Methane emissions from beef and dairy cattle: quantifying the effect of physiological stage and diet characteristics

Methane release from enteric fermentation and manure management of domestic water buffalo in Nepal

Methane (CH₄) emission in livestock arises from enteric fermentation (EnF) and manure management (MM). This study develops the country-specific CH₄ emission factors (EFs) in both EnF and MM for domestic water buffalo (Bubalus bubalis) and estimates total CH₄ emission in Nepal using Intergovernmental Panel on Climate Change (IPCC) Tier 2 methodology. Seasonal field data were collected on morphological characteristics, feed characteristics, and manure management practices of the buffalo. The buffalo population was divided into five age groups, and at least 35 buffalo individuals were measured from each age group in the Hilly and Plain regions of Nepal in the winter and summer seasons. Buffalo adult male (BAM) had the highest body weight of 530 ± 53 kg in the plain region and 514 ± 65 kg in the Hill region. Similarly, the weight of buffalo calf (BC) was 91 ± 25 kg in the plain region and 77 ± 26 kg in the Hill region. For different age groups of buffalo, EnF EFs ranged from 34 ± 8 to 90 ± 10 kg CH₄ head^-1 year^-1 and MM EFs ranged from 2.5 ± 0.5 to 7.5 ± 0.5 kg CH₄ head^-1 year^-1. The estimated EnF and MM EFs of buffalo were not statistically different by region (p > 0 .05). The total CH₄ flux from buffalo was 347.8 Gg year^-1 in Nepal, contributing 322.2 Gg year^-1 from EnF and 25.6 Gg year^-1 from MM. The country-specific EFs are highly recommended for precise computing of the national emissions and carrying out mitigation action.

Study on the Discrimination of Possible Error Sources That Might Affect the Quality of Volatile Organic Compounds Signature in Dairy Cattle Using an Electronic Nose

Simple Summary

In recent decades, remarkable progress in the development of electronic nose (EN) technologies, particularly for disease detection, has been accomplished through the disclosure of novel methods and associated devices, mainly for the detection of volatile organic compounds (VOCs). Herein, we assessed the ability of a novel EN technology (MENT-EGAS prototype) to respond to direct sampling and to evaluate the influence of possible error sources that might affect the quality of VOC signatures. Principal Component Analyses (PCA) evidenced the presence in the analyzed samples of sufficient information to consent the discrimination of different environmental backgrounds, feed headspaces and exhalated breath between two groups of cows fed with two different types of feed. Moreover, discrimination was also observed within the same group between exhalated breaths sampled before and after feed intake. Based on these findings, we provided evidence that the MENT-EGAS prototype can identify error sources with accuracy. Livestock precision farming technologies are powerful tools for monitoring animal health and welfare parameters in a continuous and automated way.

Abstract

Electronic nose devices (EN) have been developed for detecting volatile organic compounds (VOCs). This study aimed to assess the ability of the MENT-EGAS prototype-based EN to respond to direct sampling and to evaluate the influence of possible error sources that might affect the quality of VOC signatures. This study was performed on a dairy farm using 11 (n = 11) multiparous Holstein-Friesian cows. The cows were divided into two groups housed in two different barns: group I included six lactating cows fed with a lactating diet (LD), and group II included 5 non-lactating late pregnant cows fed with a far-off diet (FD). Each group was offered 250 g of their respective diet; 10 min later, exhalated breath was collected for VOC determination. After this sampling, 4 cows from each group were offered 250 g of pellet concentrates. Ten minutes later, the exhalated breath was collected once more. VOCs were also measured directly from the feed’s headspace, as well as from the environmental backgrounds of each. Principal component analyses (PCA) were performed and revealed clear discrimination between the two different environmental backgrounds, the two different feed headspaces, the exhalated breath of groups I and II cows, and the exhalated breath within the same group of cows before and after the feed intake. Based on these findings, we concluded that the MENT-EGAS prototype can recognize several error sources with accuracy, providing a novel EN technology that could be used in the future in precision livestock farming.

Enteric Methane Emissions and Animal Performance in Dairy and Beef Cattle Production: Strategies, Opportunities, and Impact of Reducing Emissions

Enteric methane (CH4) emissions produced by microbial fermentation in the rumen resulting in the emission of greenhouse gases (GHG) into the atmosphere. The GHG emissions reduction from the livestock industry can be attained by increasing production efficiency and improving feed efficiency, by lowering the emission intensity of production, or by combining the two. In this work, information was compiled from peer-reviewed studies to analyze CH4 emissions calculated per unit of milk production, energy-corrected milk (ECM), average daily gain (ADG), dry matter intake (DMI), and gross energy intake (GEI), and related emissions to rumen fermentation profiles (volatile fatty acids [VFA], hydrogen [H2]) and microflora activities in the rumen of beef and dairy cattle. For dairy cattle, there was a positive correlation (p < 0.001) between CH4 emissions and DMI (R2 = 0.44), milk production (R2 = 0.37; p < 0.001), ECM (R2 = 0.46), GEI (R2 = 0.50), and acetate/propionate (A/P) ratio (R2 = 0.45). For beef cattle, CH4 emissions were positively correlated (p < 0.05−0.001) with DMI (R2 = 0.37) and GEI (R2 = 0.74). Additionally, the ADG (R2 = 0.19; p < 0.01) and A/P ratio (R2 = 0.15; p < 0.05) were significantly associated with CH4 emission in beef steers. This information may lead to cost-effective methods to reduce enteric CH4 production from cattle. We conclude that enteric CH4 emissions per unit of ECM, GEI, and ADG, as well as rumen fermentation profiles, show great potential for estimating enteric CH4 emissions.

Review: New feeds and new feeding systems in intensive and semi-intensive forage-fed ruminant livestock systems

The contributions that ruminant livestock make to greenhouse gas and other pollutant emissions are well documented and of considerable policy and public concern. At the same time, livestock production continues to play an important role in providing nutrient-rich foodstuffs for many people, particularly in less developed countries. They also offer a means by which plants that cannot be digested by humans, e.g. grass, can be converted into human-edible protein. In this review, we consider opportunities to improve nutrient capture by ruminant livestock through new feeds and feeding systems concentrating on intensive and semi-intensive systems, which we define as those in which animals are given diets that are designed and managed to be used as efficiently as possible. We consider alternative metrics for quantifying efficiency, taking into account resource use at a range of scales. Mechanisms for improving the performance and efficiencies of both individual animals and production systems are highlighted. We then go on to map these to potential changes in feeds and feeding systems. Particular attention is given to improving nitrogen use efficiency and reducing enteric methane production. There is significant potential for the use of home-grown crops or novel feedstuffs such as insects and macroalgae to act as alternative sources of key amino acids and reduce reliance on unsustainably grown soybeans. We conclude by highlighting the extent to which climate change could impact forage-based livestock production and the need to begin work on developing appropriate adaptation strategies.

Seasonal Effect on Feed Intake and Methane Emissions of Cow-Calf Systems on Native Grassland with Variable Herbage Allowance

The aim of this study was to measure methane emissions (CH₄) and herbage intake, and, on the basis of these results, obtain the methane yield (MY, methane yield as g CH₄/kg dry matter intake (DMI) and Ym, methane yield as a percentage of Gross Energy intake), from beef cows grazing on native grasslands. We used forty pregnant heifers, with two treatments of herbage allowance (HA) adjusted seasonally (8 and 5 kg dry matter (DM)/kg cattle live weight (LW), on average), during autumn, winter and spring. Methane emissions (207 g CH₄/d), organic matter intake (OMI, 7.7 kg organic matter (OM)/d), MY (23.6 g CH₄/kg DMI) and Ym (7.4%), were similar between treatments. On the other hand, all variables had a marked increase in spring (10.8 kg OM/d and 312 g CH₄/d), except for Ym. The methane emission factor from Intergovernmental Panel on Climate Change (IPCC) Tier 2 estimated with these results was 78 kg CH₄/head/year. The results show that methane emissions and intake were influenced by the season, but not by the HA analyzed in this study. This information for cow-calf systems in native grasslands in Uruguay can be used in National greenhouse gases (GHG) inventories, representing a relevant contribution to global GHG inventories.

Methane emission factors and carbon fluxes from enteric fermentation in cattle of Nepal Himalaya

This study presents a first estimate of the country-specific enteric methane (CH₄) emission factors (EFs) and the net CH₄ fluxes for the local and improved cattle breeds (LCB and ICB) in Nepal using the IPCC Tier 2 methodology. The country-specific herd structure, morphological and feed characteristics data of cattle were collected from the field survey. In LCB, adult males had the highest mean live body weights (BWs) ranging from 222 ± 42 kg in the Hill to 237 ± 36 kg in the Plain region, while for improved cattle, adult females had the highest BW of 334 ± 45 kg in the Hill to 308 ± 38 kg in the Plain regions. Weight gains of ICB were higher than the LCB. Local calves gained BWs of 97 ± 20 g day^-1, while improved calves gained a weight of 202 ± 41 g day^-1. The CH₄ EFs ranged from 13 ± 3 to 46 ± 9 kg CH₄ head^-1 yr^-1 for different age-groups of the LCB, while for the ICB, the EFs ranged from 14 ± 3 to 75 ± 15 kg CH₄ head^-1 yr^-1. Overall, the EFs were 33 ± 7 and 46 ± 9 kg CH₄ head^-1 yr^-1 for LCB and ICB, respectively. The estimated enteric EFs of cattle in the Hill and Plain regions were not statistically different (p > 0.05), but a significant difference existed between the breeds (LCB and ICB; p < 0.05). The net CH₄ flux was 254 ± 51 Gg yr^-1 from enteric fermentation in cattle of Nepal using the country-specific EFs, about 15% higher than using the default EFs (221 ± 66 Gg yr^-1). We underline that the emission estimation, deploying the country-specific EFs, will be more accurate, contributing to reduce the uncertainties in the national GHG inventories and supporting the mitigation actions.

Identification of rumen microbial biomarkers linked to methane emission in Holstein dairy cows

Mitigation of greenhouse gas emissions is relevant for reducing the environmental impact of ruminant production. In this study, the rumen microbiome from Holstein cows was characterized through a combination of 16S rRNA gene and shotgun metagenomic sequencing. Methane production (CH4 ) and dry matter intake (DMI) were individually measured over 4-6 weeks to calculate the CH4 yield (CH4 y = CH4 /DMI) per cow. We implemented a combination of clustering, multivariate and mixed model analyses to identify a set of operational taxonomic unit (OTU) jointly associated with CH4 y and the structure of ruminal microbial communities. Three ruminotype clusters (R1, R2 and R3) were identified, and R2 was associated with higher CH4 y. The taxonomic composition on R2 had lower abundance of Succinivibrionaceae and Methanosphaera, and higher abundance of Ruminococcaceae, Christensenellaceae and Lachnospiraceae. Metagenomic data confirmed the lower abundance of Succinivibrionaceae and Methanosphaera in R2 and identified genera (Fibrobacter and unclassified Bacteroidales) not highlighted by metataxonomic analysis. In addition, the functional metagenomic analysis revealed that samples classified in cluster R2 were overrepresented by genes coding for KEGG modules associated with methanogenesis, including a significant relative abundance of the methyl-coenzyme M reductase enzyme. Based on the cluster assignment, we applied a sparse partial least-squares discriminant analysis at the taxonomic and functional levels. In addition, we implemented a sPLS regression model using the phenotypic variation of CH4 y. By combining these two approaches, we identified 86 discriminant bacterial OTUs, notably including families linked to CH4 emission such as Succinivibrionaceae, Ruminococcaceae, Christensenellaceae, Lachnospiraceae and Rikenellaceae. These selected OTUs explained 24% of the CH4 y phenotypic variance, whereas the host genome contribution was ~14%. In summary, we identified rumen microbial biomarkers associated with the methane production of dairy cows; these biomarkers could be used for targeted methane-reduction selection programmes in the dairy cattle industry provided they are heritable.

Compositional mixed modeling of methane emissions and ruminal volatile fatty acids from individual cattle and multiple experiments

The aim of the study was to investigate the association of methane (CH) yields (g/kg DMI) with rumen VFA molar proportions and animal and diet-related covariates from individual animals and multiple experiments. The dataset available consisted of 284 measurements of CH yields for beef cattle from 6 experiments measured in indirect respiration chambers. A compositional modeling approach was employed where VFA measurements were considered as a whole, instead of in isolation, emphasizing their multivariate relative scale. The analysis revealed expected close groupings of acetate and butyrate; propionate and valerate; iso-butyrate and iso-valerate. Linear mixed models were then fitted to examine relationships between CH yield and VFA, represented by meaningful log-contrasts of components called compositional balances, while accounting for other animal and diet-related covariates and random variability between experiments. A compositional balance representing (acetate × butyrate)/propionate best explained the contribution of VFA to variation in CH yield. The covariates DMI, forage:concentrate proportion (expressed as a categorical variable diet type: high concentrate, mixed forage:concentrate or high forage), and diet ME were also statistically significant. These results provided new insights into the relative inter-relationships among VFA measurements and also between VFA and CH yield. In conclusion, VFA molar proportions as represented by compositional balances were a significant contributor to explaining variation in CH yields from individual cattle.

Global beef cattle methane emissions: yield prediction by cluster and meta-analyses

Methane yield values (MY; g methane/kg dry-matter intake) in beef cattle reported in the global literature (expanded MitiGate database of methane-mitigation studies) were analysed by cluster and meta-analyses. The Ward and k means cluster analyses included accounting for the categorical effects of methane measurement method, cattle breed type, country or region of study, age and sex of cattle, and proportion of grain in the diet and the standardised continuous variables of number of animals, liveweight and MY. After removal of data from outlier studies, meta-analyses were conducted on subsets of data to produce prediction equations for MY. Removing outliers with absolute studentised residual values of >1, followed by meta-analysis of data accounting for categorical effects, is recommended as a method for predicting MY. The large differences among some countries in MY values were significant but difficult to interpret. On the basis of the datasets available, a single, global MY or percentage of gross energy in feed converted to methane (Ym) value is not appropriate for use in Intergovernmental Panel on Climate Change (IPCC) greenhouse accounting methods around the world. Therefore, ideally country-specific MY values should be used in each country’s accounts (i.e. an IPCC Tier 2 or 3 approach) from data generated within that country.

Methane emissions from lactating and non-lactating dairy cows and growing cattle fed fresh pasture

Currently, a fixed methane (CH4) emission factor is used for calculating total CH4 emissions from cattle in the national greenhouse gas inventory of New Zealand, independent of diet composition, cattle class (beef, dairy) or physiological state (growing, lactating, non-lactating). The objectives of this study were to determine CH4 emissions from lactating and non-lactating dairy cows (118 dairy cows; 81 lactating and 37 non-lactating, over 10 periods) and growing dairy heifers (12 measured twice) fed 100% fresh pasture forage in respiration chambers, which in combination with the published data of beef cattle (36 measured twice) fed fresh pasture were used to determine the relationship between CH4 emissions and dry matter intake (DMI), feed quality, cattle class (dairy vs beef) and physiological state (lactating, non-lactating and growing). Before regression analysis the dominant variables (DMI, CH4) needed to be transformed using natural logarithms (Ln) to make the variation in CH4 emissions more homogeneous across the range of data (i.e. stabilise the variance). Over all periods, average DMI ranged from 3.1 to 13.9 kg/day, average CH4 production from 64 to 325 g/day and average CH4 yield from 21.4 to 26.5 g/kg DMI. The DMI alone explained 90.8% of the variation in CH4 production (LnCH4 (g/day) = 3.250 + 0.9487 × LnDMI). Regression was improved to a minor extent (<3%, with associated increased prediction error) by including physiological status, cattle class or dietary composition in the model, in addition to LnDMI, on LnCH4 production. In conclusion, DMI alone was the strongest predictor for CH4 emissions from cattle fed fresh pasture with minor but irrelevant improvements in the prediction when considering pasture quality, cattle class or physiological status.

An evaluation of the accuracy and precision of methane prediction equations for beef cattle fed high-forage and high-grain diets

The study determined the performance of equations to predict enteric methane (CH4) from beef cattle fed forage- and grain-based diets. Many equations are available to predict CH4 from beef cattle and the predictions vary substantially among equations. The aims were to (1) construct a database of CH4 emissions for beef cattle from published literature, and (2) identify the most precise and accurate extant CH4 prediction models for beef cattle fed diets varying in forage content. The database was comprised of treatment means of CH4 production from in vivo beef studies published from 2000 to 2015. Criteria to include data in the database were as follows: animal description, intakes, diet composition and CH4 production. In all, 54 published equations that predict CH4 production from diet composition were evaluated. Precision and accuracy of the equations were evaluated using the concordance correlation coefficient (r c ), root mean square prediction error (RMSPE), model efficiency and analysis of errors. Equations were ranked using a combined index of the various statistical assessments based on principal component analysis. The final database contained 53 studies and 207 treatment means that were divided into two data sets: diets containing ⩾400 g/kg dry matter (DM) forage (n=116) and diets containing ⩽200 g/kg DM forage (n=42). Diets containing between ⩽400 and ⩾200 g/kg DM forage were not included in the analysis because of their limited numbers (n=6). Outliers, treatment means where feed was fed restrictively and diets with CH4 mitigation additives were omitted (n=43). Using the high-forage dataset the best-fit equations were the International Panel on Climate Change Tier 2 method, 3 equations for steers that considered gross energy intake (GEI) and body weight and an equation that considered dry matter intake and starch:neutral detergent fiber with r c ranging from 0.60 to 0.73 and RMSPE from 35.6 to 45.9 g/day. For the high-grain diets, the 5 best-fit equations considered intakes of metabolisable energy, cellulose, hemicellulose and fat, or for steers GEI and body weight, with r c ranging from 0.35 to 0.52 and RMSPE from 47.4 to 62.9 g/day. Ranking of extant CH4 prediction equations for their accuracy and precision differed with forage content of the diet. When used for cattle fed high-grain diets, extant CH4 prediction models were generally imprecise and lacked accuracy.

Body fat mobilization in early lactation influences methane production of dairy cows

Long-chain fatty acids mobilized during early lactation of dairy cows are increasingly used as energy substrate at the expense of acetate. As the synthesis of acetate in the rumen is closely linked to methane (CH4) production, we hypothesized that decreased acetate utilization would result in lower ruminal acetate levels and thus CH4 production. Twenty heifers were sampled for blood, rumen fluid and milk, and CH4 production was measured in respiration chambers in week -4, +5, +13 and +42 relative to first parturition. Based on plasma non-esterified fatty acid (NEFA) concentration determined in week +5, animals were grouped to the ten highest (HM; NEFA > 580 μmol) and ten lowest (LM; NEFA < 580 μmol) mobilizing cows. Dry matter intake (DMI), milk yield and ruminal short-chain fatty acids did not differ between groups, but CH4/DMI was lower in HM cows in week +5. There was a negative regression between plasma NEFA and plasma acetate, between plasma NEFA and CH4/DMI and between plasma cholecystokinin and CH4/DMI in week +5. Our data show for the first time that fat mobilization of the host in early lactation is inversely related with ruminal CH4 production and that this effect is not attributed to different DMI.

A universal equation to predict methane production of forage-fed cattle in Australia

The methods for estimating methane emissions from cattle as used in the Australian national inventory are based on older data that have now been superseded by a large amount of more recent data. Recent data suggested that the current inventory emissions estimates can be improved. To address this issue, a total of 1034 individual animal records of daily methane production (MP) was used to reassess the relationship between MP and each of dry matter intake (DMI) and gross energy intake (GEI). Data were restricted to trials conducted in the past 10 years using open-circuit respiration chambers, with cattle fed forage-based diets (forage >70%). Results from diets considered to inhibit methanogenesis were omitted from the dataset. Records were obtained from dairy cattle fed temperate forages (220 records), beef cattle fed temperate forages (680 records) and beef cattle fed tropical forages (133 records). Relationships were very similar for all three production categories and single relationships for MP on a DMI or GEI basis were proposed for national inventory purposes. These relationships were MP (g/day) = 20.7 (±0.28) × DMI (kg/day) (R2 = 0.92, P < 0.001) and MP (MJ/day) = 0.063 (±0.008) × GEI (MJ/day) (R2 = 0.93, P < 0.001). If the revised MP (g/day) approach is used to calculate Australia’s national inventory, it will reduce estimates of emissions of forage-fed cattle by 24%. Assuming a global warming potential of 25 for methane, this represents a 12.6 Mt CO2-e reduction in calculated annual emissions from Australian cattle.