Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database

An integral assessment of the impact of diet and manure management on whole-farm greenhouse gas and nitrogen emissions in dairy cattle production systems using process-based models

Feed management decisions are crucial in mitigating greenhouse gas (GHG) and nitrogen (N) emissions from ruminant farming systems. However, assessing the downstream impact of diet on emissions in dairy production systems is complex, due to the multifunctional relationships between a variety of distinct but interconnected sources such as animals, housing, manure storage, and soil. Therefore, there is a need for an integral assessment of the direct and indirect GHG and N emissions that considers the underlying processes of carbon (C), N and their drivers within the system. Here we show the relevance of using a cascade of process-based (PB) models, such as Dutch Tier 3 and (Manure)-DNDC (Denitrification-Decomposition) models, for capturing the downstream influence of diet on whole-farm emissions in two contrasting case study dairy farms: a confinement system in Germany and a pasture-based system in New Zealand. Considerable variation was found in emissions on a per hectare and per head basis, and across different farm components and categories of animals. Moreover, the confinement system had a farm C emission of 1.01 kg CO2-eq kg-1 fat and protein corrected milk (FPCM), and a farm N emission of 0.0300 kg N kg-1 FPCM. In contrast, the pasture-based system had a lower farm C and N emission averaging 0.82 kg CO2-eq kg-1 FPCM and 0.006 kg N kg-1 FPCM, respectively over the 4-year period. The results demonstrate how inputs and outputs could be made compatible and exchangeable across the PB models for quantifying dietary effects on whole-farm GHG and N emissions.

Predicting CO2 production of lactating dairy cows from animal, dietary, and production traits using an international dataset

Automated measurements of the ratio of concentrations of methane and carbon dioxide, [CH4]:[CO2], in breath from individual animals (the so-called "sniffer technique") and estimated CO2 production can be used to estimate CH4 production, provided that CO2 production can be reliably calculated. This would allow CH4 production from individual cows to be estimated in large cohorts of cows, whereby ranking of cows according to their CH4 production might become possible and their values could be used for breeding of low CH4-emitting animals. Estimates of CO2 production are typically based on predictions of heat production, which can be calculated from body weight (BW), energy-corrected milk yield, and days of pregnancy. The objectives of the present study were to develop predictions of CO2 production directly from milk production, dietary, and animal variables, and furthermore to develop different models to be used for different scenarios, depending on available data. An international dataset with 2,244 records from individual lactating cows including CO2 production and associated traits, as dry matter intake (DMI), diet composition, BW, milk production and composition, days in milk, and days pregnant, was compiled to constitute the training dataset. Research location and experiment nested within research location were included as random intercepts. The method of CO2 production measurement (respiration chamber [RC] or GreenFeed [GF]) was confounded with research location, and therefore excluded from the model. In total, 3 models were developed based on the current training dataset: model 1 ("best model"), where all significant traits were included; model 2 ("on-farm model"), where DMI was excluded; and model 3 ("reduced on-farm model"), where both DMI and BW were excluded. Evaluation on test dat sets with either RC data (n = 103), GF data without additives (n = 478), or GF data only including observations where nitrate, 3-nitrooxypropanol (3-NOP), or a combination of nitrate and 3-NOP were fed to the cows (GF+: n = 295), showed good precision of the 3 models, illustrated by low slope bias both in absolute values (-0.22 to 0.097) and in percentage (0.049 to 4.89) of mean square error (MSE). However, the mean bias (MB) indicated systematic overprediction and underprediction of CO2 production when the models were evaluated on the GF and the RC test datasets, respectively. To address this bias, the 3 models were evaluated on a modified test dataset, where the CO2 production (g/d) was adjusted by subtracting (where measurements were obtained by RC) or adding absolute MB (where measurements were obtained by GF) from evaluation of the specific model on RC, GF, and GF+ test datasets. With this modification, the absolute values of MB and MB as percentage of MSE became negligible. In conclusion, the 3 models were precise in predicting CO2 production from lactating dairy cows.

Dietary concentrate supplementation increases milk production and reduces predicted greenhouse gas emission intensity in pasture-based commercial dairy farms

Controlled studies have extensively documented that concentrate supplements typically increase enteric CH4 emissions and milk yield and reduce emissions per unit of milk produced and dry matter intake. However, no studies have been conducted to determine the effect of concentrate on predicted greenhouse gas emissions from dairy farms representing the Australian pasture-based farming system. Thus, this study sought to determine how dietary concentrate supplementation affects enteric and manure CH4, and N2O of Australian pasture-based dairy farms. The Australian Dairy Carbon Calculator was used, which incorporates emission factors and methodologies used in the National Greenhouse Gas Inventory as reported to the Intergovernmental Panel on Climate Change. Primary data were collected and analyzed from 120 commercial farms in Australia's major dairy regions. Then the farms were divided into 4 groups based on their dietary concentrate supplementation: ≤1 (low; 15 farms), 1 to 2 (moderate; 35 farms), 2 to 3 (high; 35 farms), and ≥3 (very high; 35 farms) t of concentrate dry matter (tDM) per cow per year. Sources of greenhouse gas emissions were CO2 from concentrate production, enteric CH4, and manure CH4 and N2O. Total dry matter intake, milk yield, and daily enteric CH4 production (g/d) quadratically increased with concentrate level, whereas greenhouse gas emission intensity of milk production (kg of CO2 equivalent per kg of fat- and protein-corrected milk) decreased by 14% for farms supplementing with ≥3 tDM/cow per year compared with those supplementing with ≤1 tDM/cow per year of dietary concentrate. The N2O and CH4 emissions from manure increased quadratically and linearly, respectively, with the increasing supplementation of concentrate. Farms supplementing 2 to 3 tDM/cow per year showed substantial increases in gross income, gross margin, earnings before interest and tax, and net income ($/cow per year) compared with those supplementing of ≤1, 1 to 2, and ≥3 tDM/cow per year. Overall, increasing dietary concentrate supplementation for dairy cows resulted in increased milk production per cow, reduced greenhouse gas emissions per unit of milk produced, and increased income and profit. However, a comprehensive life cycle assessment study is needed to account for carbon sequestration by other farm components, such as pastures and trees, which were not considered in the present study. In addition, the present study was based on modeling and did not gather ground truth information for DMI, digestibility, crude protein, and urinary and fecal N excretion. Therefore, data should be interpreted with caution, and studies gathering such information are encouraged.

Industrialization Mitigates Greenhouse Gas Intensity in China's Dairy Sector yet May Prove Insufficient to Offset Emissions from Future Production Expansion

China's dairy farming is undergoing a critical transition from extensive to industrial systems. To achieve sustainable milk production within China's dual-carbon goals, understanding the multidimensional impacts of industrialization on greenhouse gas (GHG) emissions is imperative. This study comprehensively analyzed the implications of China's dairy industrialization on GHG emissions and explored future mitigation potential. Results indicated that industrial systems exhibited lower methane but higher carbon dioxide intensities, with net GHG intensity lower than other systems. During 2002-2020, China's milk production increased by 165%, while GHG emissions increased by 105% to 50.27 Tg CO2eq, accompanying an industrialization rate increased from 16% to 75%. The industrialization progress played a mitigating effect on GHG primarily through intensification within individual production systems before 2008 and transformation between systems post-2008. However, the industrialization's effect was relatively modest compared to other socio-economic factors. By 2030, 11.8 Tg CO2eq will be triggered by predicted milk production growth, but only 0.6 Tg can be offset by system transformation. Integrating measures to improve feed, herd, and manure management on industrial farms could decouple GHG emissions from milk production and achieve a carbon peak before 2030. We suggest transforming to improved industrial systems as a necessary step toward sustainable livestock production.

Invited review: Advances in nutrition and feed additives to mitigate enteric methane emissions

Methane, both enteric and from manure management, is the most important greenhouse gas from ruminant livestock, and its mitigation can deliver substantial decreases in the carbon footprint of animal products and potentially contribute to climate change mitigation. Although choices may be limited, certain feeding-related practices can substantially decrease livestock enteric CH4 emission. These practices can be generally classified into 2 categories: diet manipulation and feed additives. Within the first category, selection of forages and increasing forage digestibility are likely to decrease enteric CH4 emission, but the size of the effect, relative to current forage practices in the United States dairy industry, is likely to be minimal to moderate. An opportunity also exists to decrease enteric CH4 emissions by increasing dietary starch concentration, but interventions have to be weighed against potential decreases in milk fat yield and farm profitability. A similar conclusion can be made about dietary lipids and oilseeds, which are proven to decrease CH4 emission but can also have a negative effect on rumen fermentation, feed intake, and milk production and composition. Sufficient and robust scientific evidence indicates that some feed additives, specifically the CH4 inhibitor 3-nitrooxypropanol, can substantially reduce CH4 emissions from dairy and beef cattle. However, the long-term effects and external factors affecting the efficacy of the inhibitor need to be further studied. The practicality of mass-application of other mitigation practices with proven short-term efficacy (i.e., macroalgae) is currently unknown. One area that needs more research is how nutritional mitigation practices (both diet manipulation and feed additives) interact with each other and whether there is synergism among feed additives with different mode of action. Further, effects of diet on manure composition and greenhouse gas emissions during storage (e.g., emission trade-offs) have not been adequately studied. Overall, if currently available mitigation practices prove to deliver consistent results and novel, potent, and safe strategies are discovered and are practical, nutrition alone can deliver up to 60% reduction in enteric CH4 emissions from dairy farms in the United States.

How much can performance measures explain of the between-cow variation in enteric methane?

Enteric CH4 produced from dairy cows contributes to the emission of greenhouse gases from anthropogenic sources. Recent studies have shown that the selection of lower CH4-emitting cows is possible, but doing so would be simpler if performance measures already recorded on farm could be used, instead of measuring gas emissions from individual cows. These performance measures could be used for selection of low emitting cows. The aim of this analysis was to quantify how much of the between-cow variation in CH4 production can be explained by variation in performance measures. A dataset with 3 experiments and a total of 149 lactating dairy cows with repeated measures was used to estimate the between-cow variation (the variation between cow estimates) for performance and gas measures from GreenFeed (C-Lock, Rapid City, SD). The cow estimates were obtained with a linear mixed model with the diet within period effect as a fixed effect and the cow within experiment as a random effect. The cow estimates for CH4 production were first regressed on the performance and gas measures individually, and then performance and CO2 production measures were grouped in 3 subsets for principal component analysis and principal component regression. The variables that explained most of the between-cow variation in CH4 production were DMI (R2 = 0.44), among the performance measures, and CO2 production (R2 = 0.61), among gas measures. Grouping the measures increased the R2 to 0.53 when only performance measures were used, and to 0.66 when CO2 production was added to the significant performance measures. We found the marginal improvement to be insufficient to justify the use of grouped measures rather than an individual measure because the latter simplifies the model and avoids over-fitting. Investigation of other measures that can be explored to increase explanatory power of between-cow variation in CH4 production is briefly discussed. Finally, the use of residual CH4 as a measure for CH4 efficiency could be considered by using either DMI or CO2 production as the sole predicting variables.

Systematic review for optimizing sample size in dairy cow methane emission studies in temperate regions: a comprehensive methodological approach

This research introduces a systematic framework for calculating sample size in studies focusing on enteric methane (CH4, g/kg of DMI) yield reduction in dairy cows. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search across the Web of Science, Scopus, and PubMed Central databases for studies published from 2012 to 2023. The inclusion criteria were: studies reporting CH4 yield and its variability in dairy cows, employing specific experimental designs (Latin Square Design (LSD), Crossover Design, Randomized Complete Block Design (RCBD), and Repeated Measures Design) and measurement methods (Open-circuit respirometry chambers (RC), the GreenFeed system, and the sulfur hexafluoride tracer technique), conducted in Canada, the United States and Europe. A total of 150 studies, which included 177 reports, met our criteria and were included in the database. Our methodology for using the database for sample size calculations began by defining 6 CH4 yield reduction levels (5, 10, 15, 20, 30, and 50%). Utilizing an adjusted Cohen's f formula and a power analysis we calculated the sample sizes required for these reductions in balanced LSD and RCBD reports from studies involving 3 or 4 treatments. The results indicate that within-subject studies (i.e., LSD) require smaller sample sizes to detect CH4 yield reductions compared with between-subject studies (i.e., RCBD). Although experiments using RC typically require fewer individuals due to their higher accuracy, our results demonstrate that this expected advantage is not evident in reports from RCBD studies with 4 treatments. A key innovation of this research is the development of a web-based tool that simplifies the process of sample size calculation (samplesizecalculator.ucdavis.edu). Developed using Python, this tool leverages the extensive database to provide tailored sample size recommendations for specific experimental scenarios. It ensures that experiments are adequately powered to detect meaningful differences in CH4 emissions, thereby contributing to the scientific rigor of studies in this critical area of environmental and agricultural research. With its user-friendly interface and robust backend calculations, this tool represents a significant advancement in the methodology for planning and executing CH4 emission studies in dairy cows, aligning with global efforts toward sustainable agricultural practices and environmental conservation.

Climate Change and Photochemical Ozone Creation Potential Impact Indicators of Cow Milk: A Comparison of Different Scenarios for a Diet Assessment

An estimate of the environmental impact of dairy farms in Northern Italy producing milk for hard cheese (protected designation of origin) has been obtained through a comprehensive life cycle assessment. The estimate focused on climate change (CC) and photochemical ozone creation potential (POCP) indicators, which were evaluated according to the Intergovernmental Panel on Climate Change (IPCC) guidelines and interpreted with the aid of the feeds' composition evaluated using near-infrared reflectance spectroscopy (Foss NIR-System 5000) as well as with a diet evaluation according to the NRC (National Research Council) or the CNCPS (Cornell Net Carbohydrate and Protein System) nutrient requirement modeling. Herds were classified into high-, mid-, and low-performing based on the daily milk yield per cow. A lower impact on indicators was observed as herd performance increased. The high-performing herds had a lower contribution from enteric fermentation (6.30 × 10-1 kgCO2-eq), and the more milk that they produced allowed for a differentiation of CC from land use and transformation (2.39 × 10-1 kgCO2-eq), compared to low-performing herds (3.66 × 10-1 kgCO2-eq). Compared to the IPCC approach, the CC and POCP indicator estimates were reduced when addressing the feed's quality, particularly in mid- and high-performing herds. The results could be helpful in the dairy sector as they provide an insight into how diet quality affects the environmental impact of milk.

A meta-analysis of probiotic interventions to mitigate ruminal methane emissions in cattle: implications for sustainable livestock farming

In recent years, the significant impact of ruminants on methane emissions has garnered international attention. While dietary strategies have been implemented to solve this issue, probiotics gained the attention of researchers due to their sustainability. However, it is challenging to ascertain their effectiveness as an extensive range of strains and doses have been reported in the literature. Hence, the objective of this experiment was to perform a meta-analysis of probiotic interventions aiming to reduce ruminal methane emissions from cattle. From 362 articles retrieved from scientific databases, 85 articles were assessed independently by two reviewers, and 20 articles representing 49 comparisons were found eligible for meta-analysis. In each study, data such as mean, SD, and sample sizes of both the control and probiotic intervention groups were extracted. The outcomes of interest were methane emission, methane yield, and methane intensity. For the meta-analysis, effect sizes were pooled using a fixed effect or a random effect model depending on the heterogeneity. Afterward, sensitivity analyses were conducted to confirm the robustness of the findings. Overall pooled standardized mean differences (SMDs) with their confidence intervals (CIs) did not detect significant differences in methane emission (SMD = -0.04; 95% CI = -0.18-0.11; P = 0.632), methane yield (SMD = -0.08; 95% CI = -0.24-0.07; P = 0.291), and methane intensity (SMD = -0.22; 95% CI = -0.50-0.07; P = 0.129) between cattle supplemented with probiotics and the control group. However, subgroup analyses revealed that multiple-strain bacterial probiotics (SMD = -0.36; 95% CI = -0.62 to -0.11; P = 0.005), specifically the combination of bacteria involved in reductive acetogenesis and propionate production (SMD = -0.71; 95% CI = -1.04 to -0.36; P = 0.001), emerged as better interventions. Likewise, crossbreeds (SMD = -0.48; 95% CI = -0.78 to -0.18; P = 0.001) exhibited a more favorable response to the treatments. Furthermore, meta-regression demonstrated that longer periods of supplementation led to significant reductions in methane emissions (P = 0.001), yield (P = 0.032), and intensity (P = 0.012) effect sizes. Overall, the results of the current study suggest that cattle responses to probiotic interventions are highly dependent on the probiotic category. Therefore, extended trials performed with probiotics containing multiple bacterial strains are showing the most promising results. Ideally, further trials focusing on the use of probiotics to reduce ruminal methane in cattle should be conducted to complete the available literature.

Feed intake in housed dairy cows: validation of a three-dimensional camera-based feed intake measurement system

Measuring feed intake accurately is crucial to determine feed efficiency and for genetic selection. A system using three-dimensional (3D) cameras and deep learning algorithms can measure the volume of feed intake in dairy cows, but for now, the system has not been validated for feed intake expressed as weight of feed. The aim of this study was to validate the weight of feed intake predicted from the 3D cameras with the actual measured weight. It was hypothesised that diet-specific coefficients are necessary for predicting changes in weight, that the relationship between weight and volume is curvilinear throughout the day, and that manually pushing the feed affects this relationship. Twenty-four lactating Danish Holstein cows were used in a cross-over design with four dietary treatments, 2 × 2 factorial arranged with either grass-clover silage or maize silage as silage factor, and barley or dried beet pulp as concentrate factor. Cows were adapted to the diets for 11 d, and for 3 d to tie-stall housing before camera measurements. Six cameras were used for recording, each mounted over an individual feeding platform equipped with a weight scale. When building the predictive models, four cameras were used for training, and the remaining two for testing the prediction of the models. The most accurate predictions were found for the average feed intake over a period when using the starting density of the feed pile, which resulted in the lowest errors, 6% when expressed as RMSE and 5% expressed as mean absolute error. A model including curvilinear effects of feed volume and the impact of manual feed pushing was used on a dataset including daily time points. When cross-validating, the inclusion of a curvilinear effect and a feed push effect did not improve the accuracy of the model for neither the feed pile nor the feed removed by the cow between consecutive time points. In conclusion, measuring daily feed intake from this 3D camera system in the present experimental setup could be accomplished with an acceptable error (below 8%), but the system should be improved for individual meal intake measurements if these measures were to be implemented.

Efficacy of Salvia officinalis Shrub as a Sustainable Feed Additive for Reducing Ruminal Methane Production and Enhancing Fermentation in Ruminants

The objective of this study was to evaluate the effects of the inclusion of dried Salvia officinalis (SO) shrub leaves on nutrient degradability, ruminal in vitro fermentation, gas production (GP), methane (CH4), and carbon dioxide (CO2) productions. Dried and ground SO shrub leaves were included at 0% (control), 0.5%, 1%, 1.5%, and 2% DM of a diet consisting of (per kg DM) 500 g concentrate feed mixture, 400 g berseem hay, and 100 g rice straw. The diet was incubated for 48 h. The asymptotic GP and the rate of GP changed linearly and quadratically (p < 0.01), with the highest GP observed at 1% inclusion of SO and then decreasing thereafter with greater inclusion (i.e., 1.5% and 2%), while CH4 production and its rate decreased linearly (p < 0.01) with all levels of SO inclusion. A linear increase in CO2 production and its rate was also found with an increasing level of SO inclusion in the diet (p < 0.05). Furthermore, the degradability of DM, NDF, and the concentration of total short-chain fatty acids and acetate changed linearly and quadratically, with the greatest being found at 1% SO inclusion and then steadily declining after (p < 0.01) with the 1.5% and 2% inclusion levels. Meanwhile, the propionate, NH3-N, and microbial crude protein levels showed similar trends, with the plateau found at 1% inclusion of SO, where there was no change in butyrate concentration. Moreover, the pH, metabolizable energy, and partitioning factor (PF24) also changed linearly and quadratically (p < 0.05), where the pH and PF24 were considerably reduced and ME increased with a 1% inclusion of SO (p < 0.05). In summary, SO at 1% inclusion in the diet showed the potential to improve gas production kinetics, nutrient degradability, and the ruminal fermentation profile, with a more significant reduction in ruminal CH4 production suggesting that SO at 1% could be included in the ruminant diet to reduce their carbon footprint and increase the production performance.

Understanding potential opportunities and risks associated with feeding supplemental rumen available fats to mitigate enteric methane emissions in lactating dairy cows

Supplemental dietary rumen available fats show promise as enteric methane (eCH4) mitigators for lactating dairy cows. However, concerns include variability in eCH4 response and possible negative effects on dairy cow performance. Successful implementation of this mitigation option requires better prediction of responses specifically to rumen available FA as well as understanding the modulating effects of other dietary and animal characteristics. Using meta-analytic and meta-regression techniques, 35 published studies with diet definition were used to assess changes in eCH4 emissions and lactation performance associated with supplemental fat, specific supplemental rumen available FA types, and other dietary characteristics. Enteric CH4 (g/d) was reduced by 3.77% per percentage unit of supplemental rumen available EE (RAEE). Supplemental rumen available PUFA (C18:2 and C18:3) and UFA (C18:1, C18:2, C18:3) mitigated eCH4 (g/d) emissions in dairy cows by 6.88 and 4.65% per percentage unit increase, respectively. The anti-methanogenic effects of PUFA, MUFA and MCFA increased with correspondingly greater basal dietary levels of each FA type. Higher rumen-degradable starch (RDS; > 18% DM) in the basal diet promoted greater reductions in eCH4 yield (eCH4/DMI, g/kg) with supplemental rumen available PUFA and UFA. Both milk fat percentage and yield (kg/d) were reduced with rumen available fat supplementation with a reduction of 7.8% and 6.0%, respectively, relative to control diets. Our results highlight the importance of determining basal levels of the rumen available FA before providing supplemental rumen available FA as an option for enteric eCH4 mitigation. Dairy nutritionists can use estimates generated from this analysis to predict changes in eCH4 emissions and dairy cow performance associated with dietary supplementation of rumen available EE and specific rumen available FA types for the purpose of eCH4 mitigation.

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.

Animal factors that affect enteric methane production measured using the GreenFeed monitoring system in grazing dairy cows

Similar to all dairy systems internationally, pasture-based dairy systems are under increasing pressure to reduce their greenhouse gas (GHG) emissions. Ireland and New Zealand are 2 countries operating predominantly pasture-based dairy production systems where enteric CH4 contributes 23% and 36% of total national emissions, respectively. Ireland currently has a national commitment to reduce 51% of total GHG emissions by 2030 and 25% from agriculture by 2030, as well as striving to achieve climate neutrality by 2050. New Zealand's national commitment is to reduce 10% of methane emissions by 2030 and between 24% and 47% reduction in methane emissions by 2050. To achieve these reductions, factors that affect enteric methane (CH4) production in a pasture-based system need to be investigated. The objective of this study was to assess the relationship between enteric CH4 and other animal traits (feed intake, metabolic liveweight, energy corrected milk yield, milk urea concentration, and body condition score [BCS]) in a grazing dairy system. Enteric CH4 emissions were measured on 45 late lactation (213.8 ± 29 d after calving) grazing Holstein-Friesian and Holstein-Friesian × Jersey crossbred cows (lactation number 3.01 ± 1.65, 538.64 ± 59.37 kg live weight, and 3.14 ± 0.26 BCS) using GreenFeed monitoring equipment for 10 wk. There was a training period for the cows to use the GreenFeed of 3 wk before the 10-wk study period. The average enteric CH4 produced in the study was 352 g ± 45.7 g per day with an animal to animal coefficient of variation of 13%. Dry matter intake averaged 16.6 kg ± 2.23 kg per day, while milk solids (fat plus protein) averaged 1.62 kg ± 0.29 kg per day. A multiple linear regression model indicated that each one unit increase in energy corrected milk yield, metabolic liveweight and milk urea concentration, resulted in an increase in enteric CH4 production per day by 3.9, 1.74, and 1.38 g, respectively. Although each one unit increase in BCS resulted in a decrease in 39.03 g CH4 produced per day. When combined, these factors explained 47% of the variation in CH4 production, indicating that there is a large proportion of variation not included in the model. The repeatability of the CH4 measurements was 0.66 indicating that cows are relatively consistently exhibiting the same level of CH4 throughout the study. Therefore, enteric CH4 production is suitable for phenotyping.

Quantifying the Impact of Different Dietary Rumen Modulating Strategies on Enteric Methane Emission and Productivity in Ruminant Livestock: A Meta-Analysis

A meta-analysis was conducted with an aim to quantify the beneficial effects of nine different dietary rumen modulating strategies which includes: the use of plant-based bioactive compounds (saponin, tannins, oils, and ether extract), feed additives (nitrate, biochar, seaweed, and 3-nitroxy propanol), and diet manipulation (concentrate feeding) on rumen fermentation, enteric methane (CH4) production (g/day), CH4 yield (g/kg dry matter intake) and CH4 emission intensity (g/kg meat or milk), and production performance parameters (the average daily gain, milk yield and milk quality) of ruminant livestock. The dataset was constructed by compiling global data from 110 refereed publications on in vivo studies conducted in ruminants from 2005 to 2023 and anlayzed using a meta-analytical approach.. Of these dietary rumen manipulation strategies, saponin and biochar reduced CH4 production on average by 21%. Equally, CH4 yield was reduced by 15% on average in response to nitrate, oils, and 3-nitroxy propanol (3-NOP). In dairy ruminants, nitrate, oils, and 3-NOP reduced the intensity of CH4 emission (CH4 in g/kg milk) on average by 28.7%. Tannins and 3-NOP increased on average ruminal propionate and butyrate while reducing the acetate:propionate (A:P) ratio by 12%, 13.5% and 13%, respectively. Oils increased propionate by 2% while reducing butyrate and the A:P ratio by 2.9% and 3.8%, respectively. Use of 3-NOP increased the production of milk fat (g/kg DMI) by 15% whereas oils improved the yield of milk fat and protein (kg/d) by 16% and 20%, respectively. On the other hand, concentrate feeding improved dry matter intake and milk yield (g/kg DMI) by 23.4% and 19%, respectively. However, feed efficiency was not affected by any of the dietary rumen modulating strategies. Generally, the use of nitrate, saponin, oils, biochar and 3-NOP were effective as CH4 mitigating strategies, and specifically oils and 3-NOP provided a co-benefit of improving production parameters in ruminant livestock. Equally concentrate feeding improved production parameters in ruminant livestock without any significant effect on enteric methane emission. Therefore, it is advisable to refine further these strategies through life cycle assessment or modelling approaches to accurately capture their influence on farm-scale production, profitability and net greenhouse gas emissions. The adoption of the most viable, region-specific strategies should be based on factors such as the availability and cost of the strategy in the region, the specific goals to be achieved, and the cost-benefit ratio associated with implementing these strategies in ruminant livestock production systems.

Climate and local environment co-mediate the taxonomic and functional diversity of bacteria and archaea in the Qinghai-Tibet Plateau rivers

Understanding the environmental response patterns of riverine microbiota is essential for predicting the potential impact of future environmental change on river ecosystems. Vulnerable plateau ecosystems are particularly sensitive to climate and local environmental changes, however, the environmental response patterns of the taxonomic and functional diversity of riverine microbiota remain unclear. Here, we conducted a systematic investigation of the taxonomic and functional diversity of bacteria and archaea from riparian soils, sediments, and water across the elevation of 1800- 4800 m in the Qinghai-Tibet Plateau rivers. We found that within the elevation range of 1800 to 3800 m, riparian soils and sediments exhibited similarities and stabilities in microbial taxonomic and functional diversity, and water microbiomes were more sensitive with great fluctuations in microbial diversity. Beyond the elevation of 3800 m, microbial diversity declined across all riverine matrixes. Local environmental conditions can influence the sensitivity of microbiomes to climate change. The combination of critical climate and local environmental factors, including total nitrogen, total organic carbon, as well as climate variables associated with temperature and precipitation, provided better explanations for microbial diversity than single-factor analyses. Under the extremely adverse scenario of high greenhouse gas emission concentrations (SSP585), we anticipate that by the end of this century, the bacterial, archaeal, and microbial functional diversity across the river network of the Yangtze and Yellow source basin would potentially change by -16.9- 5.2 %, -16.1- 5.7 %, and -9.3- 6.4 %, respectively. Overall, climate and local environments jointly shaped the microbial diversity in plateau river ecosystems, and water microbiomes would provide early signs of environmental changes. Our study provides effective theoretical foundations for the conservation of river biodiversity and functional stability under environmental changes.

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

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

Ammonia and methane emissions from small herd cattle buildings in a cold climate

Ammonia (NH3) and methane (CH4) emission measurements that reflect local production conditions are required to track progress in national emission policies and verify emission factors. The findings can also be used to better understand key factors influencing emissions. This is especially important in Norway, which has long cold winters, and small cattle herds in mechanically ventilated buildings. However, until now, NH3 and CH4 emissions from Norwegian cattle buildings have not been reported in literature. Moreover, in other cold climates, NH3 and CH4 emissions are often taken from large dairy herds in naturally ventilated buildings, with less focus on suckler cows. The objectives were to assess indoor climate, report NH3 and CH4 emissions and examine the impact of climatic factors on NH3 and CH4 emissions in three small herd dairy and suckler cow buildings over three seasons. Three of the buildings had mechanical ventilation, while one was naturally ventilated. The suckler building had higher relative humidity (RH > 90 %) and NH3 concentrations (> 25 ppm) due to lower minimum air change rate (ACH = 1.2 h-1). The suckler building also had the highest NH3 emissions (2.04 g Livestock Unit (LU)-1 h-1) followed by the mechanically ventilated dairy building (1.92 g LU-1 h-1) with the highest ACH. These two buildings had the lowest stocking densities and floor areas. In contrast, the suckler building had the lowest CH4 emissions (6.8-10.7 g LU-1 h-1). Methane emissions from the dairy building with the supply-exhaust air mixing system (16.4-19.3 g LU-1 h-1) was higher than the other dairy buildings (11.7-13.8 g LU-1 h-1). Temperature influenced NH3 emissions however, the direction of association between temperature and NH3 emissions differed among buildings. Relationship between RH and NH3 emissions was positive, but the correlation coefficient (R2 = 0.67) was strongest in the building with the highest RH.

Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy

Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin's concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.

Dose- and substrate-dependent reduction of enteric methane and ammonia by natural additives in vitro

Ruminants contribute to global warming by emitting greenhouse gasses, particularly methane (CH4) which is a product of rumen fermentation. The use of feed additives able to modulate rumen fermentation is a promising strategy to reduce enteric CH4 and ammonia (NH3) emissions. Among the various strategies investigated, plant secondary metabolites (PSMs) have attracted attention due to their apparent potential to reduce enteric CH4 and NH3 emissions, and it would be possible to use such compounds as feed additives in organic production systems. In an in vitro system simulating rumen fermentation, we have tested the impact of different classes of naturally occurring PSMs; catechin and quercetin (flavonoids), salicylic acid (phenolic acid) and tannic acid (hydrolysable tannin). The PSMs were added to two different basal feeds (maize and grass silages) at three inclusion doses 1.5, 3 and 6% of the feed dry matter (DM). CH4 production was significantly lowered upon addition of quercetin to two basal feeds at doses of 3 and 6%, and this without changes in concentrations of total volatile fatty acid (VFA) produced during fermentation. Quercetin, as the only tested additive, reduced CH4 production, and when added to maize silage and grass silage, the reduction increased linearly with increasing dose, ie., by 51 and 43%, respectively, at a dose of 3% of feed DM and by 86 and 58%, respectively, at a dose of 6% of feed DM. Moreover, quercetin significantly reduced NH3 concentration by >12% at doses of 3 and 6% in feed DM irrespective of the basal feed used as compared to when the basal feeds were incubated alone. Although none of the other additives affected CH4 formation, several additives had significant impacts on concentrations of NH3 and VFAs in the incubated fluid after fermentation. This study demonstrated a dose-dependent ability of quercetin to reduce CH4 emission from rumen fermentation, however, the magnitude of the suppression of CH4 depended on the basal feed. Furthermore, quercetin reduced NH3 concentration irrespective of the basal feed type. These findings encourage to in vivo studies to verify whether quercetin can reduce CH4 emission also in cows.

Exhaled volatile fatty acids, ruminal methane emission, and their diurnal patterns in lactating dairy cows

To date, the commonly used methods to assess rumen fermentation are invasive. Exhaled breath contains hundreds of volatile organic compounds (VOC) that can reflect animal physiological processes. In the present study, for the first time, we aimed to use a noninvasive metabolomics approach based on high-resolution mass spectrometry to identify rumen fermentation parameters in dairy cows. Enteric methane (CH4) production from 7 lactating cows was measured 8 times over 3 consecutive days using the GreenFeed system (C-Lock Technology Inc.). Simultaneously, exhalome samples were collected in Tedlar gas sampling bags and analyzed offline using a secondary electrospray ionization high-resolution mass spectrometry system. In total, 1,298 features were detected, among them targeted exhaled volatile fatty acids (eVFA; i.e., acetate, propionate, butyrate), which were putatively annotated using their exact mass-to-charge ratio. The intensity of eVFA, in particular acetate, increased immediately after feeding and followed a similar pattern to that observed for ruminal CH4 production. The average total eVFA concentration was 35.5 count per second (CPS), and among the individual eVFA, acetate had the greatest concentration, averaging 21.3 CPS, followed by propionate at 11.5 CPS, and butyrate at 2.67 CPS. Further, exhaled acetate was on average the most abundant of the individual eVFA at around 59.3%, followed by 32.5 and 7.9% of the total eVFA for propionate and butyrate, respectively. This corresponds well with the previously reported proportions of these VFA in the rumen. The diurnal patterns of ruminal CH4 emission and individual eVFA were characterized using a linear mixed model with cosine function fit. The model characterized similar diurnal patterns for eVFA and ruminal CH4 and H2 production. Regarding the diurnal patterns of eVFA, the phase (time of peak) of butyrate occurred first, followed by that of acetate and propionate. Importantly, the phase of total eVFA occurred around 1 h before that of ruminal CH4. This corresponds well with existing data on the relationship between rumen VFA production and CH4 formation. Results from the present study revealed a great potential to assess the rumen fermentation of dairy cows using exhaled metabolites as a noninvasive proxy for rumen VFA. Further validation, with comparisons to rumen fluid, and establishment of the proposed method are required.

Methane reduction by quercetin, tannic and salicylic acids: influence of molecular structures on methane formation and fermentation in vitro

Plant secondary metabolites (PSMs) can potentially reduce ruminal methane formation. However, related to differences in their molecular structures, it is not yet clear what causes an anti-methanogenic effect. In an in vitro system simulating rumen fermentation, we investigated the impact of eight compounds with distinct chemical characteristics (gallic and salicylic acids, tannic acid, catechin, epicatechin, quercetin, rutin, and salicin) when added to a basal feed (maize silage) at a concentration of 12% of the feed dry matter. After 48 h of incubation in buffered rumen fluid, methane production was significantly lowered by quercetin (43%), tannic acid (39%) and salicylic acid (34%) compared to the control (maize silage alone) and without changes in total volatile fatty acid production during fermentation. No other PSM reduced methane formation as compared to control but induced significant differences on total volatile fatty acid production. The observed differences were related to lipophilicity, the presence of double bond and carbonyl group, sugar moieties, and polymerization of the compounds. Our results indicate the importance of distinct molecular structures of PSMs and chemical characteristics for methane lowering properties and volatile fatty acid formation. Further systematic screening studies to establish the structure-function relationship between PSMs and methane reduction are warranted.

Effect of nitrate supplementation, dietary protein supply, and genetic yield index on performance, methane emission, and nitrogen efficiency in dairy cows

The objective was to investigate the effect of nonprotein nitrogen source, dietary protein supply, and genetic yield index on methane emission, N metabolism, and ruminal fermentation in dairy cows. Forty-eight Danish Holstein dairy cows (24 primiparous cows and 24 multiparous cows) were used in a 6 × 4 incomplete Latin square design with 4 periods of 21-d duration. Cows were fed ad libitum with the following 6 experimental diets: diets with low, medium, or high rumen degradable protein (RDP):rumen undegradable protein (RUP) ratio (manipulated by changing the proportion of corn meal, corn gluten meal, and corn gluten feed) combined with either urea or nitrate (10 g NO₃^-/kg of dry matter) as nonprotein nitrogen source. Samples of ruminal fluid and feces were collected from multiparous cows, and total-tract nutrient digestibility was estimated using TiO₂ as flow marker. Milk samples were collected from all 48 cows. Gas emission (CH₄, CO₂, and H₂) was measured by 4 GreenFeed units. We observed no significant interaction between dietary RDP:RUP ratio and nitrate supplementation, and between nitrate supplementation and genetic yield index on CH₄ emission (production, yield, intensity). As dietary RDP:RUP ratio increased, intake of crude protein, RDP, and neutral detergent fiber and total-tract digestibility of crude protein linearly increased, and RUP intake linearly decreased. Yield of milk, energy-corrected milk, and milk protein and lactose linearly decreased, whereas milk fat and milk urea nitrogen concentrations linearly increased as dietary RDP:RUP ratio increased. The increase in dietary RDP:RUP ratio resulted in a linear increase in the excretion of total purine derivatives and N in urine, but a linear decrease in N efficiency (milk N in % of N intake). Nitrate supplementation reduced dry matter intake (DMI) and increased total-tract organic matter digestibility compared with urea supplementation. Nitrate supplementation resulted in a greater reduction in DMI and daily CH₄ production and a greater increase in daily H₂ production in multiparous cows compared with primiparous cows. Nitrate supplementation also showed a greater reduction in milk protein and lactose yield in multiparous cows than in primiparous cows. Milk protein and lactose concentrations were lower for cows receiving nitrate diets compared with cows receiving urea diets. Nitrate supplementation reduced urinary purine derivatives excretion from the rumen, whereas N efficiency tended to increase. Nitrate supplementation reduced proportion of acetate and propionate in ruminal volatile fatty acids. In conclusion, no interaction was observed between dietary RDP:RUP ratio and nitrate supplementation, and no interaction between nitrate supplementation and genetic yield index on CH₄ emission (production, yield, intensity) was noted. Nitrate supplementation resulted in a greater reduction in DMI and CH₄ production, and a greater increase in H₂ production in multiparous cows than in primiparous cows. As the dietary RDP:RUP ratio increased, CH₄ emission was unaffected and RDP intake increased, but RUP intake and milk yield decreased. Genetic yield index did not affect CH₄ production, yield, or intensity.

Dietary supplementation with xylooligosaccharides and exogenous enzyme improves milk production, energy utilization efficiency and reduces enteric methane emissions of Jersey cows

BACKGROUND

Sustainable strategies for enteric methane (CH₄) mitigation of dairy cows have been extensively explored to improve production performance and alleviate environmental pressure. The present study aimed to investigate the effects of dietary xylooligosaccharides (XOS) and exogenous enzyme (EXE) supplementation on milk production, nutrient digestibility, enteric CH₄ emissions, energy utilization efficiency of lactating Jersey dairy cows. Forty-eight lactating cows were randomly assigned to one of 4 treatments: (1) control diet (CON), (2) CON with 25 g/d XOS (XOS), (3) CON with 15 g/d EXE (EXE), and (4) CON with 25 g/d XOS and 15 g/d EXE (XOS + EXE). The 60-d experimental period consisted of a 14-d adaptation period and a 46-d sampling period. The enteric CO₂ and CH₄ emissions and O₂ consumption were measured using two GreenFeed units, which were further used to determine the energy utilization efficiency of cows.

RESULTS

Compared with CON, cows fed XOS, EXE or XOS + EXE significantly (P < 0.05) increased milk yield, true protein and fat concentration, and energy-corrected milk yield (ECM)/DM intake, which could be reflected by the significant improvement (P < 0.05) of dietary NDF and ADF digestibility. The results showed that dietary supplementation of XOS, EXE or XOS + EXE significantly (P < 0.05) reduced CH₄ emission, CH₄/milk yield, and CH₄/ECM. Furthermore, cows fed XOS demonstrated highest (P < 0.05) metabolizable energy intake, milk energy output but lowest (P < 0.05) of CH₄ energy output and CH₄ energy output as a proportion of gross energy intake compared with the remaining treatments.

CONCLUSIONS

Dietary supplementary of XOS, EXE or combination of XOS and EXE contributed to the improvement of lactation performance, nutrient digestibility, and energy utilization efficiency, as well as reduction of enteric CH₄ emissions of lactating Jersey cows. This promising mitigation method may need further research to validate its long-term effect and mode of action for dairy cows.

Factors Affecting Enteric Emission Methane and Predictive Models for Dairy Cows

Enteric methane emission is the main source of greenhouse gas contribution from dairy cattle. Therefore, it is essential to evaluate drivers and develop more accurate predictive models for such emissions. In this study, we built a large and intercontinental experimental dataset to: (1) explain the effect of enteric methane emission yield (g methane/kg diet intake) and feed conversion (kg diet intake/kg milk yield) on enteric methane emission intensity (g methane/kg milk yield); (2) develop six models for predicting enteric methane emissions (g/cow/day) using animal, diet, and dry matter intake as inputs; and to (3) compare these 6 models with 43 models from the literature. Feed conversion contributed more to enteric methane emission (EME) intensity than EME yield. Increasing the milk yield reduced EME intensity, due more to feed conversion enhancement rather than EME yield. Our models predicted methane emissions better than most external models, with the exception of only two other models which had similar adequacy. Improved productivity of dairy cows reduces emission intensity by enhancing feed conversion. Improvement in feed conversion should be prioritized for reducing methane emissions in dairy cattle systems.

Replacing ground corn with soyhulls plus palmitic acid in low metabolizable protein diets with or without rumen-protected amino acids: Effects on production and nutrient utilization in dairy cows

We previously observed that diets with reduced starch concentration decreased yields of milk and milk protein in dairy cows fed low metabolizable protein diets. Supplementation of reduced-starch diets with a lipid source may attenuate or eliminate production losses. Our objective was to investigate the effects of partially replacing ground corn with soyhulls plus a palmitic acid-enriched supplement on dry matter (DM) intake, milk yield and composition, plasma AA concentration, and N and energy utilization in cows fed low metabolizable protein diets (mean = -68 g/d balance) with or without rumen-protected Met, Lys, and His (RP-MLH). Sixteen multiparous Holstein cows averaging (mean ± standard deviation) 112 ± 28 d in milk, 724 ± 44 kg of body weight, and 46 ± 5 kg/d of milk in the beginning of the study were used in a replicated 4 × 4 Latin square design with a 2 × 2 factorial arrangement of treatments. Each period lasted 21 d, consisting of 14 d for diet adaptation and 7 d for data and sample collection. Diets were fed as follows: (1) high starch (HS), (2) HS plus RP-MLH (HS+AA), (3) reduced starch plus a palmitic acid-enriched supplement (RSPA), and (4) RSPA plus RP-MLH (RSPA+AA). The HS diet contained (DM basis) 26% ground corn and 7% soyhulls, and the RSPA diet had 10% ground corn, 22% soyhulls, and 1.5% palmitic acid. The HS diet averaged (DM basis) 32.6% starch and 4% ether extract, while starch and ether extract concentrations of the RSPA diet were 21.7 and 5.9%, respectively. All 4 diets had (DM basis) 40% corn silage, 5% mixed-mostly grass haylage, 5% grass hay, and 50% concentrate. Diets did not affect DM intake and milk yield. Contrarily, feeding RSPA and RSPA+AA increased yields of energy-corrected milk (47.0 vs. 44.8 kg/d) and milk fat (1.65 vs. 1.50 kg/d) compared with HS and HS+AA. Milk fat concentration tended to decrease when RP-MLH was supplemented to HS, but no change was seen when added to RS (starch level × RP-MLH interaction). Milk and plasma urea N increased, and milk N efficiency decreased in cows fed RSPA and RSPA+AA versus HS and HS+AA. Apparent total-tract digestibilites of crude protein and neutral detergent fiber, as well as urinary urea N and total N excretion, were greater in cows offered RSPA and RSPA+AA than HS and HS+AA. Plasma Met and His concentrations increased with supplemental RP-MLH. Intake of gross energy and digestible energy and the output of urinary and milk energy were all greater with feeding RSPA and RSPA+AA versus HS and HS+AA. In summary, partially replacing ground corn with soyhulls plus palmitic acid in diets supplemented or not with RP-MLH increased milk fat yield and fiber digestibility and maintained DM intake and milk yield, but with decreased milk N efficiency and elevated urinary N excretion.

Modeling the effects of heat stress in animal performance and enteric methane emissions in lactating dairy cows

Heat stress (HS) negatively affects dry matter intake (DMI), milk yield (MY), feed efficiency (FE), and free water intake (FWI) in dairy cows, with detrimental consequences to animal welfare, health, and profitability of dairy farms. Absolute enteric methane (CH₄) emission, yield (CH₄/DMI), and intensity (CH₄/MY) may also be affected. Therefore, the goal of this study was to model the changes in dairy cow productivity, water intake, and absolute CH₄ emissions, yield, and intensity with the progression (days of exposure) of a cyclical HS period in lactating dairy cows. Heat stress was induced by increasing the average temperature by 15°C (from 19°C in the thermoneutral period to 34°C) while keeping relative humidity constant at 20% (temperature-humidity index peaks of approximately 83) in climate-controlled chambers for up to 20 d. A database composed of individual records (n = 1,675) of DMI and MY from 82 heat-stressed lactating dairy cows housed in environmental chambers from 6 studies was used. Free water intake was also estimated based on DMI, dry matter, crude protein, sodium, and potassium content of the diets, and ambient temperature. Absolute CH₄ emissions was estimated based on DMI, fatty acids, and dietary digestible neutral detergent fiber content of the diets. Generalized additive mixed-effects models were used to describe the relationships of DMI, MY, FE, and absolute CH₄ emissions, yield, and intensity with HS. Dry matter intake and absolute CH₄ emissions and yield reduced with the progression of HS up to 9 d, when it started to increase again up to 20 d. Milk yield and FE reduced with the progression of HS up to 20 d. Free water intake (kg/d) decreased during the exposure to HS mainly because of a reduction in DMI; however, when expressed in kg/kg of DMI it increased modestly. Methane intensity also reduced initially up to d 5 during HS exposure but then started to increase again following the DMI and MY pattern up to d 20. However, the reductions in CH₄ emissions (absolute, yield, and intensity) occurred at the expense of decreases in DMI, MY, and FE, which are not desirable. This study provides quantitative predictions of the changes in animal performance (DMI, MY, FE, FWI) and CH₄ emissions (absolute, yield, and intensity) with the progression of HS in lactating dairy cows. The models developed in this study could be used as a tool to help dairy nutritionists to decide when and how to adopt strategies to mitigate the negative effects of HS on animal health and performance and related environmental costs. Thus, more precise and accurate on-farm management decisions could be taken with the use of these models. However, application of the developed models outside of the ranges of temperature-humidity index and period of HS exposure included in this study is not recommended. Also, validation of predictive capacity of the models to predict CH₄ emissions and FWI using data from in vivo studies where these variables are measured in heat-stressed lactating dairy cows is required before these models can be used.

Effect of a garlic and citrus extract supplement on performance, rumen fermentation, methane production, and rumen microbiome of dairy cows

The aim of this trial was to determine the effect of a garlic and citrus extract supplement (GCE) on the performance, rumen fermentation, methane emissions, and rumen microbiome of dairy cows. Fourteen multiparous Nordic Red cows in mid-lactation from the research herd of Luke (Jokioinen, Finland) were allocated to 7 blocks in a complete randomized block design based on body weight, days in milk, dry matter intake (DMI), and milk yield. Animals within each block were randomly allocated to a diet with or without GCE. The experimental period for each block of cows (one for each of the control and GCE groups) consisted of 14 d of adaptation followed by 4 d of methane measurements inside the open circuit respiration chambers, with the first day being considered as acclimatization. Data were analyzed using the GLM procedure of SAS (SAS Institute Inc.). Methane production (g/d) and methane intensity (g/kg of energy-corrected milk) were lower by 10.3 and 11.7%, respectively, and methane yield (g/kg of DMI) tended to be lower by 9.7% in cows fed GCE compared with the control. Dry matter intake, milk production, and milk composition were similar between treatments. Rumen pH and total volatile fatty acid concentrations in rumen fluid were similar, whereas GCE tended to increase molar propionate concentration and decrease the molar ratio of acetate to propionate. Supplementation with GCE resulted in greater abundance of Succinivibrionaceae, which was associated with reduced methane. The relative abundance of the strict anaerobic Methanobrevibacter genus was reduced by GCE. The change in microbial community and rumen propionate proportion may explain the decrease in enteric methane emissions. In conclusion, feeding GCE to dairy cows for 18 d modified rumen fermentation and microbiota, leading to reduced methane production and intensity without compromising DMI or milk production in dairy cows. This could be an effective strategy for enteric methane mitigation of dairy cows.

Evaluation of Intergovernmental Panel on Climate Change (IPCC) equations to predict enteric methane emission from lactating cows fed Mediterranean diets

The study aimed to evaluate Intergovernmental Panel on Climate Change (IPCC) Tier 2 (2006 and 2019) to predict enteric CH4 emissions from lactating cows fed Mediterranean diets. The effects of the CH4 conversion factor (Ym; CH4 energy loss as a percentage of gross energy intake) and digestible energy (DE) of the diet were evaluated as model predictors. A data set was created using individual observations derived from 3 in vivo studies on lactating dairy cows housed in respiration chambers and fed diets typical of the Mediterranean region based on silages and hays. Five models using different Ym and DE were evaluated following a Tier 2 approach: (1) average values of Ym (6.5%) and DE (70%) from IPCC (2006); (2) average value of Ym (5.7%) and DE (70.0%) from IPCC (2019; 1YM); (3) Ym = 5.7% and DE measured in vivo (1YMIV); (4) Ym = 5.7 or 6.0%, depending on dietary NDF, and DE = 70% (2YM); and (5) Ym = 5.7 or 6.0%, depending on dietary NDF, and DE measured in vivo (2YMIV). Finally, a Tier 2 model for Mediterranean diets (MED) was derived from the Italian data set (Ym = 5.58%; DE = 69.9% for silage-based diets and 64.8% for hay-based diets) and validated on an independent data set of cows fed Mediterranean diets. The most accurate models tested were 2YMIV, 2YM, and 1YMIV with predictions of 384, 377, and 377 (g of CH4/d), respectively, versus the in vivo value of 381. The most precise model was 1YM (slope bias = 1.88%; r = 0.63). Overall, 1YM showed the highest concordance correlation coefficient value (0.579), followed by 1YMIV (0.569). Cross-validation on an independent data set of cows fed Mediterranean diets (corn silage and alfalfa hay) resulted in concordance correlation coefficient of 0.492 and 0.485 for 1YM and MED, respectively. The prediction of MED (397) was more accurate than 1YM (405) in comparison with the corresponding in vivo value of 396 g of CH4/d. The results of this study showed that the average values proposed by IPCC (2019) can adequately predict CH4 emissions from cows fed typical Mediterranean diets. However, the use of specific factors for the Mediterranean area, such as DE, improved the accuracy of the models.

Enteric Methane Emissions Prediction in Dairy Cattle and Effects of Monensin on Methane Emissions: A Meta-Analysis

Greenhouse gas emissions, such as enteric methane (CH₄) from ruminant livestock, have been linked to global warming. Thus, easily applicable CH₄ management strategies, including the inclusion of dietary additives, should be in place. The objectives of the current study were to: (i) compile a database of animal records that supplemented monensin and investigate the effect of monensin on CH₄ emissions; (ii) identify the principal dietary, animal, and lactation performance input variables that predict enteric CH₄ production (g/d) and yield (g/kg of dry matter intake DMI); (iii) develop empirical models that predict CH₄ production and yield in dairy cattle; and (iv) evaluate the newly developed models and published models in the literature. A significant reduction in CH₄ production and yield of 5.4% and 4.0%, respectively, was found with a monensin supplementation of ≤24 mg/kg DM. However, no robust models were developed from the monensin database because of inadequate observations under the current paper's inclusion/exclusion criteria. Thus, further long-term in vivo studies of monensin supplementation at ≤24 mg/kg DMI in dairy cattle on CH₄ emissions specifically beyond 21 days of feeding are reported to ensure the monensin effects on the enteric CH₄ are needed. In order to explore CH₄ predictions independent of monensin, additional studies were added to the database. Subsequently, dairy cattle CH₄ production prediction models were developed using a database generated from 18 in vivo studies, which included 61 treatment means from the combined data of lactating and non-lactating cows (COM) with a subset of 48 treatment means for lactating cows (LAC database). A leave-one-out cross-validation of the derived models showed that a DMI-only predictor model had a similar root mean square prediction error as a percentage of the mean observed value (RMSPE, %) on the COM and LAC database of 14.7 and 14.1%, respectively, and it was the key predictor of CH₄ production. All databases observed an improvement in prediction abilities in CH₄ production with DMI in the models along with dietary forage proportion inclusion and the quadratic term of dietary forage proportion. For the COM database, the CH₄ yield was best predicted by the dietary forage proportion only, while the LAC database was for dietary forage proportion, milk fat, and protein yields. The best newly developed models showed improved predictions of CH₄ emission compared to other published equations. Our results indicate that the inclusion of dietary composition along with DMI can provide an improved CH₄ production prediction in dairy cattle.

Relevance of sward structure and forage nutrient contents in explaining methane emissions from grazing beef cattle and sheep

Forage nutrient contents are an important factor explaining the dry matter intake (DMI), average daily gain (ADG), and methane emissions (CH₄) of ruminants fed indoors. However, for grazing animals, the forage nutrient contents might be limited in explaining such response variables. We aimed to verify the explanatory power of forage nutrient contents and sward structure on daily intake, performance, and CH₄ emissions by sheep and beef cattle grazing different grassland types in southern Brazil. We analyzed data from five grazing trials using sheep and beef cattle grazing on Italian ryegrass (Lolium multiflorum), mixed Italian ryegrass and black oat (Lolium multiflorum + Avena strigosa), pearl millet (Pennisetum americanum), and multispecies native grassland. We used mixed models, including the forage nutrient contents [crude protein (CP), neutral detergent fiber (NDF), and acid detergent fiber (ADF)], sward structure (sward height and herbage mass) and their interactions, as fixed effects and trial, season, methodologies, animal species, grassland type, and paddock, as random effects. The model for DMI (kg DM/LW^0.75) had an adjusted coefficient of determination (R²_adj) of 71.6 %, where 11.3, 23.1, and 37.2 % of the R²_adj were explained by the forage nutrient contents, sward structure, and their interaction, respectively. The ADG (kg/LW^0.75) model presented an R²_adj of 74.2 %, with 12.5 % explained by forage nutrient contents, 29.3 % by sward structure, and 32.4 % by their interaction. The daily CH₄ emission (g/LW^0.75) model had a lower adjusted coefficient of determination (R²_adj = 47.6 %), with 16.8 % explained by forage nutrient contents and 30.8 % explained by sward structure, but no effect of the interaction. Our results show that in grazing ecosystems, the forage nutrient contents explain a small fraction, and the greater explanatory power for DMI, ADG, and CH₄ emissions models is related to sward structure descriptors, such as sward height and herbage mass. Moreover, the interaction between these variables explains most of the variation. In conclusion, forage nutrient contents and sward structure have different influences on DMI, ADG, and CH₄ emissions by grazing ruminants. Because of its relevance to daily CH₄ emissions, offering an optimal sward structure to grazing animals is a major climate-smart strategy to improve animal production and mitigate CH₄ emissions in pastoral ecosystems.

Genetic Analysis of Methane Emission Traits in Holstein Dairy Cattle

Genetic selection can be a feasible method to help mitigate enteric methane emissions from dairy cattle, as methane emission-related traits are heritable and genetic gains are persistent and cumulative over time. The objective of this study was to estimate heritability of methane emission phenotypes and the genetic and phenotypic correlations between them in Holstein cattle. We used 1765 individual records of methane emission obtained from 330 Holstein cattle from two Canadian herds. Methane emissions were measured using the GreenFeed system, and three methane traits were analyzed: the amount of daily methane produced (g/d), methane yield (g methane/kg dry matter intake), and methane intensity (g methane/kg milk). Genetic parameters were estimated using univariate and bivariate repeatability animal models. Heritability estimates (±SE) of 0.16 (±0.10), 0.27 (±0.12), and 0.21 (±0.14) were obtained for daily methane production, methane yield, and methane intensity, respectively. A high genetic correlation (rg = 0.94 ± 0.23) between daily methane production and methane intensity indicates that selecting for daily methane production would result in lower methane per unit of milk produced. This study provides preliminary estimates of genetic parameters for methane emission traits, suggesting that there is potential to mitigate methane emission in Holstein cattle through genetic selection.

Methane emission, nutrient digestibility, and rumen microbiota in Holstein heifers fed 14 different grass or clover silages as the sole feed

This experiment investigated the variation in enteric methane production and associated gas exchange parameters, nutrient digestibility, rumen fermentation, and rumen microbiome when a range of silages based on different forage types (grass or clover), and different species within the 2 types, were fed as the sole feed to heifers. Three grass species (perennial ryegrass, festulolium, and tall fescue) and 2 clover species (red clover and white clover) were included. Perennial ryegrass was harvested at 2 maturity stages in the primary growth, white clover was harvested once in the primary growth, and 4 cuts of festulolium and tall fescue and 3 cuts of red clover were harvested during the growing season, giving 14 different silage batches in total. Sixteen Holstein heifers 16 to 21 mo old and 2 to 5 mo in pregnancy were fed the silages ad libitum as the sole feed in an incomplete crossover design. Each silage was fed to 4 heifers, except for the 2 perennial ryegrass silages, which were fed to 8 heifers; in total 64 observations. The CH₄ production was measured for 3 d in respiration chambers. Heifers fed clover silages had higher dry matter intake (DMI) compared with heifers fed grass silages, and heifers fed tall fescue silages had the numerically the lowest DMI. Compared with grass silages, feeding clover silages led to higher crude protein digestibility but lower neutral detergent fiber (NDF) digestibility. Rumen pH was higher in heifers fed clover silages compared with those fed grass silages. Based on composition analysis, the rumen microbiota of the heifers clustered clearly according to forage type and species. More specifically, 7 of the 34 dominating rumen bacterial genus-level groups showed higher relative abundances for the clover silages, whereas 7 genus-level groups showed higher abundances for the grass silages. Methane yield was higher for heifers fed grass silages than for those fed clover silages when methane production was related to dry matter and digestible organic matter intake, whereas the opposite was seen when related to NDF digestion. The gross energy lost as methane (CH₄ conversion factor, %) reduced from 7.5% to 6.7%, equivalent to an 11% reduction. The present study gives the outlines for choosing the optimal forage type and forage species with respect to nutrient digestibility and enteric methane emission in ruminants.

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries

On-farm methane (CH₄) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH₄ have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH₄ emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH₄ inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH₄ emission (g d^-1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH₄ yield (g kg^-1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH₄ emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH₄ prediction for beef cattle in inventories of LAC countries based either on more or less readily available data.

Feeding 3-nitrooxypropanol reduces methane emissions by feedlot cattle on tropical conditions

Our objective was to evaluate the effects of feeding 3-nitrooxypropanol (3-NOP; Bovaer, DSM Nutritional Products) at two levels on methane emissions, nitrogen balance, and performance by feedlot cattle. In experiment 1, a total of 138 Nellore bulls (initial body weight, 360 ± 37.3 kg) were housed in pens (27 pens with either 4 or 5 bulls per pen) and fed a high-concentrate diet for 96 d, containing 1) no addition of 3-NOP (control), 2) inclusion of 3-NOP at 100 mg/kg dry matter (DM), and 3) inclusion of 3-NOP at 150 mg/kg DM. No adverse effects of 3-NOP were observed on DM intake (DMI), animal performance, and gain:feed (P > 0.05). In addition, there was no effect (P > 0.05) of 3-NOP on carcass characteristics (subcutaneous fat thickness and rib eye area). In experiment 2, 24 bulls (initial BW, 366 ± 39.6 kg) housed in 12 pens (2 bulls/pen) from experiment 1 were used for CH4 measurements and nitrogen balance. Irrespective of the level, 3-NOP consistently decreased (P < 0.001) animals' CH4 emissions (g/d; ~49.3%), CH4 yield (CH4/DMI; ~40.7%) and CH4 intensity (CH4/average daily gain; ~38.6%). Moreover, 3-NOP significantly reduced the gross energy intake lost as CH4 by 42.5% (P < 0.001). The N retention: N intake ratio was not affected by 3-NOP (P = 0.19). We conclude that feeding 3-NOP is an effective strategy to reduce methane emissions, with no impairment on feedlot cattle performance.

Feeding incremental amounts of ground flaxseed: effects on diversity and relative abundance of ruminal microbiota and enteric methane emissions in lactating dairy cows

We evaluated the effects of incremental amounts of ground flaxseed (GFX) on diversity and relative abundance of ruminal microbiota taxa, enteric methane (CH₄) emissions, and urinary excretion of purine derivatives (PD) in lactating dairy cows in a replicated 4 × 4 Latin square design. Twenty mid-lactation Jersey cows were used in the study. Of these 20 cows, 12 were used for ruminal sampling, 16 for enteric CH₄ measurements, and all for spot urine collection. Each period lasted 21 d with 14 d for diet adaptation and 7 d for data and sample collection. Diets were formulated by replacing corn meal and soybean meal with 0%, 5%, 10%, and 15% of GFX in the diet's dry matter. Ruminal fluid samples obtained via stomach tubing were used for DNA extraction. Enteric CH₄ production was measured using the sulfur hexafluoride tracer technique. Diets had no effect on ruminal microbiota diversity. Similarly, the relative abundance of ruminal archaea genera was not affected by diets. In contrast, GFX decreased or increased linearly the relative abundance of Firmicutes (P < 0.01) and Bacteroidetes (P < 0.01), respectively. The relative abundance of the ruminal bacteria Ruminococcus (P < 0.01) and Clostridium (P < 0.01) decreased linearly, and that of Prevotella (P < 0.01) and Pseudobutyrivibrio (P < 0.01) increased linearly with feeding GFX. A tendency for a linear reduction (P = 0.055) in enteric CH₄ production (from 304 to 256 g/d) was observed in cows fed increasing amounts of GFX. However, neither CH₄ yield nor CH₄ intensity was affected by treatments. Diets had no effect on the urinary excretion of uric acid, allantoin, and total PD. Overall, feeding GFX decreased linearly the relative abundance of the ruminal bacterial genera Ruminococcus and Clostridium and enteric CH₄ production, but no change was seen for CH₄ yield and CH₄ intensity, or urinary excretion of total PD, suggesting no detrimental effect of GFX on microbial protein synthesis in the rumen.

Global Warming and Dairy Cattle: How to Control and Reduce Methane Emission

Agriculture produces greenhouse gases. Methane is a result of manure degradation and microbial fermentation in the rumen. Reduced CH4 emissions will slow climate change and reduce greenhouse gas concentrations. This review compiled studies to evaluate the best ways to decrease methane emissions. Longer rumination times reduce methane emissions and milk methane. Other studies have not found this. Increasing propionate and reducing acetate and butyrate in the rumen can reduce hydrogen equivalents that would otherwise be transferred to methanogenesis. Diet can reduce methane emissions. Grain lowers rumen pH, increases propionate production, and decreases CH4 yield. Methane generation per unit of energy-corrected milk yield reduces with a higher-energy diet. Bioactive bromoform discovered in the red seaweed Asparagopsis taxiformis reduces livestock intestinal methane output by inhibiting its production. Essential oils, tannins, saponins, and flavonoids are anti-methanogenic. While it is true that plant extracts can assist in reducing methane emissions, it is crucial to remember to source and produce plants in a sustainable manner. Minimal lipid supplementation can reduce methane output by 20%, increasing energy density and animal productivity. Selecting low- CH4 cows may lower GHG emissions. These findings can lead to additional research to completely understand the impacts of methanogenesis suppression on rumen fermentation and post-absorptive metabolism, which could improve animal productivity and efficiency.

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.

Effects of ruminal protozoa on methane emissions in ruminants-A meta-analysis

The effects of different ruminal protozoa (RP) on CH₄ emissions from ruminants were evaluated in a meta-analysis, using 64 publications reporting data from 79 in vivo experiments. Experiments included in the database reported CH₄ emissions (g/d) and total RP (TRP, log₁₀ cells/mL) from the same group of animals. The relationship between CH₄ emissions and RP (TRP, entodiniomorphids, and isotrichids), and TRP-, entodiniomorphid-, and isotrichid-based CH₄ emission prediction models, were evaluated as mixed models with experiment as a random effect and weighted by the reciprocal of the standard error of the mean and centered around one. Positive associations existed between TRP and isotrichids with CH₄ emissions but not between entodiniomorphids and CH₄ emissions. A reduction in CH₄ emissions was observed, averaging 7.96 and 4.25 g/d, per log unit reduction in TRP and isotrichid concentrations, respectively. Total RP and isotrichids were important variables in predicting CH₄ emissions from ruminants. Isotrichid CH₄ prediction model was more robust than the TRP, evidenciated by lower predicted sigma hat study (%), and error (%), and with higher concordance correlation coefficient. Both TRP and isotrichid models can accurately predict CH₄ emissions across different ruminant types, as shown by the low square root of the mean square prediction error, with 6.59 and 4.08% of the mean of root of the mean square prediction error in the TRP and isotrichid models, respectively. Our results confirm that isotrichids are more important than entodiniomorphids in methanogenesis. Distinguishing these 2 populations yielded a more robust CH₄ prediction model than combining them as total protozoa.

Prediction of nitrogen excretion from data on dairy cows fed a wide range of diets compiled in an intercontinental database: A meta-analysis

Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake.

Reduction of Enteric Methane Emissions in Heifers Fed Tropical Grass-Based Rations Supplemented with Palm Oil

Vegetable oils have been shown to reduce enteric methane (CH4) production by up to 20%. However, when the level of incorporation exceeds the threshold of 70 g/kg DM, dry matter intake (DMI) and nutrient digestibility may be reduced. The objective of this study was to determine the effects of the incorporation of three levels of palm oil (PO) on enteric CH4 emissions, rumen fermentation and apparent digestibility in heifers fed low-quality grass. Four rumen-cannulated heifers (Bos taurus × Bos indicus) were randomly assigned to four treatments: control (CON) and three increasing PO levels: 20, 40 and 60 g/kg in a 4 × 4 Latin square design with four periods of 22 days (14 days of adaptation to the ration), 5 days of feces and rumen fluid sampling (day 18, 4 h postprandial) and the last 3 days for measurements of CH4 in respiration chambers. With the exception of CP (p = 0.04), starch (p = 0.002) and EE (p < 0.001), the intake of nutrients was not affected by the inclusion of PO (p > 0.05). The apparent digestibility (AD) of nutrients was not affected by the inclusion of PO (p > 0.05), except for starch, which reduced its AD as the PO level was increased (p < 0.05). The gross energy intake was higher in PO-containing rations (p = 0.001), on the other hand, the digestible energy intake was similar between treatments (p > 0.05). In situ ruminal digestion kinetics and the potential degradability remained unchanged (p > 0.05), however, the effective degradability decreased with the inclusion of PO in the rations (p < 0.05). The ruminal pH and molar proportions of acetic, isovaleric and valeric acid were not different between treatments (p > 0.05). The ruminal concentration of propionic acid increased as the PO level increased, reaching its highest molar proportion with 60 g/kg PO (p < 0.05), however, the acetic/propionic ratio and the molar proportions of butyric acid and isobutyric acid decreased as the PO level increased (p < 0.05). The total daily CH4 production was lower in diets containing 20, 40 and 60 g/kg PO compared to the CON diet (p < 0.001). The production of CH4 per kg DMI and DOMI was greater (p < 0.05) for the CON diet compared to all three rations containing PO. The emission intensity, Ym, energy lost as CH4, emission factor (EF) and kg CO2 eq/year were reduced as an effect of the inclusion of PO (p < 0.05). Based on the results obtained, it is concluded that the incorporation of PO in cattle rations has the potential to reduce enteric methane emissions by 4% for every 10 g/kg PO in the ration, without affecting DMI, apparent digestibility or the consumption of digestible nutrient fractions.

Symposium review: Effective nutritional strategies to mitigate enteric methane in dairy cattle

Intensive research in the past decade has resulted in a better understanding of factors driving enteric methane (CH₄) emissions in ruminants. Meta-analyses of large databases, developed through the GLOBAL NETWORK project, have identified successful strategies for mitigation of CH₄ emissions. Methane inhibitors, alternative electron sinks, vegetable oils and oilseeds, and tanniferous forages are among the recommended strategies for mitigating CH₄ emissions from dairy and beef cattle and small ruminants. These strategies were also effective in decreasing CH₄ emissions yield and intensity. However, a higher inclusion rate of oils may negatively affect feed intake, rumen function, and animal performance, specifically milk components in dairy cows. In the case of nitrates (electron sinks), concerns with animal health may be impeding their adoption in practice, and potential emission trade-offs have to be considered. Tannins and tanniferous forages may have a negative effect on nutrient digestibility, and more research is needed to confirm their effects on overall animal performance in long-term experiments with high-producing animals. A meta-analysis of studies with dairy cows fed the CH₄ inhibitor 3-nitrooxypropanol (3-NOP) at the Pennsylvania State University showed (1) a consistent 28 to 32% decrease in daily CH₄ emissions or emissions yield and intensity; (2) no effect on dry matter intake, milk production, body weight, or body weight change, and a slight increase in milk fat concentration and yield (0.19 percentage units and 90 g/d, respectively); 3-NOP also appears to increase milk urea nitrogen concentration; (3) an exponential decrease in the mitigation effect of the inhibitor with increasing its dose (from 40 to 200 mg/kg of feed dry matter, corresponding to 3-NOP intake of 1 to 4.8 g/cow per day); and (4) a potential decrease in the efficacy of 3-NOP over time, which needs to be further investigated in long-term, full-lactation or multiple-lactation studies. The red macroalga Asparagopsis taxiformis has a strong CH₄ mitigation effect, but studies are needed to determine its feasibility, long-term efficacy, and effects on animal production and health. We concluded that widespread adoption of mitigation strategies with proven effectiveness by the livestock industries will depend on cost, government policies and incentives, and willingness of consumers to pay a higher price for animal products with decreased carbon footprint.

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.