Comparison of methane production, intensity, and yield throughout lactation in Holstein cows.

Genetic selection to reduce methane (CH4) emissions from dairy cows is an attractive means of reducing the impact of agricultural production on climate change. In this study, we investigated the feasibility of such an approach by characterizing the interactions between CH4 and several traits of interest in dairy cows. We measured CH4, dry matter intake (DMI), fat- and protein-corrected milk (FPCM), body weight (BW), and body condition score (BCS) from 107 first- and second-parity Holstein cows from December 2019 to November 2021. Methane emissions were measured using a GreenFeed device and expressed in terms of production (MeP, in g/d), yield (MeY, in g/kg DMI), and intensity (MeI, in g/kg FPCM). Because of the limited number of cows, only animal parameters were estimated. Both MeP and MeI were moderately repeatable (>0.45), whereas MeY presented low repeatability, especially in early lactation. Mid lactation was the most stable and representative period of CH4 emissions throughout lactation, with animal correlations above 0.9. The average animal correlations of MeP with DMI, FPCM, and BW were 0.62, 0.48, and 0.36, respectively. The MeI was negatively correlated with FCPM (<-0.5) and DMI (>-0.25), and positively correlated with BW and BCS. The MeY presented stable and weakly positive correlations with the 4 other traits throughout lactation, with the exception of slightly negative animal correlations with FPCM and DMI after the 35th week. The MeP, MeI, and MeY were positively correlated at all lactation stages and, assuming animal and genetic correlations do not strongly differ, selection on one trait should lead to improvements in all. Overall, selection for MeI is probably not optimal as its change would result more from CH4 dilution in increased milk yield than from real decrease in methane emission. Instead, MeY is related to rumen function and is only weakly associated with DMI, FPCM, BW, and BCS; it thus appears to be the most promising CH4 trait for selection, provided that this would not deteriorate feed efficiency and that a system of large-scale phenotyping is developed. The MeP is easier to measure and thus may represent an acceptable alternative, although care would need to be taken to avoid undesirable changes in FPCM and BW.

Heritability and genetic correlations between enteric methane production and concentration recorded by GreenFeed and sniffers on dairy cows.

To reduce methane (CH4) emissions of dairy cows by animal breeding, CH4 measurements have to be recorded on thousands of individual cows. Currently, several techniques are used to phenotype cows for CH4, differing in costs and applicability. However, there is uncertainty about the agreement between techniques. To judge the similarity and repeatability between measurements of different recording techniques, the repeatability, heritability, and genetic correlation are useful metrics. Therefore, our objective was to estimate (1) the repeatability and heritability for CH4 and carbon dioxide production recorded by GreenFeed (GF) and for CH4 and carbon dioxide concentration measured by cost-effective but less accurate sniffers, and (2) the genetic correlation between CH4 recorded with these 2 different on farm and high throughput techniques. Data were available from repeated measurements of CH4 production (grams/day) by GF units and of CH4 concentration (ppm) by sniffers, recorded on commercial dairy farms in the Netherlands. The final data comprised 24,284 GF daily means from 822 cows, 170,826 sniffer daily means from 1,800 cows, and 1,786 daily means from 75 cows by both GF and sniffer (in the same period). Additionally, CH4 records were averaged per week. For daily and weekly mean GF CH4 the heritabilities were 0.19 ± 0.02 and 0.33 ± 0.04, and for daily and weekly mean sniffer CH4 the heritabilities were similar and were 0.18 ± 0.01 and 0.32 ± 0.02, respectively. Phenotypic correlations between GF CH4 production and sniffer CH4 concentration were moderate (0.39 ± 0.03 for daily means and 0.37 ± 0.05 for weekly means). However, genetic correlations were high; 0.71 ± 0.13 for daily means and 0.76 ± 0.15 for weekly means. The high genetic correlation indicates that selection on low CH4 concentrations (ppm) recorded by the cost-effective sniffer method, will result in reduced CH4 production (grams/day) as recorded with GF.

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.

Methodological guidelines: Cow milk mid-infrared spectra to predict reference enteric methane data collected by an automated head-chamber system.

Various methodological protocols were tested on milk samples from cows fed diets affecting both methanogenesis and milk synthesis to identify the best approach for the prediction of GreenFeed system (GF) measured methane (CH4) emissions by milk mid-infrared (MIR) spectroscopy. The models developed were also tested on a data set from cows fed chemical inhibitors of CH4 emission [3-nitrooxypropanol (3NOP)] that just marginally affect milk composition. A total of 129 primiparous and multiparous Holstein cows fed diets with different methanogenic potential were considered. Individual milk yield (MY) and dry matter intake were recorded daily, whereas fat- and protein-corrected milk (FPCM) was recorded twice a week. The MIR spectra from 2 consecutive milkings were collected twice a week. Twenty CH4 spot measurements with GF were taken as the basic measurement unit (BMU) of CH4. The equations were built using partial least squares regression by splitting the database into calibration and validation data sets (excluding 3NOP samples). Models were developed for milk MIR spectra by milking and on day spectra obtained by averaging spectra from 2 consecutive milkings. Models based on day spectra were calibrated by using CH4 reference data for a measurement duration of 1, 2, 3, or 4 BMU. Models built from the average of the day spectra collected during the corresponding CH4 measurement periods were developed. Corrections of spectra by days in milk (DIM) and the inclusion of parity, MY, and FPCM as explanatory variables were tested as tools to improve model performance. Models built on day milk MIR spectra gave slightly better performances that those developed using spectra from a single milking. Long duration of CH4 measurement by GF performed better than short duration: the coefficient of determination of validation (R2V) for CH4 emissions expressed in grams per day were 0.60 vs. 0.52 for 4 and 1 BMU, respectively. When CH4 emissions were expressed as grams per kilogram of dry of matter intake, grams per kilogram of MY, or grams per kilogram of FPCM, performance with a long duration also improved. Coupling GF reference data with the average of milk MIR spectra collected throughout the corresponding CH4 measurement period gave better predictions than using day spectra (R2V = 0.70 vs. 0.60 for CH4 as g/d on 4 BMU). Correcting the day spectra by DIM improved R2V compared with the equivalent DIM-uncorrected models (R2V = 0.67 vs. 0.60 for CH4 as g/d on 4 BMU). Adding other phenotypic information as explanatory variables did not further improve the performance of models built on single day DIM-corrected spectra, whereas including MY (or FPCM) improved the performance of models built on the average of spectra (uncorrected by DIM) recorded during the CH4 measurement period (R2V = 0.73 vs. 0.70 for CH4 as g/d on 4 BMU). When validating the models on the 3NOP data set, predictions were poor without (R2V = 0.13 for CH4 as g/d on 1 BMU) or with (R2V = 0.31 for CH4 as g/d on 1 BMU) integration of 3NOP data in the models. Thus, specific models would be required for CH4 prediction when cows receive chemical inhibitors of CH4 emissions not affecting milk composition.

The use of an upgraded GreenFeed system and milk fatty acids to estimate energy balance in early-lactation cows.

Measurements of energy balance (EB) require the use of respiration chambers, which are quite expensive and laborious. The GreenFeed (GF) system (C-Lock Inc.) has been developed to offer a less expensive, user friendly alternative. In this study, we used the GF system to estimate the EB of cows in early lactation and compared it with EB predicted from energy requirements for dairy cows in the Finnish feeding standards. We also evaluated the association between milk fatty acids and the GF estimated EB. The cows were fed the same grass silage but supplemented with either cereal grain or fibrous by-product concentrate. Cows were followed from 1 to 18 wk of lactation, and measurements of energy metabolism variables were taken. Data were subjected to ANOVA using the mixed model procedure of SAS (SAS Institute Inc.). The repeatability estimates of the gaseous exchanges from the GF were moderate to high, presenting an opportunity to use it for indirect calorimetry in EB estimates. Energy metabolism variables were not different between cows fed different concentrates. However, cows fed the grain concentrate produced more methane (24.0 MJ/d or 62.9 kJ/MJ of gross energy) from increased digestibility than cows fed the by-product concentrate (21.3 MJ/d or 56.5 kJ/MJ of gross energy). Nitrogen metabolism was also not different between the diets. Milk long-chain fatty acids displayed an inverse time course with EB and de novo fatty acids. There was good concordance (0.85) between EB predicted using energy requirements derived from the Finnish feed table and EB estimated by the GF system. In conclusion, the GF can accurately estimate EB in early-lactating dairy cows. However, more data are needed to further validate the system for a wide range of dietary conditions.

Effects of 3-nitrooxypropanol and varying concentrate feed proportions in the ration on methane emission, rumen fermentation and performance of periparturient dairy cows.

The climate-relevant enteric methane (CH4) formation represents a loss of feed energy that is potentially meaningful for energetically undersupplied peripartal dairy cows. Higher concentrate feed proportions (CFP) are known to reduce CH4 emissions in cows. The same applies to the feed additive 3-nitrooxypropanol (3-NOP), albeit through different mechanisms. It was hypothesised that the hydrogen not utilised for CH4 formation through the inhibition by 3-NOP would be sequestered by propionate formation triggered by higher CFP so that it could thereby give rise to a synergistically reduced CH4 emission. In a 2 × 2-factorial design, low (LC) or high (HC) CFP were either tested without supplements (CONLC, CONHC) or combined with 3-NOP (NOPLC, 48.4 mg/kg dry matter (DM); NOPHC, 51.2 mg 3-NOP/kg DM). These four rations were fed to a total of 55 Holstein cows from d 28 ante partum until d 120 post partum. DM intake (DMI) was not affected by 3-NOP but increased with CFP (CFP; p < 0.001). CH4/DMI and CH4/energy-corrected milk (ECM) were mitigated by 3-NOP (23% NOPLC, 33% NOPHC) (p < 0.001) and high CFP (12% CON, 22% 3-NOP groups) (CFP × TIME p < 0.001). Under the conditions of the present experiment, the CH4 emissions of NOPLC increased to the level of the CON groups from week 8 until the end of trial (3-NOP × CFP × TIME; p < 0.01). CO2 yield decreased by 3-NOP and high CFP (3-NOP × CFP; p < 0.001). The reduced body weight loss and feed efficiency in HC groups paralleled a more positive energy balance being most obvious in NOPHC (3-NOP × CFP; p < 0.001). ECM was lower for NOPHC compared to CONHC (3-NOP × CFP; p < 0.05), whereas LC groups did not differ. A decreased fat to protein ratio was observed in HC groups and, until week 6 post partum, in NOPLC. Milk lactose and urea increased by 3-NOP (3-NOP; p < 0.05). 3-NOP and high CFP changed rumen fermentation to a more propionic-metabolic profile (3-NOP; CFP; p < 0.01) but did not affect rumen pH. In conclusion, CH4 emission was synergistically reduced when high CFP was combined with 3-NOP while the CH4 mitigating 3-NOP effect decreased with progressing time when the supplement was added to the high-forage ration. The nature of these interactions needs to be clarified.

Short communication: Comparison of the GreenFeed system with the sulfur hexafluoride tracer technique for measuring enteric methane emissions from dairy cows.

The objective of this study was to compare 2 commonly used techniques for measuring methane emissions from ruminant animals: the GreenFeed (GF) system and the sulfur hexafluoride (SF6) technique. The study was part of a larger experiment in which a methane inhibitor, 3-nitrooxypropanol, fed at 4 application rates (0, 40, 60, and 80 mg/kg of feed dry matter) decreased enteric methane emission by an average of 30% (measured by both GF and SF6) in a 12-wk experiment with 48 lactating Holstein cows fed a total mixed ration. The larger experiment used a randomized block design and was conducted in 2 phases (February to May, phase 1, and June to August, phase 2), with 2 sets of 24 cows in each phase. Using both GF and SF6 techniques, methane emission data were collected simultaneously during experimental wk 2, 6, and 12 (phase 1) and 2, 9, and 12 (phase 2), which corresponded to a total of 6 sampling periods. During each sampling period, 8 spot samples of gas emissions (staggered over a 3-d period) were collected from each cow using GF, as well as 3×24-h collections using the SF6 technique. Methane emission data were averaged per cow for the statistical analysis. The mean methane emission was 373 (standard deviation=96.3) and 405 (standard deviation=156) g/cow per day for GF and SF6, respectively. Coefficients of variation for the 2 methods were 25.8 and 38.6%, respectively; correlation and concordance between the 2 methods were 0.40 and 0.34, respectively. The difference in methane emission between the 2 methods (SF6 - GF) within treatment was from 46 to 144 and 24 to 27 g/d for phases 1 and 2, respectively. In the conditions of this experiment, the SF6 technique produced larger variability in methane emissions than the GF method. The overall difference between the 2 methods was on average about 8%, but was not consistent over time, likely influenced by barn ventilation and background methane and SF6 concentrations.

Integrating spot short-term measurements of carbon emissions and backward dietary energy partition calculations to estimate intake in lactating dairy cows fed ad libitum or restricted.

The objective of this study was to use spot short-term measurements of CH4 (QCH4) and CO2 (QCO2) integrated with backward dietary energy partition calculations to estimate dry matter intake (DMI) in lactating dairy cows. Twelve multiparous cows averaging 173±37d in milk and 4 primiparous cows averaging 179±27d in milk were blocked by days in milk, parity, and DMI (as a percentage of body weight) and, within each block, randomly assigned to 1 of 2 treatments: ad libitum intake (AL) or restricted intake (RI=90% DMI) according to a crossover design. Each experimental period lasted 22d with 14d for treatments adaptation and 8d for data and sample collection. Diets contained (dry matter basis): 40% corn silage, 12% grass-legume haylage, and 48% concentrate. Spot short-term gas measurements were taken in 5-min sampling periods from 15 cows (1 cow refused sampling) using a portable, automated, open-circuit gas quantification system (GreenFeed, C-Lock Inc., Rapid City, SD) with intervals of 12h between the 2daily samples. Sampling points were advanced 2h from a day to the next to yield 16 gas samples per cow over 8d to account for diurnal variation in QCH4 and QCO2. The following equations were used sequentially to estimate DMI: (1) heat production (MJ/d)=(4.96 + 16.07 ÷ respiratory quotient) × QCO2; respiratory quotient=0.95; (2) metabolizable energy intake (MJ/d)=(heat production + milk energy) ± tissue energy balance; (3) digestible energy (DE) intake (MJ/d)=metabolizable energy + CH4 energy + urinary energy; (4) gross energy (GE) intake (MJ/d)=DE + [(DE ÷ in vitro true dry matter digestibility) - DE]; and (5) DMI (kg/d)=GE intake estimated ÷ diet GE concentration. Data were analyzed using the MIXED procedure of SAS (SAS Institute Inc., Cary, NC) and Fit Model procedure in JMP (α=0.05; SAS Institute Inc.). Cows significantly differed in DMI measured (23.8 vs. 22.4kg/d for AL and RI, respectively). Dry matter intake estimated using QCH4 and QCO2 coupled with dietary backward energy partition calculations (Equations 1 to 5 above) was highest in cows fed for AL (22.5 vs. 20.2kg/d). The resulting R(2) were 0.28 between DMI measured and DMI estimated by gaseous measurements, and 0.36 between DMI measured and DMI predicted by the National Research Council model (2001). Results showed that spot short-term measurements of QCH4 and QCO2 coupled with dietary backward estimations of energy partition underestimated DMI by 7.8%. However, the approach proposed herein was able to significantly discriminate differences in DMI between cows fed for AL or RI.

Exploring definitions of daily enteric methane emission phenotypes for genetic evaluations using a population of indoor-fed multi-breed growing cattle with feed intake data.

Genetic selection has been identified as a promising approach for reducing enteric methane (CH4) emissions; a prerequisite for genetic evaluations; however, these are estimates of the necessary genetic parameters based on a population representative of where the genetic evaluations will be used. The objective of this study was, therefore, to derive genetic parameters for a series of definitions of CH4, carbon dioxide (CO2), and dry matter intake (DMI) as well as genetic correlations between CH4, CO2, and DMI in a bid to address the paucity of studies involving methane emissions measured in beef cattle using GreenFeed systems. Lastly, estimated breeding values (EBV) were generated for nine alternative definitions of CH4 using the derived genetic parameters; the EBV were validated against both phenotypic performance (adjusted for non-genetic effects) and the Legarra and Reverter method comparing EBV generated for a subset of the dataset compared to EBV generated from the entire dataset. Individual animal CH4 and CO2 records were available from a population of 1,508 multi-breed growing beef cattle using 10 GreenFeed Emission Monitoring systems. Nine trait definitions for CH4 and CO2 were derived: individual spot measures, the average of all spot measures within a 3-h, 6-h, 12-h, 1-d, 5-d, 10-d, and 15-d period and the average of all spot measures across the full test period (20 to 114 d on test). Heritability estimates from 1,155 animals, for CH4, increased as the length of the averaging period increased and ranged from 0.09 ±â€…0.03 for the individual spot measures trait to 0.43 ±â€…0.11 for the full test average trait; a similar trend existed for CO2 with the estimated heritability ranging from 0.17 ±â€…0.04 to 0.50 ±â€…0.11. Enteric CH4 was moderately to strongly genetically correlated with DMI with a genetic correlation of 0.72 ±â€…0.02 between the spot measures of CH4 and a 1-d average DMI. Correlations, adjusted for heritability, between the adjusted phenotype and (parental average) EBV ranged from 0.56 to 1.14 across CH4 definitions and the slope between the adjusted phenotype and EBV ranged from 0.92 to 1.16 (expectation = 1). Validation results from the Legarra and Reverter regression method revealed a level bias of between -0.81 and -0.45, a dispersion bias of between 0.93 and 1.17, and ratio accuracy (ratio of the partial evaluation accuracies on whole evaluation accuracies) from 0.28 to 0.38. While EBV validation results yielded no consensus, CH4 is a moderately heritable trait, and selection for reduced CH4 is achievable.

Livestock production is a significant contributor to greenhouse gas emissions. Animal breeding programs have been proposed as a sustainable mitigation strategy to reduce enteric methane emissions in livestock production. Before creating a genetic evaluation for enteric methane production, it is important to estimate how much inter-animal genetic variability contributes to the observed differences in enteric methane production. The purpose of this study was to explore multiple enteric methane phenotypes and estimate how much phenotypic variation was due to genetic differences among 1,508 growing cattle of multiple breeds and crosses; also of interest was the extent of similarity in the genetic control of enteric methane, carbon dioxide, and feed intake (i.e., the genetic correlation) and to determine if selection of animals on the estimated genetic merit for methane emissions of their parents would manifest itself in differences in actual methane produced by those animals. Between 9% and 43% of the inter-animal differences in daily enteric methane production were due to differences in the genetic composition of those animals; the genetic control influencing methane production was similar to that of feed intake (i.e., a strong genetic correlation between methane emissions and feed intake of up to 0.72).

Combining short-term breath measurements to develop methane prediction equations from cow milk mid-infrared spectra.

Predicting methane (CH4) emission from milk mid-infrared (MIR) spectra provides large amounts of data which is necessary for genomic selection. Recent prediction equations were developed using the GreenFeed system, which required averaging multiple CH4 measurements to obtain an accurate estimate, resulting in large data loss when animals unfrequently visit the GreenFeed. This study aimed to determine if calibrating equations on CH4 emissions corrected for diurnal variations or modeled throughout lactation would improve the accuracy of the predictions by reducing data loss compared with standard averaging methods used with GreenFeed data. The calibration dataset included 1 822 spectra from 235 cows (Holstein, Montbéliarde, and Abondance), and the validation dataset included 104 spectra from 46 (Holstein and Montbéliarde). The predictive ability of the equations calibrated on MIR spectra only was low to moderate (R2v = 0.22-0.36, RMSE = 57-70 g/d). Equations using CH4 averages that had been pre-corrected for diurnal variations tended to perform better, especially with respect to the error of prediction. Furthermore, pre-correcting CH4 values allowed to use all the data available without requiring a minimum number of spot measures at the GreenFeed device for calculating averages. This study provides advice for developing new prediction equations, in addition to a new set of equations based on a large and diverse population.

Short communication: Correlation of methane production, intensity, and yield with residual feed intake throughout lactation in Holstein cows.

The environmental impact of dairy production can be reduced in several ways, including increasing feed efficiency and reducing methane (CH4) emissions. There is no consensus on their relationship. This study aimed at estimating the correlations between residual feed intake (RFI) and CH4 emissions expressed in g/d methane production (MeP), g/kg of fat- and protein-corrected milk methane intensity (MeI), or g/kg of DM intake methane yield (MeY) throughout lactation. We collected CH4 data using GreenFeed devices from 107 Holstein cows, as well as production and intake phenotypes. RFI was predicted from DM intake, fat- and protein-corrected milk, BW, and body condition score. Five-trait random regression models were used to estimate the individual variance components of the CH4 and production traits, which were used to calculate the correlations between RFI and CH4 traits throughout lactation. We found positive correlations of RFI with MeP and MeI ranging from 0.05 to 0.47 throughout the lactation. Correlations between RFI and MeY are low and vary from positive to negative, ranging from -0.18 to 0.17. Both MeP and MeI are favorably correlated with RFI, as is MeY during the first half of lactation. These correlations are mostly favorable for genetic selection, but the confirmation of these results is needed with genetic correlations over a larger dataset.

Characterization of the number of spot samples required for quantification of gas fluxes and metabolic heat production from grazing beef cows using a GreenFeed.

Enteric fermentation from cattle results in greenhouse gas production that is an environmental concern and also an energetic loss. Several methods exist to quantify gas fluxes; however, an open circuit gas quantification system (OCGQS) allows for unencumbered quantification of methane (CH4), carbon dioxide (CO2), and oxygen (O2) from grazing cattle. While previous literature has proven the accuracy of an OCGQS, little work has been done to establish the minimum number of spot samples required to best evaluate an individual grazing animal's gas fluxes and metabolic heat production. A GreenFeed system (C-Lock Inc.) was used to collect at least 100 spot samples each from 17 grazing cows. The mean gas fluxes and metabolic heat production were computed starting from the first 10 visits (forward) and increasing by increments of 10 until an animal had 100 visits. Mean gas fluxes and metabolic heat production were also computed starting from visit 100 (reverse) in increments of 10 using the same approach. Pearson and Spearman correlations were computed between the full 100 visits and each shortened visit interval. A large increase in correlations were seen between 30 and 40 visits. Thus, mean forward and reverse gas fluxes and metabolic heat production were also computed starting at 30 visits and increasing by 2 until 40 visits. The minimum number of spot samples was determined when correlations with the full 100 visits were greater than 0.95. The results indicated that the minimum numbers of spot samples needed for accurate quantification of CH4, CO2, and O2 gas fluxes are 38, 40, and 40, respectively. Metabolic heat production can be calculated using gas fluxes collected by the OCGQS with 36 spot samples. Practically, calculation of metabolic heat production will require 40 spot samples because the component gases for metabolic heat calculation require up to 40 spot samples. Published literature from nongrazing (confined) environments recommended a similar number of total spot samples. Large variation existed around the average number of spot samples for an animal per day, therefore a wide range of test durations may be needed to meet the same number of spot samples in different populations. For this reason, protocols for the OCGQS should be based on the total number of spot samples, rather than a test duration.

Enteric fermentation in ruminant livestock species produces methane (CH4), which has negative effects on the environment and producer profitability. Gas fluxes from livestock species can be quantified with an open-circuit gas quantification system (OCGQS) or GreenFeed (C-Lock Inc., Rapid City, SD). However, little work has been done to establish a standardized protocol for OCGQS use in grazing beef cows. The minimum number of spot samples for quantification of CH4, carbon dioxide (CO2), oxygen (O2), and metabolic heat production was determined for grazing beef cows. The minimum number of spot samples needed for accurate quantification of CH4 was 38. Forty spot samples were needed to quantify CO2 and O2. Metabolic heat production can be calculated using CH4, CO2, and O2 gas fluxes from the OCGQS with 36 spot samples. Approximately 30 d were needed for animals in the current study to obtain the recommended number of visits for gas quantification, but this could vary across studies depending on the frequency of animals visiting the unit. There was large variability in the duration needed to obtain the recommended number of spot samples. Therefore, OCGQS protocols should include a minimum number of spot samples rather than a test duration.

Performance and enteric methane emissions from housed beef cattle fed silage produced on pastures with different forage profiles.

Methane (CH4) produced by ruminants is a significant source of greenhouse gases from agriculture in the United Kingdom (UK), accounting for approximately 50% of the emissions in this sector. Ration modification is linked to changes in rumen fermentation and can be an effective means of CH4 abatement. In temperate climate countries, forage silage represents a major feed component for cattle during the housing period. The objective of this study was, therefore, to compare enteric CH4 emission from cattle offered silage produced from different types of grassland. Beef cattle, steers (n = 89) and heifers (n = 88) with average liveweight (LW) of 328 ± 57.1 kg were evaluated during two housing seasons (2016-2017 and 2017-2018) from November to April, at the Rothamsted Research North Wyke Farm Platform (UK). The treatments corresponded to three diet types, comprising silage harvested from three different pastures: MRG, monoculture of perennial ryegrass (PRG, Lolium perenne L.cv. AberMagic), bred to express the high-sugar phenotype; RG-WC, a mixed sward comprised of the same perennial ryegrass cultivar with white clover (Trifolium repens L.) with a target clover proportion of 30% as land cover; and permanent pasture (PP) dominated by PRG and a small number of non-introduced species. MRG and PP received 160-200 kg N/ha/year. Cattle were weighed every 30 days, and the enteric CH4 emission was determined using GreenFeed automated systems. No significant differences in enteric CH4 emission per head or per kg LW were observed between treatments. However, emission expressed per average daily gain (ADG) in LW was greater (P < 0.001) for MRG compared with RG-WC and PP, at 270, 248 and 235 g CH4/kg ADG, respectively. This related to a lower ADG (P = 0.041) for the animals fed MRG silage compared with RG-WC and PP which were similar, with respective values of 0.67, 0.71 and 0.74 kg/day. The forages compared in this study showed little or no potential to reduce enteric CH4 emission when fed as silage to growing beef cattle during the winter housing period.

Individual methane emissions (and other gas flows) are repeatable and their relationships with feed efficiency are similar across two contrasting diets in growing bulls.

In the current economic and environmental context, the selection of livestock phenotypes combining high feed efficiency (FE) and low greenhouse gas emissions is interesting. This study aimed to quantify methane (CH4) emissions and other gas flows (carbon dioxide (CO2) and dihydrogen (H2) emissions, oxygen (O2) consumption) in growing bulls fed with two contrasting diets in order to (i) evaluate the persistence of individual variability in gas flows through time, and (ii) assess the inter-individual relationship between gas flows and FE across diets. Charolais bulls were fattened for 6 months during two consecutive years in two independent batches (50-51 per year). In each batch, half of the animals received a total mixed ad libitum ration either based on maize silage (62% dietary DM) or high-starch concentrate (MS-S), and half based on grass silage (59% dietary DM) and high-fibre concentrate (GS-F). The absolute gas flows (g/d) were individually measured with 2 GreenFeed systems during 88 days (group 1) and 64 days (group 2). All gas flows were also expressed in g/kg DM intake (gas yield), in g/kg average daily gain (CH4 intensity) and residual of daily emissions for CH4 (R CH4). Different FE metrics (residual feed intake (RFI), residual gain (RG) and feed conversion efficiency (FCE)) were investigated during the same period. The relationships between gas flows and FE metrics were tested by linear regression with the diet as fixed effect. For both diets, we observed a consistent individual variability over the measurement period for absolutes values (g/d) of CH4, CO2, and O2 (repeatability >0.7 for GS-F and >0.6 for MS-S). Gas flows (g/d) were positively correlated with RFI with both diets: animals that ingested food in excess of their theoretical maintenance and growth requirements emitted more CH4, CO2 and consumed more O2. The positive relationship between absolute CH4 emissions and RFI highlighted the interest for low-CH4 emitters and efficient growing bulls when fed with high-energy diets rich in starch or fibre. For both diets, RCH4, CH4 yield and CH4 intensity were not related to RFI whereas a significant negative relationship was reported between CH4 intensity and RG, and FCE. These data suggest that intake is the main driver of the phenotypic relationships between CH4 traits and RFI. Further studies including larger numbers of animals on highly contrasting energy diets are needed to investigate the underlying biological regulatory mechanisms of the methanogenic potential of an animal in relation to production traits.

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.

Methane production from cattle digestion 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 of economic importance. Methane and carbon dioxide emissions recorded using Greenfeed systems were available on a total of 1,099 animals. In addition, performance indicators such as feed intake, live weight, ultrasound scanning data, and slaughter data were also available on all animals. Phenotypic repeatability of CH4 ranged from 0.13 to 0.74, with a CH4 repeatability of >0.6 achieved by both heifers and steers in 10-d measuring period. Due to the high repeatability of CH4 measures, an accurate portrayal of CH4 production can be observed from a 10-d measuring period when measures are averaged. Methane emission data were positively correlated with traits of economic importance. Phenotypically, animals with heavier body weights and greater feed intake had higher emissions.

Characterization and mitigation option of greenhouse gas emissions from lactating Holstein dairy cows in East China.

BACKGROUND: This study investigated greenhouse gas (GHG) emission characteristics of lactating Holstein dairy cows in East China and provided a basis for formulating GHG emission reduction measures. GreenFeed system was used to measure the amount of methane (CH4) and carbon dioxide (CO2) emitted by the cows through respiration. Data from a commercial cow farm were used to observe the effects of parity, body weight, milk yield, and milk component yield on CH4 and CO2 emissions. RESULTS: Mean herd responses throughout the study were as follows: 111 cows completed all experimental processes, while 42 cows were rejected because they were sick or had not visited the GreenFeed system 20 times. On average, lactating days of cows was 138 ± 19.04 d, metabolic weight was 136.5 ± 9.5 kg, parity was 2.8 ± 1.0, dry matter intake (DMI) was 23.1 ± 2.6 kg/d, and milk yield was 38.1 ± 6.9 kg/d. The GreenFeed system revealed that CH4 production (expressed in CO2 equivalent, CO2-eq) was found to be 8304 g/d, [Formula: see text]/DMI was 359 g/kg, [Formula: see text]/energy-corrected milk (ECM) was 229.5 g/kg, total CO2 production (CH4 production plus CO2 production) was 19,201 g/d, total CO2/DMI was 831 g/kg, and total CO2/ECM was 531 g/kg. The parity and metabolic weight of cows had no significant effect on total CO2 emissions (P > 0.05). Cows with high milk yield, milk fat yield, milk protein yield, and total milk solids yield produced more total CO2 (P < 0.05), but their total CO2 production per kg of ECM was low (P < 0.05). The total CO2/ECM of the medium and high milk yield groups was 17% and 27% lower than that of the low milk yield group, respectively. CONCLUSIONS: The parity and body condition had no effect on total CO2 emissions, while the total CO2/ECM was negatively correlated with milk yield, milk fat yield, milk protein yield, and total milk solids yield in lactating Holstein dairy cows. Measurement of total CO2 emissions of dairy cows in the Chinese production system will help establish regional or national GHG inventories and develop mitigation approaches to dairy production regimes.

Technical note: using an automated head chamber system to administer an external marker to estimate fecal output by grazing beef cattle.

The objective of this experiment was to determine if titanium dioxide (TiO2) dosed through an automated head chamber system (GreenFeed; C-Lock Inc., Rapid City, SD, USA) is an acceptable method to measure fecal output. The GreenFeed used on this experiment had a 2-hopper bait dispensing system, where hopper 1 contained alfalfa pellets marked with 1% titanium dioxide (TiO2) and hopper 2 contained unmarked alfalfa pellets. Eleven heifers (BW = 394 ± 18.7 kg) grazing a common pasture were stratified by BW and then randomized to either 1) dosed with TiO2-marked pellets by hand feeding (HFD; n = 6) or 2) dosed with TiO2-marked pellets by the GreenFeed (GFFD; n = 5) for 19 d. During the morning (0800), all heifers were offered a pelleted, high-CP supplement at 0.25% of BW in individual feeding stanchions. The HFD heifers also received 32 g of TiO2-marked pellets at morning feeding, whereas the GFFD heifers received 32 g of unmarked pellets. The GFFD heifers received a single aliquot (32 ± 1.6 g; mean ± SD) of marked pellets at their first visit to the GreenFeed each day with all subsequent 32-g aliquots providing unmarked pellets; HFD heifers received only unmarked pellets. Starting on d 15, fecal samples were collected via rectal grab at feeding and every 12 h for 5 d. A two-one sided t-test method was used to determine agreement and it was determined that the fecal output estimates by HFD and GFFD methods were similar (P = 0.04). There was a difference (P < 0.01; Bartlett's test for homogenous variances) in variability between the dosing methods for HFD and GFFD (SD = 0.1 and 0.7, respectively). This difference in fecal output variability may have been due to variability of dosing times-of-day for the GFFD heifers (0615 ± 6.2 h) relative to the constant dosing time-of-day for HFD and constant 0800 and 2000 sampling times-of-day for all animals. This research has highlighted the potential for dosing cattle with an external marker through a GreenFeed configured with two (or more) feed hoppers because estimated fecal output means were similar; however, consideration of the increased variability of the fecal output estimates is needed for future experimental designs.

Technical note: validation of the GreenFeed system for measuring enteric gas emissions from cattle.

There are knowledge gaps in animal agriculture on how to best mitigate greenhouse gas emissions while maintaining animal productivity. One reason for these gaps is the uncertainties associated with methods used to derive emission rates. This study compared emission rates of methane (CH4) and carbon dioxide (CO2) measured by a commercially available GreenFeed (GF) system with those from (1) a mass flow controller (MFC) that released known quantities of gas over time (i.e., emission rate) and (2) a respiration chamber (RC). The GF and MFC differed by only 1% for CH4 (P = 0.726) and 3% for CO2 (P = 0.013). The difference between the GF and RC was 1% (P = 0.019) for CH4 and 2% for CO2 (P = 0.007). Further investigation revealed that the difference in emission rate for CO2 was due to a small systematic offset error indicating a correction factor could be applied. We conclude that the GF system accurately estimated enteric CH4 and CO2 emission rates of cattle over a short measurement period, but additional factors would need to be considered in determining the 24-hr emission rate of an animal.

In Vitro Incubations Do Not Reflect In Vivo Differences Based on Ranking of Low and High Methane Emitters in Dairy Cows.

This study evaluated if ranking dairy cows as low and high CH4 emitters using the GreenFeed system (GF) can be replicated in in vitro conditions using an automated gas system and its possible implications in terms of fermentation balance. Seven pairs of low and high emitters fed the same diet were selected on the basis of residual CH4 production, and rumen fluid taken from each pair incubated separately in the in vitro gas production system. In total, seven in vitro incubations were performed with inoculums taken from low and high CH4 emitting cows incubated in two substrates differing in forage-to-concentrate proportion, each without or with the addition of cashew nutshell liquid (CNSL) as an inhibitor of CH4 production. Except for the aimed differences in CH4 production, no statistical differences were detected among groups of low and high emitters either in in vivo animal performance or rumen fermentation profile prior to the in vitro incubations. The effect of in vivo ranking was poorly replicated in in vitro conditions after 48 h of anaerobic fermentation. Instead, the effects of diet and CNSL were more consistent. The inclusion of 50% barley in the diet (SB) increased both asymptotic gas production by 17.3% and predicted in vivo CH4 by 26.2%, when compared to 100% grass silage (S) substrate, respectively. The SB diet produced on average more propionate (+28 mmol/mol) and consequently less acetate compared to the S diet. Irrespective of CH4 emitter group, CNSL decreased predicted in vivo CH4 (26.7 vs. 11.1 mL/ g of dry matter; DM) and stoichiometric CH4 (CH4VFA; 304 vs. 235 moles/mol VFA), with these being also reflected in decreased total gas production per unit of volatile fatty acids (VFA). Microbial structure was assessed on rumen fluid sampled prior to in vitro incubation, by sequencing of the V4 region of 16S rRNA gene. Principal coordinate analysis (PCoA) on operational taxonomic unit (OTU) did not show any differences between groups. Some differences appeared of relative abundance between groups in some specific OTUs mainly related to Prevotella. Genus Methanobrevibacter represented 93.7 ± 3.33% of the archaeal sequences. There were no clear differences between groups in relative abundance of Methanobrevibacter.

Response to Climate Change: Evaluation of Methane Emissions in Northern Australian Beef Cattle on a High Quality Diet Supplemented with Desmanthus Using Open-Circuit Respiration Chambers and GreenFeed Emission Monitoring Systems.

The main objective of this study was to compare the effect of supplementing beef cattle with Desmanthus virgatus cv. JCU2, D. bicornutus cv. JCU4, D. leptophyllus cv. JCU7 and lucerne on in vivo methane (CH4) emissions measured by open-circuit respiration chambers (OC) or the GreenFeed emission monitoring (GEM) system. Experiment 1 employed OC and utilized sixteen yearling Brangus steers fed a basal diet of Rhodes grass (Chloris gayana) hay in four treatments-the three Desmanthus cultivars and lucerne (Medicago sativa) at 30% dry matter intake (DMI). Polyethylene glycol (PEG) was added to the diets to neutralize tannin binding and explore the effect on CH4 emissions. Experiment 2 employed GEM and utilized forty-eight animals allocated to four treatments including a basal diet of Rhodes grass hay plus the three Desmanthus cultivars in equal proportions at 0%, 15%, 30% and 45% DMI. Lucerne was added to equilibrate crude protein content in all treatments. Experiment 1 showed no difference in CH4 emissions between the Desmanthus cultivars, between Desmanthus and lucerne or between Desmanthus and the basal diet. Experiment 2 showed an increase in CH4 emissions in the three levels containing Desmanthus. It is concluded that on high-quality diets, Desmanthus does not reduce CH4 emissions.