Rumen stoichiometric models and their contribution and challenges in predicting enteric methane production

Temporal dynamics of volatile fatty acids profile, methane production, and prokaryotic community in an in vitro rumen fermentation system fed with maize silage

Anaerobic in vitro fermentation is widely used to simulate rumen kinetics and study the microbiome and metabolite profiling in a controlled lab environment. However, a better understanding of the interplay between the temporal dynamics of fermentation kinetics, metabolic profiles, and microbial composition in in vitro rumen fermentation batch systems is required. To fill that knowledge gap, we conducted three in vitro rumen fermentations with maize silage as the substrate, monitoring total gas production (TGP), dry matter degradability (dDM), and methane (CH4) concentration at 6, 12, 24, 36, and 48 h in each fermentation. At each time point, we collected rumen fluid samples for microbiome analysis and volatile fatty acid (VFA) analysis. Amplicon sequencing of 16S rRNA genes (V4 region) was used to profile the prokaryotic community structure in the rumen during the fermentation process. As the fermentation time increased, dDM, TGP, VFA concentrations, CH4 concentration, and yield (mL CH4 per g DM at standard temperature and pressure (STP)) significantly increased. For the dependent variables, CH4 concentration and yield, as well as the independent variables TGP and dDM, polynomial equations were fitted. These equations explained over 85% of the data variability (R2 > 0.85) and suggest that TGP and dDM can be used as predictors to estimate CH4 production in rumen fermentation systems. Microbiome analysis revealed a dominance of Bacteroidota, Cyanobacteria, Desulfobacterota, Euryarchaeota, Fibrobacterota, Firmicutes, Patescibacteria, Proteobacteria, Spirochaetota, and Verrucomicrobiota. Significant temporal variations in Bacteroidota, Campylobacterota, Firmicutes, Proteobacteria, and Spirochaetota were detected. Estimates of alpha diversity based on species richness and the Shannon index showed no variation between fermentation time points. This study demonstrated that the in vitro fermentation characteristics of a given feed type (e.g., maize silage) can be predicted from a few parameters (CH4 concentration and yield, tVFA, acetic acid, and propionic acid) without running the actual in vitro trial if the rumen fluid is collected from similar donor cows. Although the dynamics of the rumen prokaryotes changed remarkably over time and in accordance with the fermentation kinetics, more time points between 0 and 24 h are required to provide more details about the microbial temporal dynamics at the onset of the fermentation.

Quantification of methane emitted by ruminants: A review of methods

The contribution of greenhouse gas (GHG) emissions from ruminant production systems varies between countries and between regions within individual countries. The appropriate quantification of GHG emissions, specifically methane (CH₄), has raised questions about the correct reporting of GHG inventories and, perhaps more importantly, how best to mitigate CH₄ emissions. This review documents existing methods and methodologies to measure and estimate CH₄ emissions from ruminant animals and the manure produced therein over various scales and conditions. Measurements of CH₄ have frequently been conducted in research settings using classical methodologies developed for bioenergetic purposes, such as gas exchange techniques (respiration chambers, headboxes). While very precise, these techniques are limited to research settings as they are expensive, labor-intensive, and applicable only to a few animals. Head-stalls, such as the GreenFeed system, have been used to measure expired CH₄ for individual animals housed alone or in groups in confinement or grazing. This technique requires frequent animal visitation over the diurnal measurement period and an adequate number of collection days. The tracer gas technique can be used to measure CH₄ from individual animals housed outdoors, as there is a need to ensure low background concentrations. Micrometeorological techniques (e.g., open-path lasers) can measure CH₄ emissions over larger areas and many animals, but limitations exist, including the need to measure over more extended periods. Measurement of CH₄ emissions from manure depends on the type of storage, animal housing, CH₄ concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the variable that contributes the greatest to measurement uncertainty. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH₄ emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources (enteric and manure). In contrast, top-down approaches estimate the amount of CH₄ in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point. While these two estimation approaches rarely agree, they help identify knowledge gaps and research requirements in practice.

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

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

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

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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.

Genome-wide association studies for methane emission and ruminal volatile fatty acids using Holstein cattle sequence data

Background

Methane emission by ruminants has contributed considerably to the global warming and understanding the genomic architecture of methane production may help livestock producers to reduce the methane emission from the livestock production system. The goal of our study was to identify genomic regions affecting the predicted methane emission (PME) from volatile fatty acids (VFAs) indicators and VFA traits using imputed whole-genome sequence data in Iranian Holstein cattle.

Results

Based on the significant-association threshold (p < 5 × 10^− 8), 33 single nucleotide polymorphisms (SNPs) were detected for PME per kg milk (n = 2), PME per kg fat (n = 14), and valeric acid (n = 17). Besides, 69 genes were identified for valeric acid (n = 18), PME per kg milk (n = 4) and PME per kg fat (n = 47) that were located within 1 Mb of significant SNPs. Based on the gene ontology (GO) term analysis, six promising candidate genes were significantly clustered in organelle organization (GO:0004984, p = 3.9 × 10^− 2) for valeric acid, and 17 candidate genes significantly clustered in olfactory receptors activity (GO:0004984, p = 4 × 10^− 10) for PME traits. Annotation results revealed 31 quantitative trait loci (QTLs) for milk yield and its components, body weight, and residual feed intake within 1 Mb of significant SNPs.

Conclusions

Our results identified 33 SNPs associated with PME and valeric acid traits, as well as 17 olfactory receptors activity genes for PME traits related to feed intake and preference. Identified SNPs were close to 31 QTLs for milk yield and its components, body weight, and residual feed intake traits. In addition, these traits had high correlations with PME trait. Overall, our findings suggest that marker-assisted and genomic selection could be used to improve the difficult and expensive-to-measure phenotypes such as PME. Moreover, prediction of methane emission by VFA indicators could be useful for increasing the size of reference population required in genome-wide association studies and genomic selection.

Volatile Fatty Acids in Ruminal Fluid Can Be Used to Predict Methane Yield of Dairy Cows

The dry matter intake (DMI) of forage-fed cattle can be used to predict their methane emissions. However, many cattle are fed concentrate-rich diets that decrease their methane yield. A range of equations predicting methane yield exist, but most use information that is generally unavailable when animals are fed in groups or grazing. The aim of this research was to develop equations based on proportions of ruminal volatile-fatty-acids to predict methane yield of dairy cows fed forage-dominant as well as concentrate-rich diets. Data were collated from seven experiments with a total of 24 treatments, from 215 cows. Forage in the diets ranged from 440 to 1000 g/kg. Methane was measured either by open-circuit respiration chambers or a sulfur hexafluoride (SF6) technique. In all experiments, ruminal fluid was collected via the mouth approximately four hours after the start of feeding. Seven prediction equations were tested. Methane yield (MY) was equally best predicted by the following equations: MY = 4.08 × (acetate/propionate) + 7.05; MY = 3.28 × (acetate + butyrate)/propionate + 7.6; MY = 316/propionate + 4.4. These equations were validated against independent published data from both dairy and beef cattle consuming a wide range of diets. A concordance of 0.62 suggests these equations may be applicable for predicting methane yield from all cattle and not just dairy cows, with root mean-square error of prediction of 3.0 g CH4/kg dry matter intake.

Effect of Mootral-a garlic- and citrus-extract-based feed additive-on enteric methane emissions in feedlot cattle

Enteric methane (CH₄) production is the main source of greenhouse gas emissions from livestock globally with beef cattle contributing 5.95% of total global greenhouse gas emissions. Various mitigation strategies have been developed to reduce enteric emissions with limited success. In vitro studies have shown a reduction in CH₄ emissions when using garlic and citrus extracts. However, there is paucity of data regarding in vivo studies investigating the effect of garlic and citrus extracts in cattle. The objective of this study was to quantitatively evaluate the response of Angus × Hereford cross steers consuming the feed additive Mootral, which contains extracts of both garlic and citrus, on CH₄ yield (g/kg dry matter intake [DMI]). Twenty steers were randomly assigned to two treatments: control (no additive) and Mootral supplied at 15 g/d in a completely randomized design with a 2-wk covariate and a 12-wk data collection periods. Enteric CH₄ emissions were measured using the GreenFeed system during the covariate period and experimental weeks 2, 6, 9, and 12. CH₄ yield (g/kg DMI) by steers remained similar in both treatments for weeks 2 to 9. In week 12, there was a significant decrease in CH₄ yield (23.2%) in treatment compared to control steers mainly because the steers were consuming all the pellets containing the additive. However, overall CH₄ yield (g/kg DMI) during the entire experimental period was not significantly different. Carbon dioxide yield (g/kg DMI) and oxygen consumption (g/kg DMI) did not differ between treatments during the entire experimental period. DMI, average daily gain, and feed efficiency also remained similar in control and supplemented steers. The in vivo results showed that Mootral may have a potential to be used as a feed additive to reduce enteric CH₄ production and yield in beef cattle but needs further investigation under various dietary regimen.

Genetic parameters of plasma and ruminal volatile fatty acids in sheep fed alfalfa pellets and genetic correlations with enteric methane emissions1

Animal-to-animal variation in methane (CH4) emissions determined in respiration chambers has a genetic basis, but rapid phenotyping methods that can be applied on-farm are required to enable increased genetic progress by the farming industry. Fermentation of carbohydrates in the rumen results in the formation of VFA with hydrogen (H2) as a byproduct that is used for CH4 formation. Generally, fermentation pathways leading to acetate are associated with the most H2 production, less H2 formation is associated with butyrate production, and propionate and valerate production are associated with reduced H2 production. Therefore, VFA may constitute a potential correlated proxy for CH4 emissions to enable high-throughput animal screening. The objective of the present study was to determine the genetic parameters for ruminal and plasma VFA concentrations in sheep fed alfalfa (Medicago sativa L.) pellets and their genetic (rg) and phenotypic (rp) correlations with CH4 emissions. Measurements of CH4 emissions in respiration chambers and ruminal (stomach tubing 18 h from last meal) and blood plasma (3 h post-feeding) VFA concentrations were made on 1,538 lambs from 5 birth years (2007 and 2009 to 2012) aged between 5 and 10 mo, while the animals were fed alfalfa pellets at 2.0 times maintenance requirements in 2 equal size meals (0900 and 1500 h). These measurements were repeated twice (rounds) 14 d apart. Mean (± SD) CH4 production was 24.4 ± 3.08 g/d, and the mean CH4 yield was 15.8 ± 1.51 g/kg DMI. Mean concentration of total ruminal VFA was 52.2 mM, with concentrations of acetate, propionate and butyrate of 35.97, 8.83, and 4.02 mM, respectively. Ruminal total VFA concentration had heritability (h2) and repeatability estimates (± SE) of 0.24 ± 0.05 and 0.35 ± 0.03, respectively, and similar estimates were found for acetate, propionate, and butyrate. Blood plasma concentrations of VFA had much lower estimates of h2 and repeatability than ruminal VFA. Genetic correlations with CH4 yield were greatest for total concentrations of ruminal VFA and acetate, with 0.54 ± 0.12 and 0.56 ± 0.12, respectively, which were much greater than their corresponding rp. The rp and rg of ruminal VFA proportions and blood VFAs with CH4 emissions were in general lower than for ruminal VFA concentrations. However, minor ruminal VFA proportions had also moderate rg with CH4 yield. Pre-feeding concentrations of total VFA and acetate were the strongest correlated proxies to select sheep that are genetically low CH4 emitters.

Evaluation of a sheep rumen model with fresh forages of diverse chemical composition

The sheep rumen submodel MollyRum14 was evaluated on its methane and VFA predictions against data from respiration-chamber trials conducted with sheep fed perennial ryegrass, white clover, chicory, forage rape, turnip (leafy and bulb varieties), swedes, kale, or forage radish. We assessed the model's response to substrate degradation rate (settings that affect the rate of cellulose and hemicellulose digestion) and to fermentation stoichiometry (settings that alter nonglucogenic to glucogenic short-chain fatty acid ratios). Model predictions were evaluated against data for methane production (pCH4: g/d), methane yield (yCH4: g/kg DMI), and acetate to propionate ratio (A:P). The predictive ability of the model for both pCH4 and yCH4 was superior for perennial ryegrass than for other forages. Except for swedes and chicory, predictions for yCH4 were correctly ranked across the forages evaluated. Except for forage rape, robust predictions were obtained for all forages using fast degradation kinetics and a predominantly acetogenic stoichiometry. Model predictions for forage rape were enhanced using slow degradation kinetics and a predominantly propionic stoichiometry. These results indicate that MollyRum14 is suitable to predict methane emissions from sheep fed a variety of fresh forages including annual fodder crops. However, a clear understanding of degradation rates and stoichiometries is needed to enhance the utility of the model as a predictive tool. This would allow continuous adjustment of digestion rates and stoichiometries to be potentially tailored to individual forage species.

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

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

The Contribution of Mathematical Modeling to Understanding Dynamic Aspects of Rumen Metabolism

All mechanistic rumen models cover the main drivers of variation in rumen function, which are feed intake, the differences between feedstuffs and feeds in their intrinsic rumen degradation characteristics, and fractional outflow rate of fluid and particulate matter. Dynamic modeling approaches are best suited to the prediction of more nuanced responses in rumen metabolism, and represent the dynamics of the interactions between substrates and micro-organisms and inter-microbial interactions. The concepts of dynamics are discussed for the case of rumen starch digestion as influenced by starch intake rate and frequency of feed intake, and for the case of fermentation of fiber in the large intestine. Adding representations of new functional classes of micro-organisms (i.e., with new characteristics from the perspective of whole rumen function) in rumen models only delivers new insights if complemented by the dynamics of their interactions with other functional classes. Rumen fermentation conditions have to be represented due to their profound impact on the dynamics of substrate degradation and microbial metabolism. Although the importance of rumen pH is generally acknowledged, more emphasis is needed on predicting its variation as well as variation in the processes that underlie rumen fluid dynamics. The rumen wall has an important role in adapting to rapid changes in the rumen environment, clearing of volatile fatty acids (VFA), and maintaining rumen pH within limits. Dynamics of rumen wall epithelia and their role in VFA absorption needs to be better represented in models that aim to predict rumen responses across nutritional or physiological states. For a detailed prediction of rumen N balance there is merit in a dynamic modeling approach compared to the static approaches adopted in current protein evaluation systems. Improvement is needed on previous attempts to predict rumen VFA profiles, and this should be pursued by introducing factors that relate more to microbial metabolism. For rumen model construction, data on rumen microbiomes are preferably coupled with knowledge consolidated in rumen models instead of relying on correlations with rather general aspects of treatment or animal. This helps to prevent the disregard of basic principles and underlying mechanisms of whole rumen function.

Thermodynamic Driving Force of Hydrogen on Rumen Microbial Metabolism: A Theoretical Investigation

Hydrogen is a key product of rumen fermentation and has been suggested to thermodynamically control the production of the various volatile fatty acids (VFA). Previous studies, however, have not accounted for the fact that only thermodynamic near-equilibrium conditions control the magnitude of reaction rate. Furthermore, the role of NAD, which is affected by hydrogen partial pressure (P_H2), has often not been considered. The aim of this study was to quantify the control of P_H2 on reaction rates of specific fermentation pathways, methanogenesis and NADH oxidation in rumen microbes. The control of P_H2 was quantified using the thermodynamic potential factor (F_T), which is a dimensionless factor that corrects a predicted kinetic reaction rate for the thermodynamic control exerted. Unity F_T was calculated for all glucose fermentation pathways considered, indicating no inhibition of P_H2 on the production of a specific type of VFA (e.g., acetate, propionate and butyrate) in the rumen. For NADH oxidation without ferredoxin oxidation, increasing P_H2 within the rumen physiological range decreased F_T from unity to zero for different NAD⁺ to NADH ratios and pH of 6.2 and 7.0, which indicates thermodynamic control of P_H2. For NADH oxidation with ferredoxin oxidation, increasing P_H2 within the rumen physiological range decreased F_T from unity at pH of 7.0 only. For the acetate to propionate conversion, F_T increased from 0.65 to unity with increasing P_H2, which indicates thermodynamic control. For propionate to acetate and butyrate to acetate conversions, F_T decreased to zero below the rumen range of P_H2, indicating full thermodynamic suppression. For methanogenesis by archaea without cytochromes, F_T differed from unity only below the rumen range of P_H2, indicating no thermodynamic control. This theoretical investigation shows that thermodynamic control of P_H2 on individual VFA produced and associated yield of hydrogen and methane cannot be explained without considering NADH oxidation.

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

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

Research and demonstration to improve air quality for the U.S. animal feeding operations in the 21st century - a critical review

There was an increasing interest in reducing production and emission of air pollutants to improve air quality for animal feeding operations (AFOs) in the U.S. in the 21st century. Research was focused on identification, quantification, characterization, and modeling of air pollutions; effects of emissions; and methodologies and technologies for scientific research and pollution control. Mitigation effects were on pre-excretion, pre-release, pre-emission, and post-emission. More emphasis was given on reducing pollutant emissions than improving indoor air quality. Research and demonstrations were generally continuation and improvement of previous efforts. Most demonstrated technologies were still in a limited scale of application. Future efforts are needed in many fundamental and applied research areas. Advancement in instrumentation, computer technology, and biological sciences and genetic engineering is critical to bring major changes in this area. Development in research and demonstration will depend on the actual political, economic, and environmental situations.

Methods to determine the relative value of genetic traits in dairy cows to reduce greenhouse gas emissions along the chain

Current decisions on breeding in dairy farming are mainly based on economic values of heritable traits, as earning an income is a primary objective of farmers. Recent literature, however, shows that breeding also has potential to reduce greenhouse gas (GHG) emissions. The objective of this paper was to compare 2 methods to determine GHG values of genetic traits. Method 1 calculates GHG values using the current strategy (i.e., maximizing labor income), whereas method 2 is based on minimizing GHG per kilogram of milk and shows what can be achieved if the breeding results are fully directed at minimizing GHG emissions. A whole-farm optimization model was used to determine results before and after 1 genetic standard deviation improvement (i.e., unit change) of milk yield and longevity. The objective function of the model differed between method 1 and 2. Method 1 maximizes labor income; method 2 minimizes GHG emissions per kilogram of milk while maintaining labor income and total milk production at least at the level before the change in trait. Results show that the full potential of the traits to reduce GHG emissions given the boundaries that were set for income and milk production (453 and 441kg of CO2 equivalents/unit change per cow per year for milk yield and longevity, respectively) is about twice as high as the reduction based on maximizing labor income (247 and 210kg of CO2 equivalents/unit change per cow per year for milk yield and longevity, respectively). The GHG value of milk yield is higher than that of longevity, especially when the focus is on maximizing labor income. Based on a sensitivity analysis, it was shown that including emissions from land use change and using different methods for handling the interaction between milk and meat production can change results, generally in favor of milk yield. Results can be used by breeding organizations that want to include GHG values in their breeding goal. To verify GHG values, the effect of prices and emissions factors should be considered, as well as the potential effect of variation between farm types.

Special topics--Mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options

The goal of this review was to analyze published data related to mitigation of enteric methane (CH4) emissions from ruminant animals to document the most effective and sustainable strategies. Increasing forage digestibility and digestible forage intake was one of the major recommended CH4 mitigation practices. Although responses vary, CH4 emissions can be reduced when corn silage replaces grass silage in the diet. Feeding legume silages could also lower CH4 emissions compared to grass silage due to their lower fiber concentration. Dietary lipids can be effective in reducing CH4 emissions, but their applicability will depend on effects on feed intake, fiber digestibility, production, and milk composition. Inclusion of concentrate feeds in the diet of ruminants will likely decrease CH4 emission intensity (Ei; CH4 per unit animal product), particularly when inclusion is above 40% of dietary dry matter and rumen function is not impaired. Supplementation of diets containing medium to poor quality forages with small amounts of concentrate feed will typically decrease CH4 Ei. Nitrates show promise as CH4 mitigation agents, but more studies are needed to fully understand their impact on whole-farm greenhouse gas emissions, animal productivity, and animal health. Through their effect on feed efficiency and rumen stoichiometry, ionophores are likely to have a moderate CH4 mitigating effect in ruminants fed high-grain or mixed grain-forage diets. Tannins may also reduce CH4 emissions although in some situations intake and milk production may be compromised. Some direct-fed microbials, such as yeast-based products, might have a moderate CH4-mitigating effect through increasing animal productivity and feed efficiency, but the effect is likely to be inconsistent. Vaccines against rumen archaea may offer mitigation opportunities in the future although the extent of CH4 reduction is likely to be small and adaptation by ruminal microbes and persistence of the effect is unknown. Overall, improving forage quality and the overall efficiency of dietary nutrient use is an effective way of decreasing CH4 Ei. Several feed supplements have a potential to reduce CH4 emission from ruminants although their long-term effect has not been well established and some are toxic or may not be economically feasible.

Anti-methanogenic effects of monensin in dairy and beef cattle: a meta-analysis

Monensin is a widely used feed additive with the potential to minimize methane (CH4) emissions from cattle. Several studies have investigated the effects of monensin on CH4, but findings have been inconsistent. The objective of the present study was to conduct meta-analyses to quantitatively summarize the effect of monensin on CH4 production (g/d) and the percentage of dietary gross energy lost as CH4 (Ym) in dairy cows and beef steers. Data from 22 controlled studies were used. Heterogeneity of the monensin effects were estimated using random effect models. Due to significant heterogeneity (>68%) in both dairy and beef studies, the random effect models were then extended to mixed effect models by including fixed effects of DMI, dietary nutrient contents, monensin dose, and length of monensin treatment period. Monensin reduced Ym from 5.97 to 5.43% and diets with greater neutral detergent fiber contents (g/kg of dry matter) tended to enhance the monensin effect on CH4 in beef steers. When adjusted for the neutral detergent fiber effect, monensin supplementation [average 32 mg/kg of dry matter intake (DMI)] reduced CH4 emissions from beef steers by 19±4 g/d. Dietary ether extract content and DMI had a positive and a negative effect on monensin in dairy cows, respectively. When adjusted for these 2 effects in the final mixed-effect model, monensin feeding (average 21 mg/kg of DMI) was associated with a 6±3 g/d reduction in CH4 emissions in dairy cows. When analyzed across dairy and beef cattle studies, DMI or monensin dose (mg/kg of DMI) tended to decrease or increase the effect of monensin in reducing methane emissions, respectively. Methane mitigation effects of monensin in dairy cows (-12±6 g/d) and beef steers (-14±6 g/d) became similar when adjusted for the monensin dose differences between dairy cow and beef steer studies. When adjusted for DMI differences, monensin reduced Ym in dairy cows (-0.23±0.14) and beef steers (-0.33±0.16). Monensin treatment period length did not significantly modify the monensin effects in dairy cow or beef steer studies. Overall, monensin had stronger antimethanogenic effects in beef steers than dairy cows, but the effects in dairy cows could potentially be improved by dietary composition modifications and increasing the monensin dose.

Effect of high-sugar grasses on methane emissions simulated using a dynamic model

High-sugar grass varieties have received considerable attention for their potential ability to decrease N excretion in cattle. However, feeding high-sugar grasses alters the pattern of rumen fermentation, and no in vivo studies to date have examined this strategy with respect to another environmental pollutant: methane (CH(4)). Modeling allows us to examine potential outcomes of feeding strategies under controlled conditions, and can provide a useful framework for the development of future experiments. The purpose of the present study was to use a modeling approach to evaluate the effect of high-sugar grasses on simulated CH(4) emissions in dairy cattle. An extant dynamic, mechanistic model of enteric fermentation and intestinal digestion was used for this evaluation. A simulation database was constructed and analysis of model behavior was undertaken to simulate the effect of (1) level of water-soluble carbohydrate (WSC) increase in dietary dry matter, (2) change in crude protein (CP) and neutral detergent fiber (NDF) content of the plant with an increased WSC content, (3) level of N fertilization, and (4) presence or absence of grain feeding. Simulated CH(4) emissions tended to increase with increased WSC content when CH(4) was expressed as megajoules per day or percent of gross energy intake, but when CH(4) was expressed in terms of grams per kilogram of milk, results were much more variable due to the potential increase in milk yield. As a result, under certain conditions, CH(4) (g/kg of milk) decreased. The largest increases in CH(4) emissions (MJ/d or % gross energy intake) were generally seen when WSC increased at the expense of CP in the diet and this can largely be explained by the representation in the model of the type of volatile fatty acid produced. Effects were lower when WSC increased at the expense of NDF, and intermediary when WSC increased at the expense of a mixture of CP and NDF. When WSC increased at the expense of NDF, simulated milk yield increased and, therefore, CH(4) (g/kg of milk) tended to decrease. Diminished increases of CH(4) (% gross energy intake or g/kg of milk) were simulated when DMI was increased with elevated WSC content. Simulation results suggest that high WSC grass, as a strategy to mitigate N emission, may increase CH(4) emissions, but that results depend on the grass composition, DMI, and the units chosen to express CH(4). Overall, this project demonstrates the usefulness of modeling for hypothesis testing in the absence of observed experimental results.