Effect of a blend of cinnamaldehyde, eugenol, and Capsicum oleoresin on methane emission and lactation performance of Holstein-Friesian dairy cows

This study aimed to investigate the effect of administering a standardized blend of cinnamaldehyde, eugenol, and Capsicum oleoresin (CEC) to lactating dairy cattle for 84 d (i.e., 12 wk) on enteric CH4 emission, feed intake, milk yield and composition, and body weight. The experiment involved 56 Holstein-Friesian dairy cows (145 ± 31.1 d in milk at the start of the trial; mean ± standard deviation) in a randomized complete block design. Cows were blocked in pairs according to parity, lactation stage, and current milk yield, and randomly allocated to 1 of the 2 dietary treatments: a diet including 54.5 mg of CEC/kg of DM or a control diet without CEC. Diets were provided as partial mixed rations in feed bins, which automatically recorded individual feed intake. Additional concentrate was fed in the GreenFeed system that was used to measure emissions of CO2, CH4, and H2. Feeding CEC decreased CH4 yield (g/kg DMI) by on average 3.4% over the complete 12-wk period and by on average 3.9% from 6 wk after the start of supplementation onward. Feeding CEC simultaneously increased feed intake and body weight, and tended to increase milk protein content, whereas no negative responses were observed. These results must be further investigated and confirmed in longer-term in vivo experiments.

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

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

Evaluating enteric methane emissions within a herd of genetically divergent grazing dairy cows

Enteric methane (CH4) emissions of 3 genetic groups (GG) of dairy cows were recorded across the grazing season (early March to late October). The 3 GG were (1) high economic breeding index (EBI) Holstein-Friesian (HF) representative of the top 1% of dairy cows in Ireland at the time of the study (elite), (2) national average (NA) EBI, which were representative of the average HF dairy cow in Ireland, and (3) purebred Jersey (JE) cows. Enteric CH4 was recorded using GreenFeed technology. Seasonal variation in CH4 was observed, with the lowest daily CH4 emissions and CH4 expressed per unit of dry matter intake occurring in spring (253 g/d and 15.56 g/kg, respectively), intermediate in summer (303 g/d and 18.26 g/kg, respectively), and greatest in autumn (324 g/d and 19.80 g/kg, respectively). Seasonal variation was also observed in the proportion of gross energy intake converted to CH4 (Ym); in the spring the Ym was lowest at 0.046, increasing to 0.053 and 0.058 in the summer and autumn, respectively. There was no difference in daily CH4 between the elite and NA, whereas JE had lower CH4 emissions compared with the elite. When expressed per unit of milk solids (fat + protein yield; MS), the elite and JE produced 6.8% and 9.7% less CH4 per kilogram of MS, respectively, compared with NA. There was no difference between the GG for CH4 per unit of DMI or the Ym. This research emphasizes the variation in CH4 emissions across the grazing season and among cows of differing genetic merit for CH4 emission intensities but not for CH4 per unit of DMI or the Ym.

Increased Milk Yield and Reduced Enteric Methane Concentration on a Commercial Dairy Farm Associated with Dietary Inclusion of Sugarcane Extract (Saccharum officinarum)

(1) Background: The purpose of this study was to assess the influence of a natural sugarcane extract (Polygain™) on milk production, milk composition and methane emissions on a commercial dairy farm. (2) Methods: A three-week baseline was established for lactating Holstein × Friesian animals. Following this baseline period, these animals were fed Polygain™ at 0.25% of their estimated dry matter intake for 3 weeks. Methane concentration in the feed bin was determined at each milking using the Gascard NG Infrared Sensor (Edinburgh Sensors LTD). (3) Results: During the intervention phase milk yield increased significantly from 26.43 kg to 28.54 kg per cow per day, whilst methane emissions and bulk tank somatic cell counts decreased significantly in the intervention phase. For methane concentration, an average of 246 ppm during the baseline periods reduced to an average of 161.09 ppm during the intervention phase. For the bulk tank somatic cell counts, the average was observed at 283,200 during the baseline and reduced to an average value of 151,100 during the intervention phase. (4) Conclusions: The natural sugarcane extract was shown to have the potential to mitigate enteric methane emissions while also increasing production and animal wellbeing outcomes in a commercial dairy setting.

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.

Producing natural functional and low-carbon milk by regulating the diet of the cattle-The fatty acid associated rumen fermentation, biohydrogenation, and microorganism response

Conjugated linoleic acid (CLA) has drawn significant attention in the last two decades for its various potent beneficial effects on human health, such as anticarcinogenic and antidiabetic properties. CLA could be generally found in ruminant products, such as milk. The amount of CLA in ruminant products mainly depends on the diet of the animals. In general, the fat content in the ruminant diet is low, and dietary fat supplementation can be provided to improve rumen activity and the fatty acid (FA) profile of meat and milk. Especially, dietary 18-carbon polyunsaturated FA (C18 PUFA), the dominant fat source for ruminants, can modify the milk FA profile and other components by regulating the ruminal microbial ecosystem. In particular, it can improve the CLA in milk, intensify the competition for metabolic hydrogen for propionate producing pathways and decrease methane formation in the rumen. Therefore, lipid supplementation appears to be a promising strategy to naturally increase the additional nutritional value of milk and contribute to lower methane emissions. Meanwhile, it is equally important to reveal the effects of dietary fat supplementation on rumen fermentation, biohydrogenation (BH) process, feed digestion, and microorganisms. Moreover, several bacterial species and strains have been considered to be affected by C18 PUFA or being involved in the process of lipolysis, BH, CLA, or methane emissions. However, no review so far has thoroughly summarized the effects of C18 PUFA supplementation on milk CLA concentration and methane emission from dairy cows and meanwhile taken into consideration the processes such as the microorganisms, digestibility, rumen fermentation, and BH of dairy cattle. Therefore, this review aims to provide an overview of existing knowledge of how dietary fat affects rumen microbiota and several metabolic processes, such as fermentation and BH, and therefore contributes to functional and low-carbon milk production.

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.

Characterization of rumen, fecal, and milk microbiota in lactating dairy cows

Targeting the gastrointestinal microbiome for improvement of feed efficiency and reduction of production costs is a potential promising strategy. However little progress has been made in manipulation of the gut microbiomes in dairy cattle to improve milk yield and milk quality. Even less understood is the milk microbiome. Understanding the milk microbiome may provide insight into how the microbiota correlate with milk yield and milk quality. The objective of this study was to characterize similarities between rumen, fecal, and milk microbiota simultaneously, and to investigate associations between microbiota, milk somatic cell count (SCC), and milk yield. A total of 51 mid-lactation, multiparous Holstein dairy cattle were chosen for sampling of ruminal, fecal, and milk contents that were processed for microbial DNA extraction and sequencing. Cows were categorized based on low, medium, and high SCC; as well as low, medium, and high milk yield. Beta diversity indicated that ruminal, fecal, and milk populations were distinct (p &lt; 0.001). Additionally, the Shannon index demonstrated that ruminal microbial populations were more diverse (p &lt; 0.05) than were fecal and milk populations, and milk microbiota was the least diverse of all sample types (p &lt; 0.001). While diversity indices were not linked (p &gt; 0.1) with milk yield, milk microbial populations from cows with low SCC demonstrated a more evenly distributed microbiome in comparison to cows with high SCC values (p = 0.053). These data demonstrate the complexity of host microbiomes both in the gut and mammary gland. Further, we conclude that there is a significant relationship between mammary health (i.e., SCC) and the milk microbiome. Whether this microbiome could be utilized in efforts to protect the mammary gland remains unclear, but should be explored in future studies.

Full-lactation performance of multiparous dairy cows with differing residual feed intake

Residual feed intake (RFI) is an efficiency trait underpinning profitability and environmental sustainability in dairy production. This study compared performance during a complete lactation of 36 multiparous dairy cows divided into three equal-sized groups with high (HRFI), intermediate (IRFI) or low RFI (LRFI). Residual feed intake was determined by two different equations. Residual feed intake according to the NorFor system was calculated as (RFI_NorFor) = (NE_intake)–(NE_maintenance + NE_gestation + NE_milk—NE_mobilisation + NE_deposition). Residual feed intake according to the USA National Research Council (NRC) (RFI_NRC) was calculated as: RFI = DMI − predicted DMI where predicted_s DMI = [(0.372× ECM)+(0.0968×BW^0.75)]×(1−e^{−0.192×(DIM/7+3.67)}). Cows in the HRFI_NorFor group showed higher daily CH₄ production, CH₄/ECM and CH₄ yield (g/kg DMI) than IRFI_NorFor and LRFI_NorFor cows. Cows characterized by high efficiency (LRFI_NorFor) according to the NorFor system had lower body weight. Dry matter intake and apparent dry matter digestibility were not affected by efficiency group but milk yield was lower in the low efficiency, HRFI_NorFor, group. Cows characterized by high efficiency according to the NRC system (LRFI_NRC) had lower dry matter intake while yield of CH₄ was higher. Daily CH₄ production and CH₄ g/kg ECM did not differ between RFI_NRC groups. Dairy cows characterized by high efficiency (both LRFI_NorFor and LRFI_NRC cows) over a complete lactation mobilized more of their body reserves in early lactation as well as during the complete lactation. The results also indicated great phenotypic variation in RFI between different stages the lactation.

Genetic parameters for body weight and milk production of dairy Gyr herds

The aim of this study was to estimate genetic parameters for cow weight at calving (CW) and cumulative 305-day milk yield (MY305) in dairy Gyr cattle by two-trait analysis. The study used 1,847 CW records, in which 418 females presented more than one measure, and 4,048 MY305 records, wherein 1068 females provided repeated measures, from 2,339 females belonging to three herds, which calved between 1986 and 2019. Variance components were estimated by the restricted maximum likelihood method (REML) using a two-trait animal model. The model included direct additive genetic, permanent environmental and residual effects as random effects and the fixed effects of contemporary group, formed by animals that had calved on the same farm year and season, and age of cow at calving as covariates (fitted as linear and quadratic effect). The heritability estimates for CW and MY305 were 0.21 ± 0.06 and 0.29 ± 0.04, respectively, and repeatability estimates were 0.49 ± 0.03 and 0.49 ± 0.02. The genetic correlation between CW and MY305 was positive and of low magnitude (0.33 ± 0.18), indicating that selection for MY305 will cause little genetic change in the weights of dairy Gyr animals. The genetic trends of breeding values of analyzed traits showed marked genetic gains in MY305, with little changes in CW of dairy Gyr cows over the years in the herds studied, which is an important result considering the production systems adopted in the tropics.

Genome-wide association study for methane emission traits in Danish Holstein cattle

Selecting for lower methane emitting cows requires insight into the most biologically relevant phenotypes for methane emission, which are close to the breeding goal. Several methane phenotypes have been suggested over the last decade. However, the (dis)similarity of their underlying genetic architecture and correlation structures are poorly understood. Therefore, the objective of this study was to test association of SNP and genomic regions through GWAS on 8 CH₄ emission traits in Danish Holstein cattle. The traits studied were methane concentration (MeC; ppm), methane production (MeP ; g/d), 2 definitions of residual methane (RMETc and RMETp: MeC and MeP regressed on metabolic body weight and energy-corrected milk, respectively), 2 definitions of methane intensity (MeI; MeIc = MeC/ECM and MeIp = MeP/ECM); 2 definitions of methane yield per kilogram of dry matter intake (MeY; MeYc = MeC/dry matter intake and MeYp = MeP/dry matter intake). A total of 1,962 cows with genotypes (Illumina BovineSNP50 Chip or Eurogenomic custom SNP chip) and repeated records of the above-mentioned 8 methane traits were analyzed. Strong associations were found with 3 traits (MeC, MeP, and MeYc) on chromosome 13 and with 5 traits (MeC, MeP, MeIp, MeYp, and MeYc) on chromosome 26. For MeIc, MeIp, RMETc, MeYc, and MeYp, some suggestive association signals were identified on chromosome 1. Genomic segments of 1 Mbp (n = 2,525) were tested for their association with these traits, which identified between 33 to 54 significantly associated regions. In a pairwise comparison, MeC and MeP were the traits that shared the highest number of significant segments (17). The same trend was observed when comparing SNP significantly associated with the traits MeC and MeP shared from 23 to 25 SNP (most of which were located in chromosomes 11, 13, and 26). Based on our results on GWAS and genetic correlations, we conclude that MeC is (genetically) more closely linked to MeP than any of the other methane traits analyzed.

In Vitro and In Vivo Evaluation of the Effects of a Compound Based on Plants, Yeast and Trace Elements on the Ruminal Function of Dairy Cows

The high production levels reached by the dairy sector need adjustment in nutritional inputs and efficient feed conversion. In this context, we evaluated a compound (QY—Qualix Yellow) combining optimized inputs in trace elements and 20% MIX 3.0. In a first step, the effects of MIX 3.0 on ruminal function were assessed in vitro by incubating ruminal fluid with the mixture at a ratio of 20:1. The results obtained encouraged us to test QY in vivo, on a herd of dairy cows. The herd was divided into one group of 19 dairy cows receiving the compound and a control group of 20 animals conducted in the same conditions, but which did not received the compound; the production performance and feed efficiency of the two groups were compared. In vitro experiments showed improved digestion of acid and neutral detergent fibres by 10%. The propionate production was enhanced by 14.5% after 6 h incubation with MIX 3.0. The plant mixture decreased the production of methane and ammonia by 37% and 52%, respectively, and reduced the number of protozoa by 50%. An increase in milk yield by 2.4 kg/cow/d (p < 0.1), combined with a decrease in concentrate consumption of 0.27 kg DM/cow/d (p < 0.001), was observed in vivo after consumption of the compound. Sixty-six days after the beginning of the trial, methane emissions per kg of milk were significantly lower in the group receiving QY. In conclusion, MIX 3.0 induced change in ruminal function in vitro and, when it entered into the composition of the QY, it appeared to improve feed efficiency and production performance in vivo.

Mineral status and enteric methane production in dairy cows during different stages of lactation

BACKGROUND

Lactating dairy cows are the greatest livestock contributor of methane, a major global greenhouse gas (GHG). However, good feeding management with adequate mineral intake can offers an effective approach to maintaining high levels of milk production and the health of dairy cows over the entire course of lactation, while also helping to reduce methane emission. The study described here investigated the plasma concentrations of both macroelements (Ca, Na, K, Mg, P) and microelements (Zn, Cu, Fe, Mn), as well as enteric methane emission and milk composition in high-yielding dairy cows in different lactation periods. The experiment was performed on Holstein-Friesian dairy cows with the average milk yield of 41 (± 9) L/day in a Polish commercial farm with modern dairy systems. A total of thirty high-yielding dairy cows were randomly assigned into three groups differing by lactation stage: early stage (Early, days 25-100), middle stage (Middle, days 101-250), and late stage (Late, day 250 and later). Dietary treatment for all cows was a total mixture ration (TMR) with maize and alfalfa silage the main forage components.

RESULTS

The greatest milk yield and methane production were recorded in early-stage lactating cows, but the greatest methane intensity per kg of corrected milk was recorded in the late stage of lactation. Plasma concentrations of macroelements and microelements did not differ by lactation stages, but increased plasma concentrations of Zn and Fe and decreased plasma levels of Mg were noted during lactation. A positive correlation was found between plasma levels of Mg and other macroelements (Ca, Na, K), and between the concentrations of Fe and Zn, P in plasma, but no correlation between methane emission and mineral status was detected in the different lactation stages.

CONCLUSIONS

Our results showed different mineral requirements and enteric methane emissions in each lactation stage. The feeding strategy and mineral utilization were adequate to maintain the health, mineral status, and milk production of the Holstein cows during the entire lactation period, and suggest an effective way of reducing methane emission.

Review: More effective linkages between science and policy are needed to minimize the negative environmental impacts of livestock production

Animals form an integral part of our planetary ecosystem but balance is critical to effective ecosystem functioning as demand for livestock products has increased, greater numbers of domesticated livestock have created an imbalance and hence had a negative impact on a number of ecosystem services which means that life as we know it will become unsustainable. Policies and technology advances have helped to manage the impact but more needs to be done. The aim of this paper is to highlight ways in which better knowledge of animal science, and other disciplines, can both harness technology and inform policy to work towards a sustainable balance between livestock and the environment. Effective policies require simple, quantifiable indicators against which to set targets and monitor progress. Indicators are clear for water pollution, but more complex for biodiversity. Hence, more progress has been made with the former. It is not yet possible to measure the impacts of changes in livestock management on greenhouse gas emissions per se at a farm level and progress has been slower, although new technologies are emerging. With respect to land use, the simple indicator of area has been used, but total area is oversimplistic. Our analysis of land suitability and use highlights a relatively overlooked role of livestock in acting as a 'buffer' to use by-products and grains which do not meet the standards for processing by industry during years of inclement weather, which in the past has provided an 'insurance policy' for farmers. Since extreme weather events are increasing in frequency with climate change, this role for livestock may be more important in future. The conclusions of the review with respect to strengthening the links between research and policy are i) to encourage animal scientists to identify the relevant environmental indicators, work with the cutting edge experts developing technologies to measure these cost-effectively and across a range of relevant livestock systems and ii) to work with the feed industry to optimize diets not just in terms of least cost financially but also least 'cost' in terms of global carbon flux and engage in dialogue with the food industry and policy makers on regulations for grain quality.

Improving robustness and accuracy of predicted daily methane emissions of dairy cows using milk mid-infrared spectra

BACKGROUND

A robust proxy for estimating methane (CH₄ ) emissions of individual dairy cows would be valuable especially for selective breeding. This study aimed to improve the robustness and accuracy of prediction models that estimate daily CH₄ emissions from milk Fourier transform mid-infrared (FT-MIR) spectra by (i) increasing the reference dataset and (ii) adjusting for routinely recorded phenotypic information. Prediction equations for CH₄ were developed using a combined dataset including daily CH₄ measurements (n = 1089; g d^-1 ) collected using the SF₆ tracer technique (n = 513) and measurements using respiration chambers (RC, n = 576). Furthermore, in addition to the milk FT-MIR spectra, the variables of milk yield (MY) on the test day, parity (P) and breed (B) of cows were included in the regression analysis as explanatory variables.

RESULTS

Models developed based on a combined RC and SF₆ dataset predicted the expected pattern in CH₄ values (in g d^-1 ) during a lactation cycle, namely an increase during the first weeks after calving followed by a gradual decrease until the end of lactation. The model including MY, P and B information provided the best prediction results (cross-validation statistics: R² = 0.68 and standard error = 57 g CH₄ d^-1 ).

CONCLUSIONS

The models developed accounted for more of the observed variability in CH₄ emissions than previously developed models and thus were considered more robust. This approach is suitable for large-scale studies (e.g. animal genetic evaluation) where robustness is paramount for accurate predictions across a range of animal conditions. © 2020 Society of Chemical Industry.

Breeding for reduced methane emission and feed-efficient Holstein cows: An international response

Selecting for lower methane (CH₄) emitting animals is one of the best approaches to reduce CH₄ given that genetic progress is permanent and cumulative over generations. As genetic selection requires a large number of animals with records and few countries actively record CH₄, combining data from different countries could help to expedite accurate genetic parameters for CH₄ traits and build a future genomic reference population. Additionally, if we want to include CH₄ in the breeding goal, it is important to know the genetic correlations of CH₄ traits with other economically important traits. Therefore, the aim of this study was first to estimate genetic parameters of 7 suggested methane traits, as well as genetic correlations between methane traits and production, maintenance, and efficiency traits using a multicountry database. The second aim was to estimate genetic correlations within parities and stages of lactation for CH₄. The third aim was to evaluate the expected response of economically important traits by including CH₄ traits in the breeding goal. A total of 15,320 methane production (MeP, g/d) records from 2,990 cows belonging to 4 countries (Canada, Australia, Switzerland, and Denmark) were analyzed. Records on dry matter intake (DMI), body weight (BW), body condition score, and milk yield (MY) were also available. Additional traits such as methane yield (MeY; g/kg DMI), methane intensity (MeI; g/kg energy-corrected milk), a genetic standardized methane production, and 3 definitions of residual methane production (g/d), residual feed intake, metabolic BW (MBW), BW change, and energy-corrected milk were calculated. The estimated heritability of MeP was 0.21, whereas heritability estimates for MeY and MeI were 0.30 and 0.38, and for the residual methane traits heritability ranged from 0.13 to 0.16. Genetic correlations between different methane traits were moderate to high (0.41 to 0.97). Genetic correlations between MeP and economically important traits ranged from 0.29 (MY) to 0.65 (BW and MBW), being 0.41 for DMI. Selection index calculations showed that residual methane had the most potential for inclusion in the breeding goal when compared with MeP, MeY, and MeI, as residual methane allows for selection of low methane emitting animals without compromising other economically important traits. Inclusion of residual feed intake in the breeding goal could further reduce methane, as the correlation with residual methane is moderate and elicits a favorable correlated response. Adding a negative economic value for methane could facilitate a substantial reduction in methane emissions while maintaining an increase in milk production.

Between-cow variation in the components of feed efficiency

A meta-analysis based on an individual-cow data set was conducted to investigate between-cow variations in the components and measurements of feed efficiency (FE) and to explore the associations among these components. Data were taken from 31 chamber studies, consisting of a total of 841 cow/period observations. The experimental diets were based on grass or corn silages, fresh grass, or a mixture of fresh grass and straw, with cereal grains or by-products as energy supplements, and soybean or canola meal as protein supplements. The average forage-to-concentrate ratio across all diets on a dry matter basis was 56:44. Variance component and repeatability estimates of FE measurements and components were determined using diet, period, and cow within experiment as random effects in mixed procedures of SAS (SAS Institute Inc., Cary, NC). The between-cow coefficient of variation (CV) in gross energy intake (GE; CV = 0.10) and milk energy (El) output as a proportion of GE (El/GE; CV = 0.084) were the largest among all component traits. Similarly, the highest repeatability estimates (≥0.50) were observed for these 2 components. However, the between-cow CV in digestibility (DE/GE), metabolizability [metabolizable energy (ME)/GE], methane yield (CH4E/GE), proportional urinary energy output (UE/GE), and heat production (HP/GE), as well as the efficiency of ME use for lactation (kl), were rather small. The least repeatable component of FE was UE/GE. For FE measurements, the between-cow CV in residual energy-corrected milk (RECM) was larger than for residual feed intake (RFI), suggesting a greater possibility for genetic gain in RECM than in RFI. A high DE/GE was associated with increased CH4E/GE (r = 0.24), HP/GE (r = 0.12), ME/GE (r = 0. 91), energy balance as a proportion of GE (EB/GE; r = 0.35), and kl (r = 0.10). However, no correlation between DE/GE and GE intake or UE/GE was observed. Increased proportional milk energy adjusted to zero energy balance (El(0)/GE) was associated with increases in DE/GE, ME/GE, EB/GE, and kl but decreases in UE/GE, CH4E/GE, and HP/GE, with no effect on GE intake. In conclusion, several mechanisms are involved in the observed differences in FE among dairy cows, and reducing CH4E yield (CH4E/GE) may inadvertently result in reduced GE digestibility. However, the selection of dairy cows with improved energy utilization efficiencies offers an effective approach to lower enteric CH4 emissions.

The relationship between methane emission and daytime-dependent fecal archaeol concentration in lactating dairy cows fed two different diets

Archaeol is a cell membrane lipid of methanogenic archaea excreted in feces and is therefore a potential biomarker for individual methane emission (MEM). The aims of this study were to examine the potential of the fecal archaeol concentration (fArch) to be a proxy for MEM prediction in cows fed different diets and determine if the time of fecal collection affected the archaeol concentration. Thus, we investigated (i) the variation of the fArch concentration in spot samples of feces taken thrice within 8 h during respiration chamber measurements and (ii) the effect of two diets differing in nutrient composition and net energy content on the relationship between fArch and MEM in lactating cows. Two consecutive respiration trials with four primiparous and six multiparous lactating Holstein cows were performed. In the first trial (T1) at 100±3 d in milk (IM), a diet moderate in starch and fat content was fed for ad libitum intake, whereas in the second trial (T2) at 135±3 d IM, cows received a diet lower in starch and fat. Individual MEM (g d-1) was measured for 24 h. Fecal samples were taken at 06:30, 10:00, and 14:30 LT and analyzed for fArch using Soxhlet lipid extraction and GC–MS. Cows produced less methane (364 g CH4 d-1) during T1 and had significantly lower fArch concentrations (37.1 µg g-1 dry matter; DM) compared to T2 (392 g CH4 d-1 and 47.6 µg g-1 DM). A significant positive relationship between fArch (µg g-1 fecal DM) and MEM, expressed on a dry matter intake (DMI) basis (g kg-1 DMI), was found (R2=0.53, n=20). Among samples collected over the day, those collected at 10:00 LT provided the best coefficient of determination for MEM (R2=0.23). In conclusion, fArch offers some potential in serving as a proxy for innovative breeding schemes to lower enteric methane when fecal samples are taken at a certain time of the day, but more data on the sources of variation of the MEM : fArch ratios are required.

Multitrait genomic prediction of methane emissions in Danish Holstein cattle

In dairy cattle, selecting for lower methane-emitting animals is one of the new challenges of this decade. However, genetic selection requires a large number of animals with records to get accurate estimated breeding values (EBV). Given that CH₄ records are scarce, the use of information on routinely recorded and highly correlated traits with CH₄ has been suggested to increase the accuracy of genomic EBV (GEBV) through multitrait (genomic) prediction. Therefore, the objective of this study was to evaluate accuracies of prediction of GEBV for CH₄ by including or omitting CH₄, energy-corrected milk (ECM), and body weight (BW) as well as genotypic information in multitrait analyses across 2 methods: BLUP and single-step genomic BLUP (SSGBLUP). A total of 2,725 cows with CH₄ concentration in breath (14,125 records), BW (61,667 records), and ECM (61,610 records) were included in the analyses. Approximately 2,000 of these cows were genotyped or imputed to 50K. Ten cross-validation groups were formed by randomly grouping paternal half-sibs. Five scenarios were performed: (1) base scenario with only CH₄ information; (2) without CH₄, but with information from BW, ECM, or BW+ECM only in reference population; (3) without CH₄, but with information from BW, ECM, or BW+ECM in both validation and reference population; (4) with CH₄ information and BW, ECM, or BW+ECM information only in the reference population; and (5) with CH₄ information and BW, ECM, or BW+ECM information in both validation and reference population. As a result, for each method (BLUP, SSGBLUP), 13 sub-scenarios were performed, 1 from scenario 1, and 3 for each of the subsequent 4 scenarios. The average accuracy of GEBV for CH₄ in the base scenario was 0.32 for BLUP and 0.42 for SSGBLUP, and it ranged from 0.10 in scenario 2 to 0.78 in scenario 5 across methods. In terms of bias, the base scenario 1 was unbiased for SSGBLUP; similar results were achieved with scenario 5. Including information on ECM increased the accuracy of GEBV for CH₄ by up to 61%, whereas adding information on both traits (BW and ECM) increased the accuracy by up to 90%. Scenarios that did not include CH₄ in the reference population had the lowest correlations (0.17-0.33) with single-trait CH₄ GEBV, and scenarios with CH₄ in the reference population had the highest correlations (0.41-0.81). Thus, failure to include CH₄ in future reference populations results in predicted CH₄ GEBV, which cannot be used in practical selection. Therefore, recording CH₄ in more animals remains a priority. Finally, multiple-trait genomic prediction using routinely recorded BW and ECM leads to higher prediction accuracies than traditional single-trait genomic prediction for CH₄ and is a viable solution for increasing the accuracies of GEBV for scarcely recorded CH₄ in practice.

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.

Mitigation of greenhouse gases in dairy cattle via genetic selection: 1. Genetic parameters of direct methane using noninvasive methods and proxies of methane

Records of methane emissions from 1,501 cows on 14 commercial farms in 4 regions of Spain were collected from May 2018 to June 2019. Methane concentrations (MeC) were measured using a nondispersive infrared methane detector installed within the feed bin of the automatic milking system during 14- to 21-d periods. Rumination time (RT; min/d) was collected using collars with a tag that registered time (minutes) spent eating and ruminating. The means of MeC and methane production (MeP) were 1,254.28 ppm and 182.49 g/d, respectively; mean RT was 473.38 min/d. Variance components for MeC, MeP, and RT were estimated with REML using pedigree and genomic information in a single-step model. Heritabilities for MeC and MeP were 0.11 and 0.12, respectively. Rumination time showed a slightly larger heritability estimate (0.17). The genetic correlation between MeP and MeC was high (>0.95), suggesting that selection on either trait would lead to a positive correlated response on the other. Negative correlations were estimated between RT and MeC (-0.24 ± 0.38) and MeP (-0.43 ± 0.35). Methane concentration and MeP had slightly positive correlations with milk yield (0.17 ± 0.39 and 0.21 ± 0.36), protein percentage (0.08 ± 0.32 and 0.30 ± 0.45), protein yield (0.22 ± 0.41 and 0.31 ± 0.35), fat percentage (0.02 ± 0.40 and 0.27 ± 0.36), and fat yield (0.27 ± 0.28 and 0.29 ± 0.28) from bivariate analyses. Rumination time had positive correlations with milk yield (0.41 ± 0.75) and protein yield (0.26 ± 0.57) and negative correlations with fat yield (-0.45 ± 0.32), protein percentage (-0.15 ± 0.38), and fat percentage (-0.40 ± 0.47). A positive approximated genetic correlation was estimated between fertility and MeC (0.10 ± 0.05) and MeP (0.18 ± 0.05), resulting in slightly higher CH₄ production when selecting for better fertility [days open estimated breeding values (EBV) are expressed with mean 100 and SD 10, inversely related to days from calving to conception; that is, greater days open EBV implies better fertility]. Positive correlations were also estimated for stature with MeC and MeP (0.30 ± 0.04 and 0.43 ± 0.04, respectively). Other type traits (chest width, udder depth, angularity, and capacity) were positively correlated with methane traits, possibly because of higher milk yield and higher feed intake from these animals. Rumination time showed positive EBV correlations with production traits and type traits, and negative correlations with somatic cell count and body condition score. Based on the genetic correlations and heritabilities estimated in this study, methane is measurable and heritable, and estimates of genetic correlations suggest no strong opposition to current breeding objectives in Spanish Holsteins.

Can We Observe Expected Behaviors at Large and Individual Scales for Feed Efficiency-Related Traits Predicted Partly from Milk Mid-Infrared Spectra?

Phenotypes related to feed efficiency were predicted from records easily acquired by breeding organizations. A total of 461,036 and 354,148 records were collected from the first and second parity Holstein cows. Equations were applied to the milk mid-infrared spectra to predict the main milk components and coupled with animal characteristics to predict the body weight (pBW). Dry matter intake (pDMI) was predicted from pBW using the National Research Council (NRC) equation. The consumption index (pIC) was estimated from pDMI and fat, and protein corrected milk. All traits were modeled using single trait test-day models. Descriptive statistics were within the expected range. Milk yield, pDMI, and pBW were phenotypically positively related (r ranged from 0.08 to 0.64). As expected, pIC was phenotypically negatively correlated with milk yield (-0.77 and -0.80 for the first and second lactation) and slightly positively correlated with pBW (0.16 and 0.07 for the first and second lactation). Later, parity cows seemed to have a better feed efficiency as they had a lower pIC. Although the prediction accuracy was moderate, the observed behaviors of studied traits by year, stage of lactation, and parity were in agreement with the literature. Moreover, as a genetic component was highlighted (heritability around 0.18), it would be interesting to realize a genetic evaluation of these traits and compare the obtained breeding values with the ones estimated for sires having daughters with reference feed efficiency records.

Accuracy of methane emissions predicted from milk mid-infrared spectra and measured by laser methane detectors in Brown Swiss dairy cows

Since heritability of CH₄ emissions in ruminants was demonstrated, various attempts to generate large individual animal CH₄ data sets have been initiated. Predicting individual CH₄ emissions based on equations using milk mid-infrared (MIR) spectra is currently considered promising as a low-cost proxy. However, the CH₄ emission predicted by MIR in individuals still has to be confirmed by measurements. In addition, it remains unclear how low CH₄ emitting cows differ in intake, digestion, and efficiency from high CH₄ emitters. In the current study, putatively low and putatively high CH₄ emitting Brown Swiss cows were selected from the entire Swiss herdbook population (176,611 cows), using an MIR-based prediction equation. Eventually, 15 low and 15 high CH₄ emitters from 29 different farms were chosen for a respiration chamber (RC) experiment in which all cows were fed the same forage-based diet. Several traits related to intake, digestion, and efficiency were quantified over 8 d, and CH₄ emission was measured in 4 open circuit RC. Daily CH₄ emissions were also estimated using data from 2 laser CH₄ detectors (LMD). The MIR-predicted CH₄ production (g/d) was quite constant in low and high emission categories, in individuals across sites (home farm, experimental station), and within equations (first available and refined versions). The variation of the MIR-predicted values was substantially lower using the refined equation. However, the predicted low and high emitting cows (n = 28) did not differ on average in daily CH₄ emissions measured either with RC or estimated using LMD, and no correlation was found between CH₄ predictions (MIR) and CH₄ emissions measured in RC. When individuals were recategorized based on CH₄ yield measured in RC, differences between categories of 10 low and 10 high CH₄ emitters were about 20%. Low CH₄ emitting cows had a higher feed intake, milk yield, and residual feed intake, but they differed only weakly in eating pattern and digesta mean retention times. Low CH₄ emitters were characterized by lower acetate and higher propionate proportions of total ruminal volatile fatty acids. We concluded that the current MIR-based CH₄ predictions are not accurate enough to be implemented in breeding programs for cows fed forage-based diets. In addition, low CH₄ emitting cows have to be characterized in more detail using mechanistic studies to clarify in more detail the properties that explain the functional differences found in comparison with other cows.

Structural equation models to disentangle the biological relationship between microbiota and complex traits: Methane production in dairy cattle as a case of study

The advent of metagenomics in animal breeding poses the challenge of statistically modelling the relationship between the microbiome, the host genetics and relevant complex traits. A set of structural equation models (SEMs) of a recursive type within a Markov chain Monte Carlo (MCMC) framework was proposed here to jointly analyse the host-metagenome-phenotype relationship. A non-recursive bivariate model was set as benchmark to compare the recursive model. The relative abundance of rumen microbes (RA), methane concentration (CH₄ ) and the host genetics was used as a case of study. Data were from 337 Holstein cows from 12 herds in the north and north-west of Spain. Microbial composition from each cow was obtained from whole metagenome sequencing of ruminal content samples using a MinION device from Oxford Nanopore Technologies. Methane concentration was measured with Guardian^® NG infrared gas monitor from Edinburgh Sensors during cow's visits to the milking automated system. A quarterly average from the methane eructation peaks for each cow was computed and used as phenotype for CH₄ . Heritability of CH₄ was estimated at 0.12 ± 0.01 in both the recursive and bivariate models. Likewise, heritability estimates for the relative abundance of the taxa overlapped between models and ranged between 0.08 and 0.48. Genetic correlations between the microbial composition and CH₄ ranged from -0.76 to 0.65 in the non-recursive bivariate model and from -0.68 to 0.69 in the recursive model. Regardless of the statistical model used, positive genetic correlations with methane were estimated consistently for the seven genera pertaining to the Ciliophora phylum, as well as for those genera belonging to the Euryarchaeota (Methanobrevibacter sp.), Chytridiomycota (Neocallimastix sp.) and Fibrobacteres (Fibrobacter sp.) phyla. These results suggest that rumen's whole metagenome recursively regulates methane emissions in dairy cows and that both CH₄ and the microbiota compositions are partially controlled by the host genotype.

Persistence of differences between dairy cows categorized as low or high methane emitters, as estimated from milk mid-infrared spectra and measured by GreenFeed

Currently, various attempts are being made to implement breeding schemes aimed at producing low methane (CH₄) emitting cows. We investigated the persistence of differences in CH₄ emission between groups of cows categorized as either low or high emitters over a 5-mo period. Two feeding regimens (pasture vs. indoors) were used. Early- to mid-lactation Holstein Friesian cows were categorized as low or high emitters (n = 10 each) retrospectively, using predictions from milk mid-infrared (MIR) spectra, before the start of the experiment. Data from MIR estimates and from measurements with the GreenFeed (GF; C-Lock Technology Inc., Rapid City, SD) system over the 5-mo experiment were combined into 7-, 14-, and 28-d periods. Feed intake, eating and ruminating behavior, and ruminal fluid traits were determined in two 7-d measurement periods in the grazing season. The CH₄ emission data were analyzed using a split-plot ANOVA, and the repeatability of each of the applied methods for determining CH₄ emission was calculated. Traits other than CH₄ emission were analyzed for differences between low and high emitters using a linear mixed model. The initial category-dependent differences in daily CH₄ production persisted over the subsequent 5 mo and across 2 feeding regimens with both methods. The repeatability analysis indicated that the biweekly milk control scheme, and even a monthly scheme as practiced on farms, might be sufficient for confirming category differences. However, the relationship between CH₄ data estimated by MIR and measured with GF for individual cows was weak (R² = 0.26). The categorization based on CH₄ production also generated differences in CH₄ emission per kilogram of milk; differentiation between cow categories was not persistent based on milk MIR spectra and GF. Compared with the high emitters, low emitters tended to show a lower acetate-to-propionate ratio in ruminal volatile fatty acids, whereas feed intake and ruminating time did not differ. Interestingly, the low emitters spent less time eating than the high emitters. In conclusion, the CH₄ estimation from analyzing the milk MIR spectra is an appropriate proxy to form and regularly control categories of cows with different CH₄ production levels. The categorization was also sufficient to secure similar and persistent differences in emission intensity when estimated by MIR spectra of the milk. Further studies are needed to determine whether MIR data from individual cows are sufficiently accurate for breeding.

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

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

Comparison Between Non-Invasive Methane Measurement Techniques in Cattle

Simple Summary

Enteric methane emissions pose a serious issue to ruminant production and environmental sustainability. To mitigate methane emissions, combined research efforts have been put into animal handling, feeding and genetic improvement strategies. For all research efforts, it is necessary to record methane emissions from individual cows on a large scale under farming conditions. The objective of this trial was to compare two large-scale, non-invasive methods of measuring methane (non-dispersive infrared methane analyzer (NDIR) and laser), in order to see if they can be used interchangeably. For this, paired measurements were taken with both devices on a herd of dairy cows and compared. Significant sources of disagreement were identified between the methods, such that it would not be possible to use both methods interchangeably without first correcting the sources of disagreement.

Abstract

The aim of this trial was to study the agreement between the non-dispersive infrared methane analyzer (NDIR) method and the hand held laser methane detector (LMD). Methane (CH₄) was measured simultaneously with the two devices totaling 164 paired measurements. The repeatability of the CH₄ concentration was greater with the NDIR (0.42) than for the LMD (0.23). However, for the number of peaks, repeatability of the LMD was greater (0.20 vs. 0.14, respectively). Correlation was moderately high and positive for CH₄ concentration (0.73 and 0.74, respectively) and number of peaks (0.72 and 0.72, respectively), and the repeated measures correlation and the individual-level correlation were high (0.98 and 0.94, respectively). A moderate concordance correlation coefficient was observed for the CH₄ concentration (0.62) and for the number of peaks (0.66). A moderate-high coefficient of individual agreement for the CH₄ concentration (0.83) and the number of peaks (0.77) were observed. However, CH₄ concentrations population means and all variance components differed between instruments. In conclusion, methane concentration measurements obtained by means of NDIR and LMD cannot be used interchangeably. The joint use of both methods could be considered for genetic selection purposes or for mitigation strategies only if sources of disagreement, which result in different between-subject and within-subject variabilities, are identified and corrected for.

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

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

A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions

A 1000-cow study across four European countries was undertaken to understand to what extent ruminant microbiomes can be controlled by the host animal and to identify characteristics of the host rumen microbiome axis that determine productivity and methane emissions. A core rumen microbiome, phylogenetically linked and with a preserved hierarchical structure, was identified. A 39-member subset of the core formed hubs in co-occurrence networks linking microbiome structure to host genetics and phenotype (methane emissions, rumen and blood metabolites, and milk production efficiency). These phenotypes can be predicted from the core microbiome using machine learning algorithms. The heritable core microbes, therefore, present primary targets for rumen manipulation toward sustainable and environmentally friendly agriculture.

Short communication: Heritability of methane production and genetic correlations with milk yield and body weight in Holstein-Friesian dairy cows

Greenhouse gases originating from the dairy sector, including methane (CH₄), contribute to global warming. A possible strategy to reduce CH₄ production is to use genetic selection. This requires genetic parameters for CH₄ production and correlations with production traits. Data were available on 184 Holstein-Friesian cows. Methane production was measured in the milking robot during milking from December 2009 to April 2010. In total 2,456 observations for CH₄ production were available. Milk yield (MY) and body weight (BW) were obtained at every milking from November 2008 to October 2010. In total 4,567 observations for milk yield and 4,570 observations for BW were available. Restricted maximum likelihood, using random regression models, was used to analyze the data. Heritability (standard error given in parentheses) for CH₄ production ranged from 0.12 (0.16) to 0.45 (0.11), and genetic correlations with MY ranged from 0.49 (0.12) to 0.54 (0.26). The positive genetic correlation between CH₄ production and milk yield indicates that care needs to be taken when genetically selecting for lower CH₄ production, to avoid a decrease in MY at the animal level. However, this study shows that CH₄ production is moderately heritable and therefore progress through genetic selection is possible.

Validation of mobile in situ measurements of dairy husbandry emissions by fusion of airborne/surface remote sensing with seasonal context from the Chino Dairy Complex

Mobile in situ concentration and meteorology data were collected for the Chino Dairy Complex in the Los Angeles Basin by AMOG (AutoMObile trace Gas) Surveyor on 25 June 2015 to characterize husbandry emissions in the near and far field in convoy mode with MISTIR (Mobile Infrared Sensor for Tactical Incident Response), a mobile upwards-looking, column remote sensing spectrometer. MISTIR reference flux validated AMOG plume inversions at different information levels including multiple gases, GoogleEarth imagery, and airborne trace gas remote sensing data. Long-term (9-yr.) Infrared Atmospheric Sounding Interferometer satellite data provided spatial and trace gas temporal context. For the Chino dairies, MISTIR-AMOG ammonia (NH₃) agreement was within 5% (15.7 versus 14.9 Gg yr^-1, respectively) using all information. Methane (CH₄) emissions were 30 Gg yr^-1 for a 45,200 herd size, indicating that Chino emission factors are greater than previously reported. Single dairy inversions were much less successful. AMOG-MISTIR agreement was 57% due to wind heterogeneity from downwind structures in these near-field measurements and emissions unsteadiness. AMOG CH₄, NH₃, and CO₂ emissions were 91, 209, and 8200 Mg yr^-1, implying 2480, 1870, and 1720 head using published emission factors. Plumes fingerprinting identified likely sources including manure storage, cowsheds, and a structure with likely natural gas combustion. NH₃ downwind of Chino showed a seasonal variation of a factor of ten, three times larger than literature suggests. Chino husbandry practices and trends in herd size and production were reviewed and unlikely to add seasonality. Higher emission seasonality was proposed as legacy soil emissions, the results of a century of husbandry, supported by airborne remote sensing data showing widespread emissions from neighborhoods that were dairies 15 years prior, and AMOG and MISTIR observations. Seasonal variations provide insights into the implications of global climate change and must be considered when comparing surveys from different seasons.

Impact of longevity on greenhouse gas emissions and profitability of individual dairy cows analysed with different system boundaries

Dairy production systems are often criticized as being major emitters of greenhouse gases (GHG). In this context, the extension of the length of the productive life of dairy cows is gaining interest as a potential GHG mitigation option. In the present study, we investigated cow and system GHG emission intensity and profitability based on data from 30 dairy cows of different productive lifetime fed either no or limited amounts of concentrate. Detailed information concerning productivity, feeding and individual enteric methane emissions of the individuals was available from a controlled experiment and herd book databases. A simplified GHG balance was calculated for each animal based on the milk produced at the time of the experiment and for their entire lifetime milk production. For the lifetime production, we also included the emissions arising from potential beef produced by fattening the offspring of the dairy cows. This accounted for the effect that changes in the length of productive life will affect the replacement rate and thus the number of calves that can be used for beef production. Profitability was assessed by calculating revenues and full economic costs for the cows in the data set. Both emission intensity and profitability were most favourable in cows with long productive life, whereas cows that had not finished their first lactation performed particularly unfavourably with regard to their emissions per unit of product and rearing costs were mostly not repaid. Including the potential beef production, GHG emissions in relation to total production of animal protein also decreased with age, but the overall variability was greater, as the individual cow history (lifetime milk yield, twin births, stillbirths, etc.) added further sources of variation. The present results show that increasing the length of productive life of dairy cows is a viable way to reduce the climate impact and to improve profitability of dairy production.

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

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

Variations in methane yield and microbial community profiles in the rumen of dairy cows as they pass through stages of first lactation

Considerable interest exists both from an environmental and economic perspective in reducing methane emissions from agriculture. In ruminants, CH₄ is produced by a complex community of microorganisms that is established in early life but can be influenced by external factors such as feed. Although CH₄ emissions were thought to be constant once an animal reached maturity, recent studies have shown that CH₄ yield significantly increases from early to late lactation in dairy cows. The aim of this study was to test the hypothesis that increases in CH₄ yield over the lactation cycle are related to changes in rumen microbial community structure. Nine cows were monitored throughout their first lactation cycle. Methane and dry matter intake were measured to calculate CH₄ per dry matter intake (CH₄ yield) and ruminal fluid was collected during early, mid, and late lactation. A significant difference in bacterial and archaeal community structure during early and late lactation was observed. Furthermore, when ruminal short-chain fatty acid concentrations were measured, the ratio of acetate and butyrate to propionate was significantly higher in late lactation compared with early lactation. Propionate concentrations were higher in cows with low CH₄ yield during late lactation, but no differences were observed in bacterial or archaeal community structures. Prevotella dominated the rumen of cows followed by Succinclasticum; Treponema, Fibrobacter, Ruminococcus, and Bifidobacterium were also in high abundance relative to other bacterial genera. In general, positive correlations were stronger between the most relatively abundant bacterial genera and acetate and butyrate concentrations in the cows with high CH₄ and weaker between these genera and propionate concentration. This study indicates that increased CH₄ yield in late lactation is reflected in significant changes in microbial community structure.

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.

Does the diurnal pattern of enteric methane emissions from dairy cows change over time?

Diet manipulation and genetic selection are two important mitigation strategies for reducing enteric methane (CH4) emissions from ruminant livestock. The aim of this study was to assess whether the diurnal pattern of CH4 emissions from individual dairy cows changes over time when cows are fed on diets varying in forage composition. Emissions of CH4 from 36 cows were measured during milking in an automatic (robotic) milking station in three consecutive feeding periods, for a total of 84 days. In Periods 1 and 2, the 36 cows were fed a high-forage partial mixed ration (PMR) containing 75% forage, with either a high grass silage or high maize silage content. In Period 3, cows were fed a commercial PMR containing 69% forage. Cows were offered PMR ad libitum plus concentrates during milking and CH4 emitted by individual cows was sampled during 8662 milkings. A linear mixed model was used to assess differences among cows, feeding periods and time of day. Considerable variation was observed among cows in daily mean and diurnal patterns of CH4 emissions. On average, cows produced less CH4 when fed on the commercial PMR in feeding Period 3 than when the same cows were fed on high-forage diets in feeding Periods 1 and 2. The average diurnal pattern for CH4 emissions did not significantly change between feeding periods and as lactation progressed. Emissions of CH4 were positively associated with dry matter (DM) intake and forage DM intake. It is concluded that if the management of feed allocation remains constant then the diurnal pattern of CH4 emissions from dairy cows will not necessarily alter over time. A change in diet composition may bring about an increase or decrease in absolute emissions over a 24-h period without significantly changing the diurnal pattern unless management of feed allocation changes. These findings are important for CH4 monitoring techniques that involve taking measurements over short periods within a day rather than complete 24-h observations.

Relationships between milk mid-IR predicted gastro-enteric methane production and the technical and financial performance of commercial dairy herds

Considering economic and environmental issues is important in ensuring the sustainability of dairy farms. The objective of this study was to investigate univariate relationships between lactating dairy cow gastro-enteric methane (CH4) production predicted from milk mid-IR (MIR) spectra and technico-economic variables by the use of large scale and on-farm data. A total of 525 697 individual CH4 predictions from milk MIR spectra (MIR-CH4 (g/day)) of milk samples collected on 206 farms during the Walloon milk recording scheme were used to create a MIR-CH4 prediction for each herd and year (HYMIR-CH4). These predictions were merged with dairy herd accounting data. This allowed a simultaneous study of HYMIR-CH4 and 42 technical and economic variables for 1024 herd and year records from 2007 to 2014. Pearson correlation coefficients (r) were used to assess significant relationships (P<0.05). Low HYMIR-CH4 was significantly associated with, amongst others, lower fat and protein corrected milk (FPCM) yield (r=0.18), lower milk fat and protein content (r=0.38 and 0.33, respectively), lower quantity of milk produced from forages (r=0.12) and suboptimal reproduction and health performance (e.g. longer calving interval (r=-0.21) and higher culling rate (r=-0.15)). Concerning economic results, low HYMIR-CH4 was significantly associated with lower gross margin per cow (r=0.19) and per litre FPCM (r=0.09). To conclude, this study suggested that low lactating dairy cow gastro-enteric CH4 production tended to be associated with more extensive or suboptimal management practices, which could lead to lower profitability. The observed low correlations suggest complex interactions between variables due to the use of on-farm data with large variability in technical and management practices.