technique and 338, 378, and 416 using the GF system during P1, P2, and P3, respectively. Average daily CH production determined by the GF technique was 2.4, 13.8, and 3.2% lower in P1, P2, and P3, respectively. Methane normalized to DMI continuously increased from P1 to P3 when measured in a RC, whereas it was lowest during P2 when measured by the GF method. Ranking of the cows according to CH production, CH/energy-corrected milk yield (ECM; CH/ECM), and CH/DMI differed between periods no matter which method was used. Cluster analysis including all 3 periods, however, identified the same cows with the highest and lowest CH production determined either by the RC technique or the GF system. In conclusion, multiple CH measurements at different stages of lactation are necessary for reliable discrimination of highest and lowest CH emitting cows and the GF system may be used to discriminate the extremes.

Optimizing test procedures for estimating daily methane and carbon dioxide emissions in cattle using short-term breath measures

Respiration chambers are considered the reference method for quantifying the daily CH production rate (MPR) and CO production rate (CPR) of cattle; however, they are expensive, labor intensive, cannot be used in the production environment, and can be used to assess only a limited number of animals. Alternative methods are now available, including those that provide multiple short-term measures of CH and CO, such as the GreenFeed Emission Monitoring (GEM) system. This study was conducted to provide information for optimizing test procedures for estimating MPR and CPR of cattle from multiple short-term CH and CO records. Data on 495 Angus steers on a 70-d ad libitum feedlot diet with 46,657 CH and CO records and on 121 Angus heifers on a 15-d ad libitum roughage diet with 7,927 CH and CO records were used. Mean (SD) age and BW were 554 d (SD 92) and 506 kg (SD 73), respectively, for the steers and 372 d (SD 28) and 348 kg (SD 37), respectively, for the heifers. The 2 data sets were analyzed separately but using the same procedures to examine the reduction in variance as more records are added and to evaluate the level of precision with 2 vs. 3 min as the minimum GEM visit duration for a valid record. The moving averages procedure as well as the repeated measures procedure were used to calculate variances for both CH and CO, starting with 5 records and progressively increasing to a maximum of 80 records. For both CH and CO and in both data sets, there was a sharp reduction in the variances obtained by both procedures as more records were added. However, there was no substantial reduction in the variance after 30 records had been added. Inclusion of records with a minimum of 2-min GEM visit duration resulted in reduction in precision relative to a minimum of 3 min, as indicated by significantly ( < 0.05) more heterogeneous variances for all cases except CH4 in steers. In addition, more records were required to achieve the same level of precision relative to data with minimum GEM visit durations of 3 min. For example, in the steers, 72% reduction in initial variance was achieved with 30 records for both CH and CO when minimum GEM visit duration was 3 min, relative to 45 records when data with a minimum visit duration of 2 min were included. It is concluded from this study that when using records of multiple short-term breath measures of CH or CO for the computation of an animal's MPR or CPR, a minimum of 30 records, each record obtained from a minimum GEM visit duration of 3 min, are required.