Unraveling the genetic basis of methane emission in dairy cattle: a comprehensive exploration and breeding approach to lower methane emissions

Ruminant animals, such as dairy cattle, produce CH4, which contributes to global warming emissions and reduces dietary energy for the cows. While the carbon foot print of milk production varies based on production systems, milk yield and farm management practices, enteric fermentation, and manure management are major contributors togreenhouse gas emissions from dairy cattle. Recent emerging evidence has revealed the existence of genetic variation for CH4 emission traits among dairy cattle, suggests their potential inclusion in breeding goals and genetic selection programs. Advancements in high-throughput sequencing technologies and analytical techniques have enabled the identification of potential metabolic biomarkers, candidate genes, and SNPs linked to methane emissions. Indeed, this review critically examines our current understanding of carbon foot print in milk production, major emission sources, rumen microbial community and enteric fermentation, and the genetic architecture of methane emission traits in dairy cattle. It also emphasizes important implications for breeding strategies aimed at halting methane emissions through selective breeding, microbiome driven breeding, breeding for feed efficiency, and breeding by gene editing.

Review of equations to predict methane emissions in dairy cows from milk fatty acid profiles and their application to commercial dairy farms

Greenhouse gas emission from the activities of all productive sectors is currently a topic of foremost importance. The major contributors in the livestock sector are ruminants, especially dairy cows. This study aimed to evaluate and compare 21 equations for predicting enteric methane emissions (EME) developed on the basis of milk traits and fatty acid profiles, which were selected from 46 retrieved through a literature review. We compiled a reference database of the detailed fatty acid profiles, determined by GC, of 992 lactating cows from 85 herds under 4 different dairy management systems. The cows were classified according to DIM, parity order, and dairy system. This database was the basis on which we estimated EME using the selected equations. The EME traits estimated were methane yield (20.63 ± 2.26 g/kg DMI, 7 equations), methane intensity (16.05 ± 2.76 g/kg of corrected milk, 4 equations), and daily methane production (385.4 ± 68.2 g/d, 10 equations). Methane production was also indirectly calculated by multiplying the daily corrected milk yield by the methane intensity (416.6 ± 134.7 g/d, 4 equations). We also tested for the effects of DIM, parity, and dairy system (as a correction factor) on the estimates. In general, we observed little consistency among the EME estimates obtained from the different equations, with exception of those obtained from meta-analyses of a range of data from different research centers. We found all the EME predictions to be highly affected by the sources of variation included in the statistical model: DIM significantly affected the results of 19 of the 21 equations, and parity order influenced the results of 13. Different patterns were observed for different equations with only some of them in accordance with expectations based on the cow's physiology. Finally, the best predictions of daily methane production were obtained when a measure of milk yield was included in the equation or when the estimate was indirectly calculated from daily milk yield and methane intensity.

Effect of carbohydrate type in silages and concentrates on feed intake, enteric methane and milk yield from dairy cows

Dietary carbohydrate manipulation can be used to reduce enteric CH4 emission, but there is a lack of studies on the interaction of different types of carbohydrates that can affect feed intake and ruminal fermentation. Understanding this interaction is necessary to make the most out of CH4 mitigation feeding strategies using different dietary carbohydrates. The aim of this study was to test the effect on enteric CH4 emission, feed intake and milk production response when cows were fed either grass-clover (GCS) or corn silage (CS) as the sole forage source (55% of dry matter, DM), in combination with either barley (BAR) or dried beet pulp (DBP) as a concentrate (21.5% of DM). Twenty-four (half first and half second parity) cows were used in a crossover design with 2 periods of 21 d each, receiving 2 of 4 diets obtained from a 2 × 2 factorial arrangement of the experimental diet. Feed intake, CH4 emission metrics and milk production were recorded at the end of the experimental periods. The diets had NDF concentrations between 258 and 340 g/kg of DM, and starch concentrations between 340 and 7.45 g/kg of DM (CS-BAR and GCS-DBP, respectively). The effects of silage and concentrate on dry matter intake (DMI) were additive, with the highest feed intake in cows fed COR-BAR, followed by cows fed COR-DBP, GCS-BAR, and GCS-DBP (21.2, 19.9, 19.1, and 18.3 kg/d). Energy corrected milk (ECM) yield was not affected by silage source in first parity cows, but it was higher for cows fed CS than cows fed GCS in second parity. The effects of silage and concentrate on CH4 production (g/d), yield (g/kg of DMI) and intensity (g/kg of ECM) were not additive as cows fed GCS had similar responses regardless of the concentrate used, but cows fed CS had lower CH4 production, yield and intensity, when fed BAR instead of DBP. The lower CH4 production, yield and intensity in cows fed CS-BAR compared with other diets could be partially explained by the nonlinear relationship between ruminal VFA and carbohydrates (NDF and starch) concentration reported in literature, however, we observed a linear relationship between acetate:propionate ratio and CH4 yield, suggesting possible other effects. The effects of silage and concentrate on the ruminal VFA were additive in first parity cows, but not in second parity cows. The interaction between dietary CHO type and parity might indicate an effect of feed intake or the energy balance of the cow. Feeding cows silage and concentrate both rich in starch can result in the lowest enteric CH4 emission.

Ruminal microbiome changes across lactation in primiparous Holstein cows with varying methane intensity: Heritability assessment

Methane is a potent greenhouse gas produced during the ruminal fermentation and is associated with a loss of feed energy. Therefore, efforts to reduce methane emissions have been ongoing in the last decades. Methane production is highly influenced by factors such as the ruminal microbiome and host genetics. Previous studies have proposed to use the ruminal microbiome to reduce long-term methane emissions, as ruminal microbiome composition is a moderately heritable trait and genetic improvement accumulates over time. Lactation stage is another important factor that might influence methane production but potential associations with the ruminal microbiome have not been evaluated previously. This study sought to examine the changes in ruminal microbiome over the lactation period of primiparous Holstein cows differing in methane intensity and estimate the heritability of the abundance of relevant microorganisms. Ruminal content samples from 349 primiparous Holstein cows with 14 - 378 d in milk were collected from May 2018 to June 2019. Methane intensity (MI) of each cow was calculated as methane concentration/milk yield. Up to 64 taxonomic features (TF) from 20 phyla had a significant differential abundance between cows with low and high MI early in lactation, 16 TF during mid lactation, and none late in lactation. Taxonomical features within the Firmicutes, Proteobacteria, Melainabacteria, Cyanobacteria, Bacteroidetes and Actinobacteria phyla were associated to low MI, whereas eukaryotic TF and those within the Euryarchaeota, Verrucomicrobia, Kiritimatiellaeota, Lentisphaerae phyla were associated to high MI. Out of the 60 TF that were found to be differentially abundant between early and late lactation in cows with low MI, 56 TF were also significant when cows with low and high MI were compared in the first third of the lactation. In general, microbes associated with low MI were more abundant early in lactation (e.g., Acidaminococcus, Aeromonas and Weimeria genera) and showed low to moderate heritabilities (0.03 to 0.33). These results suggest some potential to modulate the rumen microbiome composition through selective breeding for lower MI. Differences in the ruminal microbiome of cows with extreme MI levels likely result from variations in the ruminal physiology of these cows and were more noticeable early in lactation probably due to important interactions between the host phenotype and environmental factors associated to that period. Our results suggest that the ruminal microbiome evaluated early in lactation may be more precise for MI difference, and hence, this should be considered to optimize sampling periods to establish a reference population in genomic selection scenarios.

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

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

Estimation of genetic parameters and single-step genome-wide association studies for eating time and rumination time in Holstein dairy cows

The aims of this study were to estimate genetic parameters and to identify genomic regions associated with eating time (ET) and rumination time (RUT) in Holstein dairy cows. Genetic correlations among ET, RUT, and milk yield traits were also estimated. The data were collected from 2019 to 2022 in 6 dairy herds located in the Walloon Region of Belgium. The dataset consisted of daily ET and RUT records on 284 Holstein cows, from which 41 cows had records only for the first parity (P1), 101 cows had records from both the first and second parities, and 142 cows had records only for the second parity (P2). The number of daily ET and RUT records in the P1 and P2 cows were 18,569 (on 142 cows) and 34,464 (on 243 cows), respectively. Data on 28,994 SNPs located on 29 Bos taurus autosomes (BTA) of 747 animals (435 males) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by each 20-SNP sliding window (with an average size of 1.52 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Mean (standard deviation; SD) averaged daily ET and RUT were 327.0 (85.66) and 559.4 (77.69) min/d for cows in P1 and 316.0 (82.24) and 574.2 (75.42) min/d for cows in P2, respectively. Mean (standard deviation; SD) heritability estimates for daily ET and RUT were 0.42 (0.09) and 0.45 (0.06) for cows in P1 and 0.45 (0.04) and 0.43 (0.02) for cows in P2, respectively. Mean (SD) daily genetic correlations between daily ET and RUT were 0.27 (0.07) for P1 and 0.34 (0.08) for P2. Genome-wide association analyses identified 6 genomic regions distributed over 5 chromosomes (BTA1, BTA4, BTA11, 2 regions of BTA14, and BTA17) associated with ET or RUT. The findings of this study increase our preliminary understanding of the genetic background of feeding behavior in dairy cows; however, larger datasets are needed to determine whether ET and RUT might have the potential to be used in selection programs.

Lactation modeling and the effects of rotational crossbreeding on milk production traits and milk-spectra-predicted enteric methane emissions

Rotational crossbreeding has not been widely studied in relation to the enteric methane emissions of dairy cows, nor has the variation in emissions during lactation been modeled. Milk infrared spectra could be used to predict proxies of methane emissions in dairy cows. Therefore, the objective of this work was to study the effects of crossbreeding on the predicted infrared proxies of methane emissions and the variation in the latter during lactation. Milk samples were taken once from 1,059 cows reared in 2 herds, and infrared spectra of the milk were used to predict milk fat (mean ± SD; 3.79 ± 0.81%) and protein (3.68 ± 0.36%) concentrations, yield (21.4 ± 1.5 g/kg dry matter intake), methane intensity (14.2 ± 2.0 g/kg corrected milk), and daily methane production (358 ± 108 g/d). Of these cows, 620 were obtained from a 3-breed (Holstein, Montbéliarde, and Viking Red) rotational mating system, and the rest were purebred Holsteins. Milk production data and methane traits were analyzed using a nonlinear model that included the fixed effects of herd, genetic group, and parity, and the 4 parameters (a, b, c, and k) of a lactation curve modeled using the Wilmink function. Milk infrared spectra were found to be useful for direct prediction of qualitative proxies, such as methane yield and intensity, but not quantitative traits, such as daily methane production, which appears to be better estimated (450 ± 125 g/d) by multiplying a measured daily milk yield by infrared-predicted methane intensity. Lactation modeling of methane traits showed daily methane production to have a zenith curve, similar to that of milk yield but with a delayed peak (53 vs. 37 d in milk), whereas methane intensity is characterized by an upward curve that increases rapidly during the first third of lactation and then slowly till the end of lactation (10.5 g/kg at 1 d in milk to 15.2 g/kg at 300 d in milk). However, lactation modeling was not useful in explaining methane yield, which is almost constant during lactation. Lastly, the methane yield and intensity of cows from 3-breed rotational crossbreeding are not greater, and their methane production is lower than that of purebred Holsteins (452 vs. 477 g/d). Given the greater longevity of crossbred cows, and their lower replacement rate, rotational crossbreeding could be a way of mitigating the environmental impact of milk production.

Predicting methane emissions of individual grazing dairy cows from spectral analyses of their milk samples

Data on the enteric methane emissions of individual cows are useful not just in assisting management decisions and calculating herd inventories but also as inputs for animal genetic evaluations. Data generation for many animal characteristics, including enteric methane emissions, can be expensive and time consuming, so being able to extract as much information as possible from available samples or data sources is worthy of investigation. The objective of the present study was to attempt to predict individual cow methane emissions from the information contained within milk samples, specifically the spectrum of light transmittance across different wavelengths of the mid-infrared (MIR) region of the electromagnetic spectrum. A total of 93,888 individual spot measures of methane (i.e., individual samples of an animal's breath when using the GreenFeed technology) from 384 lactations on 277 grazing dairy cows were collapsed into weekly averages expressed as grams per day; each weekly average coincided with a MIR spectral analysis of a morning or evening individual cow milk sample. Associations between the spectra and enteric methane measures were performed separately using partial least squares regression or neural networks with different tuning parameters evaluated. Several alternative definitions of the enteric methane phenotype (i.e., average enteric methane in the 6 d preceding or 6 d following taking the milk sample or the average of the 6 d before and after the milk sample, all of which also included the enteric methane emitted on the day of milk sampling), the candidate model features (e.g., milk yield, milk composition, and milk MIR) as well as validation strategy (i.e., cross-validation or leave-one-experimental treatment-out) were evaluated. Irrespective of the validation method, the prediction accuracy was best when the average of the milk MIR from the morning and evening milk sample was used and the prediction model was developed using neural networks; concurrently including milk yield and days in milk in the prediction model generated superior predictions relative to just the spectral information alone. Furthermore, prediction accuracy was best when the enteric methane phenotype was the average of at least 20 methane spot measures across a 6-d period flanking each side of the milk sample with associated spectral data. Based on the strategy that achieved the best accuracy of prediction, the correlation between the actual and predicted daily methane emissions when based on 4-fold cross-validation varied per validation stratum from 0.68 to 0.75; the corresponding range when validated on each of the 8 different experimental treatments focusing on alternative pasture grazing systems represented in the dataset varied from 0.55 to 0.71. The root mean square error of prediction across the 4-folds of cross-validation was 37.46 g/d, whereas the root mean square error averaged across all folds of leave-one-treatment-out was 37.50 g/d. Results suggest that even with the likely measurement errors contained within the MIR spectrum and gold standard enteric methane phenotype, enteric methane can be reasonably well predicted from the infrared spectrum of milk samples. What is yet to be established, however, is whether (a) genetic variation exists in this predicted enteric methane phenotype and (b) selection on estimates of genetic merit for this phenotype translate to actual phenotypic differences in enteric methane emissions.

Taking the diet of cows into consideration in designing payments to reduce enteric methane emissions on dairy farms

Enteric fermentation from dairy cows is a major source of methane. Significantly and rapidly reducing those emissions would be a powerful lever to mitigate climate change. For a given productivity level, introducing fodder with high sources of n-3 content, such as grass or linseed, in the feed ration of dairy cows both improves the milk nutritional profile and reduces enteric methane emissions per liter. Changing cows' diet may represent additional costs for dairy farmers and calls for the implementation of payments for environmental services to support the transition. This paper analyzes 2 design elements influencing the effectiveness of a payment conditioned toward the reduction of enteric methane emissions: (1) the choice of emission indicator capturing the effect of farmers' practices, and (2) the payment amount relative to the additional milk production costs incurred. Using representative farm-level economic data from the French farm accountancy data network, we compare enteric methane emissions per liter of milk calculated with an Intergovernmental Panel on Climate Change Tier 2 method, to baseline emissions from a Tier 3 method accounting for diet effects. We also quantify the additional milk production costs of integrating more grass in the fodder systems by estimating variable cost functions for different dairy systems in France. Our results show the relevance of using an emission indicator sensitive to diet effects, and that the significance and direction of the additional costs for producing milk with a diet containing more grass differ according to the production basin and the current share of grasslands in the fodder crop rotation. We emphasize the importance of developing payments for environmental services with well-defined environmental indicators accounting for the technical problems addressed, and the need to better characterize heterogeneous funding requirements for supporting a large-scale adoption of more environment-friendly practices by farmers.

Technology for Carbon Neutral Animal Breeding

Animal breeding techniques are to genetically select highly productive animals with less GHG emission intensity, thereby reducing the number of animals required to produce the same amount of food. Shotgun metagenomics provides a platform to identify rumen microbial communities and genetic markers associated with CH4 emissions, allowing the selection of cattle with less CH4 emissions. Moreover, breeding is a viable option to make real progress towards carbon neutrality with a very high rate of return on investment and a very modest cost per tonne of CO2 equivalents saved regardless of the accounting method. Other high technologies include the use of cloned livestock animals and the manipulation of traits by controlling target genes with improved productivity.

Continuous real-time cow identification by reading ear tags from live-stream video

In precision dairy farming there is a need for continuous and real-time availability of data on cows and systems. Data collection using sensors is becoming more common and it can be difficult to connect sensor measurements to the identification of the individual cow that was measured. Cows can be identified by RFID tags, but ear tags with identification numbers are more widely used. Here we describe a system that makes the ear tag identification of the cow continuously available from a live-stream video so that this information can be added to other data streams that are collected in real-time. An ear tag reading model was implemented by retraining and existing model, and tested for accuracy of reading the digits on cows ear tag images obtained from two dairy farms. The ear tag reading model was then combined with a video set up in a milking robot on a dairy farm, where the identification by the milking robot was considered ground-truth. The system is reporting ear tag numbers obtained from live-stream video in real-time. Retraining a model using a small set of 750 images of ear tags increased the digit level accuracy to 87% in the test set. This compares to 80% accuracy obtained with the starting model trained on images of house numbers only. The ear tag numbers reported by real-time analysis of live-stream video identified the right cow 93% of the time. Precision and sensitivity were lower, with 65% and 41%, respectively, meaning that 41% of all cow visits to the milking robot were detected with the correct cow’s ear tag number. Further improvement in sensitivity needs to be investigated but when ear tag numbers are reported they are correct 93% of the time which is a promising starting point for future system improvements.

Assessment of Seasonal Variation in Methane Emissions of Mediterranean Buffaloes Using a Laser Methane Detector

A direct assessment of the methane (CH4) emission level and its variability factors is needed in each animal species in order to target the best mitigation strategy for the livestock sector. Therefore, the present study aimed to (1) test a laser methane detector (LMD) for the first time in Italian Mediterranean buffaloes (IMB), a non-invasive tool to quantify CH4 emissions; (2) test the effect of season on the emissions; and (3) compare the results measured directly with the ones estimated with the existing equations. CH4 emissions of twenty non-productive IMB, under the same feeding regimen, were monitored for 12 days in summer and winter. Significantly higher THI (74.46 ± 1.88 vs. 49.62 ± 4.87; p < 0.001), lower DMI (2.24 ± 0.04 vs. 2.51 ± 0.03% DMI/kg live weight; p < 0.001) and lower emission intensities (0.61 ± 0.15 vs. 0.75 ± 0.13; p < 0.001) were found during the summer period when compared with winter. LMD was found to be a versatile tool to be used in buffaloes, and it was clear that a summer increase in THI could act as a stressor for the animals, influencing their emissions. In addition, measured emissions were significantly higher than when estimated with the existing equations (p < 0.001), suggesting the need for further research in this area.

Animal board invited review: Genomic-based improvement of cattle in response to climate change

Climate change brings challenges to cattle production, such as the need to adapt to new climates and pressure to reduce greenhouse emissions (GHG). In general, the improvement of traits in current breeding goals is favourably correlated with the reduction of GHG. Current breeding goals and tools for increasing cattle production efficiency have reduced GHG. The same amount of production can be achieved by a much smaller number of animals. Genomic selection (GS) may offer a cost-effective way of using an efficient breeding approach, even in low- and middle-income countries. As climate change increases the intensity of heatwaves, adaptation to heat stress leads to lower efficiency of production and, thus, is unfavourable to the goal of reducing GHG. Furthermore, there is evidence that heat stress during cow pregnancy can have many generation-long lowering effects on milk production. Both adaptation and reduction of GHG are among the difficult-to-measure traits for which GS is more efficient and suitable than the traditional non-genomic breeding evaluation approach. Nevertheless, the commonly used within-breed selection may be insufficient to meet the new challenges; thus, cross-breeding based on selecting highly efficient and highly adaptive breeds may be needed. Genomic introgression offers an efficient approach for cross-breeding that is expected to provide high genetic progress with a low rate of inbreeding. However, well-adapted breeds may have a small number of animals, which is a source of concern from a genetic biodiversity point of view. Furthermore, low animal numbers also limit the efficiency of genomic introgression. Sustainable cattle production in countries that have already intensified production is likely to emphasise better health, reproduction, feed efficiency, heat stress and other adaptation traits instead of higher production. This may require the application of innovative technologies for phenotyping and further use of new big data techniques to extract information for breeding.

Understanding variability and repeatability of enteric methane production in feedlot cattle

Breeding ruminants for low methane (CH4) emissions can be permanent and cumulative, but requires a better understanding of the variability of CH4 production among animals to accurately assess low-CH4 phenotypes. Our objectives were to: 1) investigate the variation in CH4 production among and within growing beef cattle, 2) identify low-CH4 emitters, and 3) examine relationships between CH4 production and intake, feeding behavior, growth, and rumen fermentation. Crossbred beef heifers (n=77; body weight=450 kg) were allocated to 3 pens and offered a finishing diet of 90% concentrate and 10% silage (dry matter (DM) basis). The study was conducted over 3 consecutive 6-week periods (126 days). GrowSafe bunks measured individual animal DM intake (DMI) and rumen fluid was sampled orally each period. A GreenFeed system measured individual animal emissions for 2 weeks/period. Methane production was calculated by animal within period using visits that were ≥3 min with fluxes compiled into six 4-h blocks corresponding to time of day, and averaged over blocks to obtain an average daily emission for the period. Animals with &lt;12 visits and &lt;5 blocks were omitted for the period and animals with ≥2 periods of complete CH4 data were used in the final analysis (n=52). Animals were ranked based on CH4 yield (g/kg DMI) from low to high, and grouped as Very-low (≤10% of animals), Low (11-25%), Intermediate (26-74%), High (75-89%), and Very high (≥90%) emitters (mean ± SD, 12.6 ± 2.16). The CH4 yield was 16% less (P&lt;0.05) for Very-low compared with Intermediate animals due to lower CH4 production (g/d, P&lt;0.05), with no differences in DMI (P&gt;0.05). However, the period × grouping interaction (P&lt;0.001) for CH4 yield indicated that the ranking of animals changed over time, although there were no extreme changes in rankings. Total VFA concentration decreased as CH4 yield decreased, but molar proportions of VFA remained unchanged, suggesting lower extent of ruminal digestion rather than a shift in fermentation. There were no differences in feeding behavior or average daily gain among groupings (P&gt;0.05). The between-animal coefficient of variation in CH4 yield of 17.3% enabled identification of low CH4-emmitting finishing beef cattle. However, accurate selection of low CH4-emitting animals should be based on repeated CH4 measurements over the production cycle.

Methodological guidelines: Cow milk mid-infrared spectra to predict GreenFeed enteric methane emissions

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

Genetic Parameters for Methane Emissions Using Indirect Prediction of Methane and Its Association with Milk and Milk Composition Traits

The study covers milk yield and composition data for 17,468 Polish Holstein-Friesian cows. Methane production (g/lactation per cow, MP) for dairy cow were predicted using three methane production equations (MPE) that took into account: milk yield (MPE1), energy corrected milk (MPE2) and both milk protein concentration (%), and energy-corrected milk (MPE3). The average amounts of methane produced for each cow per lactation were 31,089 g, 46,487 g, and 51,768 g for MPE1, MPE2, and MPE3, respectively. Repeatability models were used to estimate genetic parameters for MP. The estimated heritabilities for MPE1, MPE2, and MPE3 were 0.30, 0.24, and 0.24, respectively, with a standard error of 0.01. High genetic correlations (>0.76) were obtained between methane and milk yield, protein, fat, lactose and dry matter contents in milk for MPE1, MPE2 and MPE3. Still, a moderate genetic correlation (0.34) was obtained between methane and fat content (MPE1); the standard error of the estimated genetic correlation was less than 0.05. The results of the current study indicate that genetic selection aimed to reduce MP in dairy cows is possible. However, such direct genetic selection could cause a negative genetic response in milk yield and composition due to negative genetic correlations between MP and milk yield and composition.

Eating Time as a Genetic Indicator of Methane Emissions and Feed Efficiency in Australian Maternal Composite Sheep

Previous studies have shown reduced enteric methane emissions (ME) and residual feed intake (RFI) through the application of genomic selection in ruminants. The objective of this study was to evaluate feeding behaviour traits as genetic indicators for ME and RFI in Australian Maternal Composite ewes using data from an automated feed intake facility. The feeding behaviour traits evaluated were the amount of time spent eating per day (eating time; ETD; min/day) and per visit (eating time per event; ETE; min/event), daily number of events (DNE), event feed intake (EFI; g/event) and eating rate (ER; g/min). Genotypes and phenotypes of 445 ewes at three different ages (post-weaning, hogget, and adult) were used to estimate the heritability of ME, RFI, and the feeding behaviour traits using univariate genomic best linear unbiased prediction models. Multivariate models were used to estimate the correlations between these traits and within each trait at different ages. The response to selection was evaluated for ME and RFI with direct selection models and indirect models with ETE as an indicator trait, as this behaviour trait was a promising indicator based on heritability and genetic correlations. Heritabilities were between 0.12 and 0.18 for ME and RFI, and between 0.29 and 0.47 for the eating behaviour traits. In our data, selecting for more efficient animals (low RFI) would lead to higher methane emissions per day and per kg of dry matter intake. Selecting for more ETE also improves feed efficiency but results in more methane per day and per kg dry matter intake. Based on our results, ETE could be evaluated as an indicator trait for ME and RFI under an index approach that allows simultaneous selection for improvement in emissions and feed efficiency. Selecting for ETE may have a tremendous impact on the industry, as it may be easier and cheaper to obtain than feed intake and ME data. As the data were collected using individual feeding units, the findings on this research should be validated under grazing conditions.

Methane emissions and rumen metabolite concentrations in cattle fed two different silages

In this study, 18 animals were fed two forage-based diets: red clover (RC) and grass silage (GS), in a crossover-design experiment in which methane (CH₄) emissions were recorded in respiration chambers. Rumen samples obtained through naso-gastric sampling tubes were analysed by NMR. Methane yield (g/kg DM) was significantly lower from animals fed RC (17.8 ± 3.17) compared to GS (21.2 ± 4.61) p = 0.008. In total 42 metabolites were identified, 6 showing significant differences between diets (acetate, propionate, butyrate, valerate, 3-phenylopropionate, and 2-hydroxyvalerate). Partial least squares discriminant analysis (PLS-DA) was used to assess which metabolites were more important to distinguish between diets and partial least squares (PLS) regressions were used to assess which metabolites were more strongly associated with the variation in CH₄ emissions. Acetate, butyrate and propionate along with dimethylamine were important for the distinction between diets according to the PLS-DA results. PLS regression revealed that diet and dry matter intake are key factors to explain CH₄ variation when included in the model. Additionally, PLS was conducted within diet, revealing that the association between metabolites and CH₄ emissions can be conditioned by diet. These results provide new insights into the methylotrophic methanogenic pathway, confirming that metabolite profiles change according to diet composition, with consequences for CH₄ emissions.

Genetic parameters for repeatedly recorded enteric methane concentrations of dairy cows

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

Predicting enteric methane emission in lactating Holsteins based on reference methane data collected by the GreenFeed system

Methane emission is not included in the current breeding goals for dairy cattle mainly due to the expense and difficulty in obtaining sufficient data to generate accurate estimates of the relevant traits. While several models have been developed to predict methane emission from milk spectra using reference methane data obtained by the respiration chamber, SF6 and sniffer methods, the prediction of methane emission from milk mid-infrared (MIR) spectra using reference methane data collected by the GreenFeed system has not yet been explored. Methane emission was monitored for 151 cows using the GreenFeed system. Prediction models were developed for daily and average (for the trial period of 12 or 14 days) methane production (g/d), yield (g/kg DM intake (DMI)) and intensity (g/kg of fat- and protein-corrected milk) using partial least squares regression. The predictions were evaluated in 100 repeated validation cycles, where animals were randomly partitioned into training (80%) and testing (20%) populations for each cycle. The best performing model was observed for average methane intensity using MIR, parity and DMI with validation coefficient of determination (R²_val) and RMSE of prediction of 0.66 and 4.7 g/kg of fat- and protein-corrected milk, respectively. The accuracy of the best models for average methane production and average methane yield were poor (R²_val = 0.28 and 0.12, respectively). A lower accuracy of prediction was observed for methane intensity and production (R²_val = 0.42 and 0.17) when daily records were used while prediction for methane yield was comparable to that for average methane yield (R²_val = 0.16). Our results suggest the potential to predict methane intensity with moderate accuracy. In this case, prediction models for average methane values were generally better than for daily measures when using the GreenFeed system to obtain reference methane emission measurements.

Fungal and ciliate protozoa are the main rumen microbes associated with methane emissions in dairy cattle

Background

Mitigating the effects of global warming has become the main challenge for humanity in recent decades. Livestock farming contributes to greenhouse gas emissions, with an important output of methane from enteric fermentation processes, mostly in ruminants. Because ruminal microbiota is directly involved in digestive fermentation processes and methane biosynthesis, understanding the ecological relationships between rumen microorganisms and their active metabolic pathways is essential for reducing emissions. This study analysed whole rumen metagenome using long reads and considering its compositional nature in order to disentangle the role of rumen microbes in methane emissions.

Results

The β-diversity analyses suggested a subtle association between methane production and overall microbiota composition (0.01 < R² < 0.02). Differential abundance analysis identified 36 genera and 279 KEGGs as significantly associated with methane production (P_adj < 0.05). Those genera associated with high methane production were Eukaryota from Alveolata and Fungi clades, while Bacteria were associated with low methane emissions. The genus-level association network showed 2 clusters grouping Eukaryota and Bacteria, respectively. Regarding microbial gene functions, 41 KEGGs were found to be differentially abundant between low- and high-emission animals and were mainly involved in metabolic pathways. No KEGGs included in the methane metabolism pathway (ko00680) were detected as associated with high methane emissions. The KEGG network showed 3 clusters grouping KEGGs associated with high emissions, low emissions, and not differentially abundant in either. A deeper analysis of the differentially abundant KEGGs revealed that genes related with anaerobic respiration through nitrate degradation were more abundant in low-emission animals.

Conclusions

Methane emissions are largely associated with the relative abundance of ciliates and fungi. The role of nitrate electron acceptors can be particularly important because this respiration mechanism directly competes with methanogenesis. Whole metagenome sequencing is necessary to jointly consider the relative abundance of Bacteria, Archaea, and Eukaryota in the statistical analyses. Nutritional and genetic strategies to reduce CH₄ emissions should focus on reducing the relative abundance of Alveolata and Fungi in the rumen. This experiment has generated the largest ONT ruminal metagenomic dataset currently available.

Near-infrared calibration models for estimating volatile fatty acids and methane production from in vitro rumen fermentation of different total mixed rations

•

Near-infrared (NIR) prediction models accurately predicted volatile fatty acids, methane, and gas production.

•

Outputs of models could provide useful information for calibrating rumen mechanistic models.

•

Calibrations of valeric and isovaleric acids need to be improved.

Volatile fatty acids (VFA) and methane (CH₄) are the major products of rumen fermentation. The VFA are considered an energy source for the animal and rumen microbiota, and CH₄ (which is released by eructation) is considered an energy loss. Quantification of these fermentation products is fundamental for the evaluation of feeds and diets, and provides important information regarding the use of nutrients by ruminants. Near-infrared (NIR) spectroscopy is increasingly used for the evaluation of animal feeds because it is rapid, nondestructive, noninvasive, and inexpensive; does not require reagents; and the results are reproducible. The aim of this study was to develop NIR calibration models for estimating the production of VFA (acetic, propionic, butyric, valeric, isovaleric, and isobutyric acids), total gas, and CH₄ using in vitro gas production tests with buffered rumen inoculum throughout fermentation. Fifty-four total mixed rations (TMRs) were examined, and rumen fluid was manually collected from 2 dry Holstein dairy cows that had ruminal fistulas and were fed at maintenance energy levels. Then, 30 mL of buffered rumen fluid was incubated in bottles with ~220 mg of TMR. The total gas, VFA, and CH₄ were measured after 2, 5, 9, 24, 30, 48, and 72 h of rumen incubation for each TMR. The VFA were measured on 32 randomly selected TMR. In particular, 7 bottles were used for each TMR, one for each incubation time. Methane was measured in the headspace and VFA were measured in the buffered rumen fluid. The bottles were considered experimental units for calibration purposes. The production of CH₄ was quantified from the bottle headspaces by gas chromatography, and total gas production was measured using a pressure transducer at each incubation time. Two aliquots of the fermented liquids were sampled by opening the bottles at each incubation time, and (1) the concentrations of VFA were determined by gas chromatography or (2) spectra were obtained from Fourier-transform NIR spectroscopy. The data were randomly divided into calibration and validation data sets. The average concentrations of acetic acid (45.30 ± 11.92 and 43.86 ± 11.93 mmol/L), propionic acid (14.97 ± 6.08 and 14.38 ± 6.56 mmol/L), butyric acid (8.47 ± 3.47 and 8.65 ± 3.79 mmol/L), total gas (111.34 ± 81.90 and 116.46 ± 82.44 mL/g of organic matter), and CH₄ (9.65 ± 9.45 and 10.35 ± 9.33 mmol/L) were similar in the 2 data sets. The best calibration models were retained based on the coefficient of determination (R²) and the ratio of prediction to deviation (RPD). The R² values for prediction of VFA ranged from 0.69 (RPD = 3.28) for valeric acid to 0.94 (RPD = 4.20) for acetic acid. The models also provided good predictions of CH₄ (R² = 0.89, RPD = 3.05) and cumulative gas production (R² = 0.91, RPD = 3.30). The models described here precisely and accurately estimated the production of CH₄ and VFA during in vitro rumen fermentation tests. Validations at additional laboratories may provide more robust calibrations.

Agricultural methane emissions and the potential formitigation

Agriculture is the largest anthropogenic source of methane (CH₄), emitting 145 Tg CH₄y^-1 to the atmosphere in 2017. The main sources are enteric fermentation, manure management, rice cultivation and residue burning. There is significant potential to reduce CH₄ from these sources, with bottom-up mitigation potentials of approximately 10.6, 10, 2 and 1 Tg CH₄y^-1 from rice management, enteric fermentation, manure management and residue burning. Other system-wide studies have assumed even higher potentials of 4.8-47.2 Tg CH₄y^-1 from reduced enteric fermentation, and 4-36 Tg CH₄y^-1 from improved rice management. Biogas (a methane-rich gas mixture generated from the anaerobic decomposition of organic matter and used for energy) also has the potential to reduce unabated CH₄ emissions from animal manures and human waste. In addition to these supply side measures, interventions on the demand-side (shift to a plant-based diet and a reduction in total food loss and waste by 2050) would also significantly reduce methane emissions, perhaps in the order of greater than 50 Tg CH₄y^-1. While there is a pressing need to reduce emissions of long-lived greenhouse gases (CO₂ and N₂O) due to their persistence in the atmosphere, despite CH₄ being a short-lived greenhouse gas, the urgency of reducing warming means we must reduce any GHG emissions we can as soon as possible. Because of this, mitigation actions should focus on reducing emissions of all the three main anthropogenic greenhouse gases, including CH₄. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part1)'.

How necessary and feasible are reductions of methane emissions from livestock to support stringent temperature goals?

Agriculture is the largest single source of global anthropogenic methane (CH4) emissions, with ruminants the dominant contributor. Livestock CH4 emissions are projected to grow another 30% by 2050 under current policies, yet few countries have set targets or are implementing policies to reduce emissions in absolute terms. The reason for this limited ambition may be linked not only to the underpinning role of livestock for nutrition and livelihoods in many countries but also diverging perspectives on the importance of mitigating these emissions, given the short atmospheric lifetime of CH4. Here, we show that in mitigation pathways that limit warming to 1.5°C, which include cost-effective reductions from all emission sources, the contribution of future livestock CH4 emissions to global warming in 2050 is about one-third of that from future net carbon dioxide emissions. Future livestock CH4 emissions, therefore, significantly constrain the remaining carbon budget and the ability to meet stringent temperature limits. We review options to address livestock CH4 emissions through more efficient production, technological advances and demand-side changes, and their interactions with land-based carbon sequestration. We conclude that bringing livestock into mainstream mitigation policies, while recognizing their unique social, cultural and economic roles, would make an important contribution towards reaching the temperature goal of the Paris Agreement and is vital for a limit of 1.5°C. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'.

The potential for mitigation of methane emissions in ruminants through the application of metagenomics, metabolomics, and other -OMICS technologies

Ruminant supply chains contribute 5.7 gigatons of CO_2-eq per annum, which represents approximately 80% of the livestock sector emissions. One of the largest sources of emission in the ruminant sector is methane (CH₄), accounting for approximately 40% of the sectors total emissions. With climate change being a growing concern, emphasis is being put on reducing greenhouse gas emissions, including those from ruminant production. Various genetic and environmental factors influence cattle CH₄ production, such as breed, genetic makeup, diet, management practices, and physiological status of the host. The influence of genetic variability on CH₄ yield in ruminants indicates that genomic selection for reduced CH₄ emissions is possible. Although the microbiology of CH₄ production has been studied, further research is needed to identify key differences in the host and microbiome genomes and how they interact with one another. The advancement of “-omics” technologies, such as metabolomics and metagenomics, may provide valuable information in this regard. Improved understanding of genetic mechanisms associated with CH₄ production and the interaction between the microbiome profile and host genetics will increase the rate of genetic progress for reduced CH₄ emissions. Through a systems biology approach, various “-omics” technologies can be combined to unravel genomic regions and genetic markers associated with CH₄ production, which can then be used in selective breeding programs. This comprehensive review discusses current challenges in applying genomic selection for reduced CH₄ emissions, and the potential for “-omics” technologies, especially metabolomics and metagenomics, to minimize such challenges. The integration and evaluation of different levels of biological information using a systems biology approach is also discussed, which can assist in understanding the underlying genetic mechanisms and biology of CH₄ production traits in ruminants and aid in reducing agriculture’s overall environmental footprint.

Review: Markers and proxies to monitor ruminal function and feed efficiency in young ruminants

Developing the rumen's capacity to utilise recalcitrant and low-value feed resources is important for ruminant production systems. Early-life nutrition and management practices have been shown to influence development of the rumen in young animals with long-term consequences on their performance. Therefore, there has been increasing interest to understand ruminal development and function in young ruminants to improve feed efficiency, health, welfare, and performance of both young and adult ruminants. However, due to the small size, rapid morphological changes and low initial microbial populations of the rumen, it is difficult to study ruminal function in young ruminants without major invasive approaches or slaughter studies. In this review, we discuss the usefulness of a range of proxies and markers to monitor ruminal function and nitrogen use efficiency (a major part of feed efficiency) in young ruminants. Breath sulphide and methane emissions showed the greatest potential as simple markers of a developing microbiota in young ruminants. However, there is only limited evidence for robust indicators of feed efficiency at this stage. The use of nitrogen isotopic discrimination based on plasma samples appeared to be the most promising proxy for feed efficiency in young ruminants. More research is needed to explore and refine potential proxies and markers to indicate ruminal function and feed efficiency in young ruminants, particularly for neonatal ruminants.

Milk metabolome reveals variations on enteric methane emissions from dairy cows fed a specific inhibitor of the methanogenesis pathway

Metabolome profiling in biological fluids is an interesting approach for exploring markers of methane emissions in ruminants. In this study, a multiplatform metabolomics approach was used for investigating changes in milk metabolic profiles related to methanogenesis in dairy cows. For this purpose, 25 primiparous Holstein cows at similar lactation stage were fed the same diet supplemented with (treated, n = 12) or without (control, n = 13) a specific antimethanogenic additive that reduced enteric methane production by 23% with no changes in intake, milk production, and health status. The study lasted 6 wk, with sampling and measures performed in wk 5 and 6. Milk samples were analyzed using 4 complementary analytical methods, including 2 untargeted (nuclear magnetic resonance and liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer) and 2 targeted (liquid chromatography-tandem mass spectrometry and gas chromatography coupled to a flame ionization detector) approaches. After filtration, variable selection and normalization data from each analytical platform were then analyzed using multivariate orthogonal partial least square discriminant analysis. All 4 analytical methods were able to differentiate cows from treated and control groups. Overall, 38 discriminant metabolites were identified, which affected 10 metabolic pathways including methane metabolism. Some of these metabolites such as dimethylsulfoxide, dimethylsulfone, and citramalic acid, detected by nuclear magnetic resonance or liquid chromatography-mass spectrometry methods, originated from the rumen microbiota or had a microbial-host animal co-metabolism that could be associated with methanogenesis. Also, discriminant milk fatty acids detected by targeted gas chromatography were mostly of ruminal microbial origin. Other metabolites and metabolic pathways significantly affected were associated with AA metabolism. These findings provide new insight on the potential role of milk metabolites as indicators of enteric methane modifications in dairy cows.

Challenges and opportunities for quantifying greenhouse gas emissions through dairy cattle research in developing countries

The global dairy sector is facing the challenge of reducing greenhouse gas (GHG) emissions whilst increasing productivity to feed a growing population. Despite the importance of this challenge, many developing countries do not have the required resources, specifically funding, expertise and facilities, for quantifying GHG emissions from dairy production and research. This paper aims to address this challenge by discussing the magnitude of the issue, potential mitigation approaches and benefits in quantifying GHG emissions in a developing country context. Further, the paper explores the opportunities for developing country dairy scientists to leverage resources from developed countries, such as using existing relevant GHG emission estimation models. It is clear that further research is required to support developing countries to quantify and understand GHG emissions from dairy production, as it brings significant benefits including helping to identify and implement appropriate mitigation strategies for local production systems, trading carbon credits and achieving the nationally determined contribution obligations of the Paris Agreement.

Intramuscular Fatty Acids in Meat Could Predict Enteric Methane Production by Fattening Lambs

Methane (CH4) emissions pose a serious problem for the environmental sustainability of ruminant production. The aim of the present study was to explore the usefulness of the intramuscular fatty acid (FA) profile to estimate CH4 production of lambs fattened under intensive feeding systems. A statistical regression analysis of intramuscular FA derived from ruminal metabolism was carried out to assess the best predictive model of CH4 production (g/d) in lambs fed with different diets. CH4 was calculated with three distinct equations based on organic matter digestibility (OMD) at maintenance feeding levels. The OMD of the experimental diets was determined in an in vivo digestibility trial by means of the indicator method. Regression models were obtained by stepwise regression analysis. The three optimized models showed high adjusted coefficients of determination (R2adj = 0.74-0.93) and concordance correlation coefficients (CCC = 0.89-0.98), as well as small root mean square prediction errors (RMSPE = 0.29-0.40 g/d). The best single predictor was vaccenic acid (trans-11 C18:1), a bioactive FA that is formed in the rumen to a different extent depending on dietary composition. Based on our data and further published lamb research, we propose a novel regression model for CH4 production with excellent outcomes: CH4 (g/d) = -1.98 (±1.284)-0.87 (±0.231) × trans-11 C18:1 + 0.79 (±0.045) × BW (R2adj = 0.97; RMSPE = 0.76 g/d; CCC = 0.98). In conclusion, these results indicate that specific intramuscular FA and average BW during fattening could be useful to predict CH4 production of lambs fed high concentrate diets.

Association between fecal methanogen species with methane production and grazed forage intake of beef heifers classified for residual feed intake under drylot conditions

Reduction in greenhouse gas emission from beef production is essential to the survival of the beef industry from environmental and social-economic perspectives. There are different systems available to measure methane from animals, but they are expensive, not easily accessible, and not suitable for large-scale methane measurements on the farm. Therefore exploring indicator traits, which are easy to measure, cost-effective, and suitable for large-scale measurement, are recommended. The objectives of this study were to examine the diversity of fecal methanogen profile among efficient and inefficient beef heifers on pasture and investigate methanogen profile as a possible proxy to predict methane emission in beef cattle consuming a forage diet. Forty pregnant (1st trimester) heifers previously classified for postweaning residual feed intake adjusted for off-test back fat (RFI_fat; 20 high and 20 low) were included in this study. To determine individual pasture grazing intake, heifers were dosed with 1 kg of C₃₂ labeled pellets once per day from Day 0 to Day 12, and fecal samples were collected twice daily from Day 8 to Day 15. Fecal samples from Days 8, 10, and 12 were analyzed for their methanogen profile. Animals were monitored individually for methane and carbon dioxide production using a GreenFeed Emissions Monitoring system. Total methanogen population and methanogenic community diversity of fecal samples were not different (P > 0.1) between low and high RFI_fat groups, as measured by quantitative PCR and α- and β-diversity indices. However, both groups had a different methanogen profile; the relative abundance of Methanobrevibacter wolinii and relatives were higher (P < 0.002), while that of Methanosphaera species ISO3-F5 was lower (P < 0.01) in low RFI_fat cattle compared to the high RFI_fat group. We also demonstrated that fecal methanogen profiles may be a useful proxy in predicting daily methane and carbon dioxide emissions with an adjusted R² of 0.53 and 0.33, respectively, for low RFI_fat heifers and 0.46 and 0.57, respectively, for the high RFI_fat group.

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.

A dimensional reduction approach to modulate the core ruminal microbiome associated with methane emissions via selective breeding

The rumen is a complex microbial system of substantial importance in terms of greenhouse gas emissions and feed efficiency. This study proposes combining metagenomic and host genomic data for selective breeding of the cow hologenome toward reduced methane emissions. We analyzed nanopore long reads from the rumen metagenome of 437 Holstein cows from 14 commercial herds in 4 northern regions in Spain. After filtering, data were treated as compositional. The large complexity of the rumen microbiota was aggregated, through principal component analysis (PCA), into few principal components (PC) that were used as proxies of the core metagenome. The PCA allowed us to condense the huge and fuzzy taxonomical and functional information from the metagenome into a few PC. Bivariate animal models were applied using these PC and methane production as phenotypes. The variability condensed in these PC is controlled by the cow genome, with heritability estimates for the first PC of ~0.30 at all taxonomic levels, with a large probability (>83%) of the posterior distribution being >0.20 and with the 95% highest posterior density interval (95%HPD) not containing zero. Most genetic correlation estimates between PC1 and methane were large (≥0.70), with most of the posterior distribution (>82%) being >0.50 and with its 95%HPD not containing zero. Enteric methane production was positively associated with relative abundance of eukaryotes (protozoa and fungi) through the first component of the PCA at phylum, class, order, family, and genus. Nanopore long reads allowed the characterization of the core rumen metagenome using whole-metagenome sequencing, and the purposed aggregated variables could be used in animal breeding programs to reduce methane emissions in future generations.

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

Simple Summary

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

Abstract

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

Opportunities and Challenges for Improving the Productivity of Swamp Buffaloes in Southeastern Asia

The swamp buffalo is a domesticated animal commonly found in Southeast Asia. It is a highly valued agricultural animal for smallholders, but the production of this species has unfortunately declined in recent decades due to rising farm mechanization. While swamp buffalo still plays a role in farmland cultivation, this species' purposes has shifted from draft power to meat, milk, and hide production. The current status of swamp buffaloes in Southeast Asia is still understudied compared to its counterparts such as the riverine buffaloes and cattle. This review discusses the background of swamp buffalo, with an emphasis on recent work on this species in Southeast Asia, and associated genetics and genomics work such as cytogenetic studies, phylogeny, domestication and migration, genetic sequences and resources. Recent challenges to realize the potential of this species in the agriculture industry are also discussed. Limited genetic resource for swamp buffalo has called for more genomics work to be done on this species including decoding its genome. As the economy progresses and farm mechanization increases, research and development for swamp buffaloes are focused on enhancing its productivity through understanding the genetics of agriculturally important traits. The use of genomic markers is a powerful tool to efficiently utilize the potential of this animal for food security and animal conservation. Understanding its genetics and retaining and maximizing its adaptability to harsher environments are a strategic move for food security in poorer nations in Southeast Asia in the face of climate change.

Measurement of Enteric Methane Emissions by the SF6 Technique Is Not Affected by Ambient Weather Conditions

Simple Summary

Although the SF₆ technique was developed over 25 years ago with the intention that it could be used to measure enteric methane production from ruminants outdoors, no experiments have reported the influence of ambient wind speed, temperature, humidity or rainfall on the accuracy of the technique. Six different cohorts of dairy cows (40 per cohort) were kept outdoors and fed a common diet during spring in 3 consecutive years. Individual cow feed intakes and daily methane productions were measured over 5 consecutive days and an automatic weather station measured air temperature, wind speed, relative humidity and rainfall every 10 min. Regression analyses were used to relate the average daily temperature, wind speed, humidity and rainfall to the average daily dry matter intake, methane production and methane yield of each cohort of cows. It was concluded that the modified SF₆ technique can be used outdoors during a range of weather conditions without a significant effect on the measurement of methane production or methane yield of dairy cows.

Abstract

Despite the fact that the sulphur hexafluoride (SF₆) tracer technique was developed over 25 years ago to measure methane production from grazing and non-housed animals, no studies have specifically investigated whether ambient wind speed, temperature, relative humidity and rainfall influence the accuracy of the method. The aim of this research was to investigate how these weather factors influence the measurement of enteric methane production by the SF₆ technique. Six different cohorts of dairy cows (40 per cohort) were kept outdoors and fed a common diet during spring in 3 consecutive years. Methane production from individual cows was measured daily over the last 5 days of each 32-day period. An automated weather station measured air temperature, wind speed, relative humidity and rainfall every 10 min. Regression analyses were used to relate the average daily wind speed, average daily temperature, average daily relative humidity and total daily rainfall measurements to dry matter intake, average daily methane production and methane yield of each cohort of cows. It was concluded that the modified SF₆ technique can be used outdoors during a range of wind speeds, ambient temperatures, relative humidities and rainfall conditions without causing a significant effect on the measurement of methane production or methane yield of dairy cows.

A life cycle assessment study of dairy farms in northern Germany: The influence of performance parameters on environmental efficiency

Recently, consumers concerns towards an environmental friendly food production are growing. The dairy sector contributes to the production of important greenhouse gases such as methane. The life cycle assessment (LCA) method enables to quantify the emissions and the use of resources throughout the entire life cycle of a product. The aim of the current study was to investigate the influence of performance parameters on the level of important environmental impacts (global warming potential (GWP), freshwater eutrophication (FE), terrestrial acidification (TA) and agricultural land occupation (ALO)) associated with milk production. Therein, the environmental impacts were analyzed using LCA considering two separate datasets (total, continuous) from Northern German farms throughout the years 2004-2013. Therefore, the performance parameters determining the level of environmental impacts were identified using the partial least square method. Thereby, a differentiated analysis among regions with various soil characteristics (Heath, Hill, Marsh) was conducted additionally. Further, linear mixed models were applied to each of the environmental impact categories. Energy-corrected milk yield (ECM), ECM from roughage, feed efficiency and the use of concentrates were identified as the most important determinants of environmental impacts. In general, an increase in productivity, especially an increase in ECM per cow and an increase in the amount of ECM produced per area of agricultural land accompanied with an improvement in environmental efficiency. The type of feed used had the major impact on the level of environmental impacts, whereby both concentrates and roughage had disadvantages. These results were in line with previous studies. Although, this study provides additional information relating the most important determinants of different environmental impacts, including a differentiated consideration of the relationship between performance parameters and environmental efficiency among regions. Further analyses on specific soil characteristics and their impact on environmental efficiency are recommended. In line with the concept of eco-efficiency, useful mitigation strategies in practice need to be applied depending on individual framework conditions.

A heat diffusion multilayer network approach for the identification of functional biomarkers in rumen methane emissions

A better understanding of rumen microbial interactions is crucial for the study of rumen metabolism and methane emissions. Metagenomics-based methods can explore the relationship between microbial genes and metabolites to clarify the effect of microbial function on the host phenotype. This study investigated the rumen microbial mechanisms of methane metabolism in cattle by combining metagenomic data and network-based methods. Based on the relative abundance of 1461 rumen microbial genes and the main volatile fatty acids (VFAs), a multilayer heterogeneous network was constructed, and the functional modules associated with metabolite-microbial genes were obtained by heat diffusion algorithm. The PLS model by integrating data from VFAs and microbial genes explained 72.98% variation of methane emissions. Compared with single-layer networks, more previously reported biomarkers of methane prediction can be captured by the multilayer network. More biomarkers with the rank of top 20 topological centralities were from the PLS models of diffusion subsets. The heat diffusion algorithm is different from the strategy used by the microbial metabolic system to understand methane phenotype. It inferred 24 novel biomarkers that were preferentially affected by changes in specific VFAs. Results showed that the heat diffusion multilayer network approach improved the understanding of the microbial patterns of VFAs affecting methane emissions which represented by the functional microbial genes.

Inhibition of enteric methanogenesis in dairy cows induces changes in plasma metabolome highlighting metabolic shifts and potential markers of emission

There is scarce information on whether inhibition of rumen methanogenesis induces metabolic changes on the host ruminant. Understanding these possible changes is important for the acceptance of methane-reducing practices by producers. In this study we explored the changes in plasma profiles associated with the reduction of methane emissions. Plasma samples were collected from lactating primiparous Holstein cows fed the same diet with (Treated, n = 12) or without (Control, n = 13) an anti-methanogenic feed additive for six weeks. Daily methane emissions (CH₄, g/d) were reduced by 23% in the Treated group with no changes in milk production, feed intake, body weight, and biochemical indicators of health status. Plasma metabolome analyses were performed using untargeted [nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LC–MS)] and targeted (LC–MS/MS) approaches. We identified 48 discriminant metabolites. Some metabolites mainly of microbial origin such as dimethylsulfone, formic acid and metabolites containing methylated groups like stachydrine, can be related to rumen methanogenesis and can potentially be used as markers. The other discriminant metabolites are produced by the host or have a mixed microbial-host origin. These metabolites, which increased in treated cows, belong to general pathways of amino acids and energy metabolism suggesting a systemic non-negative effect on the animal.

Genetic Parameters of Different FTIR-Enabled Phenotyping Tools Derived from Milk Fatty Acid Profile for Reducing Enteric Methane Emissions in Dairy Cattle

This study aimed to infer the genetic parameters of five enteric methane emissions (EME) predicted from milk infrared spectra (13 models). The reference values were estimated from milk fatty acid profiles (chromatography), individual model-cheese, and daily milk yield of 1158 Brown Swiss cows (85 farms). Genetic parameters were estimated, under a Bayesian framework, for EME reference traits and their infrared predictions. Heritability of predicted EME traits were similar to EME reference values for methane yield (CH₄/DM: 0.232-0.317) and methane intensity per kg of corrected milk (CH₄/CM: 0.177-0.279), smaller per kg cheese solids (CH₄/SO: 0.093-0.165), but greater per kg fresh cheese (CH₄/CU: 0.203-0.267) and for methane production (dCH₄: 0.195-0.232). We found good additive genetic correlations between infrared-predicted methane intensities and the reference values (0.73 to 0.93), less favorable values for CH₄/DM (0.45-0.60), and very variable for dCH₄ according to the prediction method (0.22 to 0.98). Easy-to-measure milk infrared-predicted EME traits, particularly CH₄/CM, CH₄/CU and dCH₄, could be considered in breeding programs aimed at the improvement of milk ecological footprint.

Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.

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.

Between-cow variation in milk fatty acids associated with methane production

We evaluated the between-cow (b-cow) variation and repeatability in omasal and milk fatty acids (FA) related to methane (CH4) emission. The dataset was originated from 9 studies with rumen-cannulated dairy cows conducted using either a switch-back or a Latin square design. Production of CH4 per mole of VFA (Y_CH4VFA) was calculated based on VFA stoichiometry. Experiment, diet within experiment, period within experiment, and cow within experiment were considered as random factors. Empirical models were developed between the variables of interest by univariate and bivariate mixed model regression analysis. The variation associated with diet was higher than the b-cow variation with low repeatability (< 0.25) for milk odd- and branch-chain FA (OBCFA). Similarly, for de novo synthesized milk FA, diet variation was ~ 3-fold greater than the b-cow variation; repeatability for these FA was moderate to high (0.34-0.58). Also, for both cis-9 C18:1 and cis-9 cis-12 cis-15 C18:3 diet variation was more than double the b-cow variation, but repeatability was moderate. Among the de novo milk FA, C4:0 was positively related with stoichiometric Y_CH4VFA, while for OBCFA, anteiso C15:0 and C15:0 were negatively related with it. Notably, when analyzing the relationship between omasal FA and milk FA we observed positive intercept estimates for all the OBCFA, which may indicate endogenous post-ruminal synthesis of these FA, most likely in the mammary gland. For milk iso C13:0, iso C15:0, anteiso C15:0, and C15:0 were positively influenced by omasal proportion of their respective FA and by energy balance. In contrast, the concentration of milk C17:0, iso C18:0, C18:0, cis-11 C18:1, and cis-9 cis-12 cis-15 C18:3 were positively influenced by omasal proportion of their respective FA but negatively related to calculated energy balance. Our findings demonstrate that for most milk FA examined, a larger variation is attributed to diet than b-cow differences with low to moderate repeatability. While some milk FA were positively or negatively related with Y_CH4VFA, there was a pronounced effect of calculated energy balance on these estimates. Additionally, even though OBCFA have been indicated as markers of rumen function, our results suggest that endogenous synthesis of these FA may occur, which therefore, may limit the utilization of milk FA as a proxy for CH4 predictions for cows fed the same diet.