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Automated measurements of the ratio of concentrations of methane and carbon dioxide, [CH 4 ]:[CO 2 ], in breath from individual animals (the so-called "sniffer technique") and estimated CO 2 production can be used to estimate CH 4 production, provided that CO 2 production can be reliably calculated. This would allow CH 4 production from individual cows to be estimated in large cohorts of cows, whereby ranking of cows according to their CH 4 production might become possible and their values could be used for breeding of low CH 4 -emitting animals. Estimates of CO 2 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 CO 2 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 CO 2 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 CO 2 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 CO 2 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 CO 2 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 CO 2 production from lactating dairy cows., (The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Addition of fats to the diets of ruminants has long been known to result in a reduction in enteric methane emissions. Tannins have also been used to reduce methane emissions but with mixed success. However, the effect of feeding fat in combination with tannin is unknown. Eight ruminally cannulated Holstein-Friesian cows were fed four diets in a double Latin-square, full crossover sequence. The treatments were 800 ml/day of water (CON), 800 g/day of cottonseed oil, 400 g/day of tannin, and 800 g/day of cottonseed oil and 400 g/day of tannin in combination (fat- and tannin-supplemented diet). Methane emissions were measured using open-circuit respiration chambers. Intake of basal diets was not different between treatments. Cows fed cottonseed oil had greater milk yield (34.9 kg/day) than those fed CON (32.3 kg/day), but the reduced concentration of milk fat meant there was no difference in energy-corrected milk between treatments. Methane yield was reduced when either cottonseed oil (14%) or tannin (11%) was added directly to the rumen, and their effect was additive when given in combination (20% reduction). The mechanism of the anti-methanogenic effect remains unclear but both fat and tannin appear to cause a reduction in fermentation in general rather than cause a change in the type of fermentation.

Wheat is the most common concentrate fed to grazing dairy cows in Australia, but no studies have examined the effects of wheat proportion in a pasture-based diet on milk production and methane emissions. In this 47-d experiment, 32 Holstein dairy cows were offered 1 of 4 diets during d 1 to 36. Cows in each of the dietary treatment groups were individually offered no wheat (W0) or wheat at 3 kg of dry matter (DM)/d (W3), 6 kg of DM/d (W6), or 9 kg of DM/d (W9). The remainder of the diet was 2.2 kg of DM of concentrate mix and freshly harvested perennial ryegrass (Lolium perenne) such that all individual cows were offered a total diet of approximately 20.2 kg of DM/d. From d 37 to 47 the diets of cows receiving treatments W0 and W3 remained unchanged, but cows in treatments W6 and W9 received the W3 diet. Individual cow feed intakes, milk yields, milk compositions, and methane emissions were measured for d 31 to 35 (period 1) and d 45 to 47 (period 2). During period 1, the mean intakes of cows offered the W0, W3, W6, and W9 diets were 19.2, 20.4, 20.2, and 19.8 kg of DM/d. Diet caused differences in energy-corrected milk, and means for W0, W3, W6, and W9 were 29.5, 32.4, 33.0, and 32.9 kg/d, respectively. Milk fat percentage differed with respective means of 3.93, 3.94, 3.69, and 3.17. Diets also caused differences in methane emissions, with means for W0, W3, W6, and W9 of 440, 431, 414, and 319 g/d. During period 1, the cows fed the W9 diet produced less methane and had lower methane yields (g/kg of DMI) and intensities (g/kg of energy-corrected milk) than cows fed the W3 diet. However, in period 2 when the wheat intake of cows in the W9 treatment was reduced to the same level as in the W3 treatment, their methane emissions, yields, and intensities were similar to those offered the W3 treatment, yet protozoa numbers in ruminal fluid were still much lower than those in cows offered the W3 treatment. Our research shows that for diets based on perennial ryegrass and crushed wheat, only the diet containing more than 30% crushed wheat resulted in substantially depressed milk fat concentration and reduced methane emissions, methane yield, and methane intensity. Thus, although feeding a diet with a high proportion of wheat can cause substantial methane mitigation, it can come at the cost of depression in milk fat concentration., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

There is a need to quantify methane (CH 4 ) emissions with alternative methods. For the past decade, milk fatty acids (MFA) could be used as proxies to predict CH 4 emissions from dairy cows because of potential common rumen biochemical pathways. However, equations have been developed based on a narrow range of diets and with limited data. The objectives of this study were to (1) construct a set of empirical models based on individual data of CH 4 emissions and MFA from a large number of lactating dairy cows fed a wide range of diets; (2) further increase the models' level of complexity (from farm to research level) with additional independent variables such as dietary chemical composition (organic matter, neutral detergent fiber, crude protein, starch, and ether extract), dairy performance (milk yield and composition), and animal characteristics (days in milk or body weight); and (3) evaluate the performance of the developed models on independent data sets including measurements from individual animals or average measurements of groups of animals. Prediction equations based only on MFA [C10:0, iso C17:0 + trans-9 C16:1,cis-11 C18:1, and trans-11,cis-15 C18:2 for CH 4 production (g/d); iso C16:0, cis-11 C18:1, trans-10 C18:1, and cis-9,cis-12 C18:2 for CH 4 yield (g/kg of dry matter intake, DMI); and iso C16:0, cis-15 C18:1, and trans-10 + trans-11 C18:1 for CH 4 intensity (g/kg of milk)] had a root mean squared error of 65.1 g/d, 2.8 g/kg of DMI, and 2.9 g/kg of milk, respectively, whereas complex equations that additionally used DMI, dietary neutral detergent fiber, ether extract, days in milk, and body weight had a lower root mean squared error of 46.6 g/d, 2.6 g/kg of DMI, and 2.7 g/kg of milk, respectively). External evaluation with individual or mean data not used for equation development led to variable results. When evaluations were performed using individual cow data from an external data set, accurate predictions of CH 4 production (g/d) were obtained using simple equations based on MFA. Better performance was observed on external evaluation with individual data for the simple equation of CH 4 production (g/d, based on MFA), whereas better performance was observed on external evaluation mean data for the simple equation of CH 4 yield (g/kg of DMI). The performance of evaluation of the models is dependent on the domain of validity of the evaluation data sets used (individual or mean)., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

The fortification of milk with phytosterols is an increasingly common practice to enhance the sterol profile and offer consumers potential health benefits. This study investigated whether cattle feed can influence the profile of phytosterols and cholesterol in the milk produced as an alternative to direct fortification of milk. Five experiments were performed using feeds commonly used by Australian dairy farmers and selected formulated rumen-protected feeds. Statistical significances were observed for some individual plant sterols and cholesterol in milk under these differing feeding regimens compared with the respective controls. In the case of the phytosterols, where the daily recommended consumption is typically 2 g per day, the total phytosterols were <0.12 mg/100 mL of milk. An experiment using a rumen-protected feed with high phytosterol levels suggested a decreased transfer of cholesterol to the milk by as much as 20%, although further work is required to confirm these preliminary results. Overall, the study suggests that different feeding practices have minimal effect on the resulting sterol profile of the milk., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Ruminant production systems are important contributors to anthropogenic methane (CH 4 ) emissions, but there are large uncertainties in national and global livestock CH 4 inventories. Sources of uncertainty in enteric CH 4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH 4 emission factors. There is also significant uncertainty associated with enteric CH 4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF 6 ) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH 4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH 4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH 4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH 4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH 4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH 4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).)

The objective of this study was to compare 2 commonly used techniques for measuring methane emissions from ruminant animals: the GreenFeed (GF) system and the sulfur hexafluoride (SF6) technique. The study was part of a larger experiment in which a methane inhibitor, 3-nitrooxypropanol, fed at 4 application rates (0, 40, 60, and 80 mg/kg of feed dry matter) decreased enteric methane emission by an average of 30% (measured by both GF and SF6) in a 12-wk experiment with 48 lactating Holstein cows fed a total mixed ration. The larger experiment used a randomized block design and was conducted in 2 phases (February to May, phase 1, and June to August, phase 2), with 2 sets of 24 cows in each phase. Using both GF and SF6 techniques, methane emission data were collected simultaneously during experimental wk 2, 6, and 12 (phase 1) and 2, 9, and 12 (phase 2), which corresponded to a total of 6 sampling periods. During each sampling period, 8 spot samples of gas emissions (staggered over a 3-d period) were collected from each cow using GF, as well as 3×24-h collections using the SF6 technique. Methane emission data were averaged per cow for the statistical analysis. The mean methane emission was 373 (standard deviation=96.3) and 405 (standard deviation=156) g/cow per day for GF and SF6, respectively. Coefficients of variation for the 2 methods were 25.8 and 38.6%, respectively; correlation and concordance between the 2 methods were 0.40 and 0.34, respectively. The difference in methane emission between the 2 methods (SF6 - GF) within treatment was from 46 to 144 and 24 to 27 g/d for phases 1 and 2, respectively. In the conditions of this experiment, the SF6 technique produced larger variability in methane emissions than the GF method. The overall difference between the 2 methods was on average about 8%, but was not consistent over time, likely influenced by barn ventilation and background methane and SF6 concentrations., (Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Grape marc (the skins, seeds, stalk, and stems remaining after grapes have been pressed to make wine) is currently a by-product used as a feed supplement by the dairy and beef industries. Grape marc contains condensed tannins and has high concentrations of crude fat; both these substances can reduce enteric methane (CH4) production when fed to ruminants. This experiment examined the effects of dietary supplementation with either dried, pelleted grape marc or ensiled grape marc on yield and composition of milk, enteric CH4 emissions, and ruminal microbiota in dairy cows. Thirty-two Holstein dairy cows in late lactation were offered 1 of 3 diets: a control (CON) diet; a diet containing dried, pelleted grape marc (DGM); and a diet containing ensiled grape marc (EGM). The diet offered to cows in the CON group contained 14.0kg of alfalfa hay dry matter (DM)/d and 4.3kg of concentrate mix DM/d. Diets offered to cows in the DGM and EGM groups contained 9.0kg of alfalfa hay DM/d, 4.3kg of concentrate mix DM/d, and 5.0kg of dried or ensiled grape marc DM/d, respectively. These diets were offered individually to cows for 18d. Individual cow feed intake and milk yield were measured daily and milk composition measured on 4d/wk. Individual cow CH4 emissions were measured by the SF6 tracer technique on 2d at the end of the experiment. Ruminal bacterial, archaeal, fungal, and protozoan communities were quantified on the last day of the experiment. Cows offered the CON, DGM, and EGM diets, ate 95, 98, and 96%, respectively, of the DM offered. The mean milk yield of cows fed the EGM diet was 12.8kg/cow per day and was less than that of cows fed either the CON diet (14.6kg/cow per day) or the DGM diet (15.4kg/cow per day). Feeding DGM and EGM diets was associated with decreased milk fat yields, lower concentrations of saturated fatty acids, and enhanced concentrations of mono- and polyunsaturated fatty acids, in particular cis-9,trans-11 linoleic acid. The mean CH4 emissions were 470, 375, and 389g of CH4/cow per day for cows fed the CON, DGM, and EGM diets, respectively. Methane yields were 26.1, 20.2, and 21.5g of CH4/kg of DMI for cows fed the CON, DGM, and EGM diets, respectively. The ruminal bacterial and archaeal communities were altered by dietary supplementation with grape marc, but ruminal fungal and protozoan communities were not. Decreases of approximately 20% in CH4 emissions and CH4 yield indicate that feeding DGM and EGM could play a role in CH4 abatement., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Variation in the composition of microorganisms in the rumen (the rumen microbiome) of dairy cattle (Bos taurus) is of great interest because of possible links to methane emission levels. Feed additives are one method being investigated to reduce enteric methane production by dairy cattle. Here we report the effect of 2 methane-mitigating feed additives (grapemarc and a combination of lipids and tannin) on rumen microbiome profiles of Holstein dairy cattle. We used untargeted (shotgun) massively parallel sequencing of microbes present in rumen fluid to generate quantitative rumen microbiome profiles. We observed large effects of the feed additives on the rumen microbiome profiles using multiple approaches, including linear mixed modeling, hierarchical clustering, and metagenomic predictions. The effect on the fecal microbiome profiles was not detectable using hierarchical clustering, but was significant in the linear mixed model and when metagenomic predictions were used, suggesting a more subtle effect of the diets on the lower gastrointestinal microbiome. A differential representation analysis (analogous to differential expression in RNA sequencing) showed significant overlap in the contigs (which are genome fragments representing different microorganism species) that were differentially represented between experiments. These similarities suggest that, despite the different additives used, the 2 diets assessed in this investigation altered the microbiomes of the samples in similar ways. Contigs that were differentially represented in both experiments were tested for associations with methane production in an independent set of animals. These animals were not treated with a methane-mitigating diet, but did show substantial natural variation in methane emission levels. The contigs that were significantly differentially represented in response to both dietary additives showed a significant enrichment for associations with methane production. This suggests that these methane-mitigating diets have altered the rumen microbiome toward naturally low methane-emitting microbial profiles. The contig sequences are predominantly new and include Faecalibacterium spp. The contigs we have identified here are potential biomarkers for low-methane-emitting cattle., (Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

An experiment was conducted to quantify the changes in energy partitioning resulting from grain supplementation in herbage-fed dairy cows at 4 stages during a 670-d lactation. The experiment used 16 lactating Holstein-Friesian cows, with a control and a grain treatment being randomly allocated to 8 cows each. During 4 measurement periods (each of 4d in a metabolism stall and 3d in an indirect calorimeter) beginning at approximately 110, 270, 450, and 560 d in milk (DIM), the energy balance of each cow was measured. Cows in both groups were individually offered freshly cut ryegrass pasture (Lolium hybridum L.) in periods 1 and 3 and ryegrass pasture silage and alfalfa (Medicago sativa L.) hay in periods 2 and 4. In all periods, cows in the grain group were offered an additional 4.4 to 5.0 kg of dry matter of cereal grain/cow per day. Adding grain to the diet increased yields of fat and protein and tended to increase yields of milk and lactose, but did not affect milk composition. Gross energy intake (GEI) declined as lactation progressed. Adding grain to the diet decreased the percentage of GEI in feces and urine, but the extent of these reductions did not change as lactation progressed. Adding grain to the diet similarly reduced the percentage of GEI lost to heat, but again the extent of the reduction remained similar as lactation progressed. The magnitude of the increase in milk energy resulting from grain supplementation did not change with advancing lactation, but tissue energy retention was greater in the first 300 DIM compared with after 300 DIM. For herbage-based diets, CH(4) emissions ranged from 6.2 to 7.6% of GEI, which corresponds to 24.0 to 25.8 g of CH(4)/kg of dry matter intake. For diets supplemented with cereal grains, CH(4) emissions ranged from 6.3 to 7.3% of GEI, which corresponds to 21.6 to 25.2 g of CH(4)/kg of dry matter intake. It was concluded that, for cows producing <24 kg of milk/d and consuming herbage-based diets supplemented with grain, the efficiency of utilizing the additional energy in the grain, as measured by the loss of energy in heat, and its partitioning to milk, did not change as lactation progressed from 110 to 560 DIM., (Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Previously observed relationships between dietary composition and production of a small number of individual milk fatty acids were the motivation to examine whether equations could be developed to predict production of all the major individual milk fatty acids. Such equations could be incorporated into ration formulation programs and used to examine factors that influence milk fat composition. Data from 29 published experiments on Holstein cows that provided 120 dietary treatments were entered into CPM-Dairy to obtain estimates of amounts of individual long-chain fatty acids (LCFA) absorbed from the intestines. These derived data and other dietary and animal data including the reported fatty acid composition of milk fat were entered into a spreadsheet. Descriptors of diet included daily intake of dry matter, total fermentable carbohydrate, total fatty acids, and profile of dietary fatty acids, intake of neutral detergent fiber, supplemental fish-oil, buffer, and magnesium oxide. Cow data included body weight and days in milk (DIM). Multiple linear regression was used to develop equations to predict the production (g/d) of each of 26 major LCFA. The equations developed generally had R(2) values in excess of 0.5. Production (g/d) of total de novo fatty acids (C4:0 to C15:0) (PTdenovo) was found to be positively related to the intake of fermentable carbohydrate, and negatively related to the intake of fish oil fatty acids and the estimated total amount of unsaturated fatty acids absorbed from the intestines. The PTdenovo was greater in pasture-fed cows than total mixed ration-fed cows and was negatively related to the square root of DIM. Production of each individual de novo fatty acid was described by a fixed proportion of PTdenovo. These proportions were 0.12 +/- 0.006 (C4:0), 0.083 +/- 0.0039 (C6:0), 0.0516 +/- 0.0025 (C8:0), 0.111 +/- 0.003 (C10:0), 0.134 +/- 0.0037 (C12:0), 0.441 +/- 0.007 (C14:0), 0.046 +/- 0.0024 (C14:1), and 0.0432 +/- 0.0017 (C15:0). Separate independent equations were developed to describe the daily production of C16:0, C16:1, and the main individual preformed fatty acids (>C16). The productions of each of the main individual pre-formed fatty acids were generally strongly related to the corresponding estimated amount (g/d) of specific fatty acids absorbed from the intestines. Percentage estimates for the direct transfer of the major absorbed LCFA to their corresponding LCFA in milk were 42% (C16:0); 9.5% (C18:0); 47.5% (cis-9 C18:1); 16.1% (all isomers of trans-C18:1), 38% (cis-9, cis-12 C18:2); and 31% (cis-9, cis-12, cis15 C18:3). High dietary intake of fish oil fatty acids was negatively associated with the production of all of the major individual preformed fatty acids with the exception of C20:5 and C22:6. In some instances, particular dietary factors were found to have positive influences on production of one fatty acid and negative influences on another. For example, high levels of dietary magnesium oxide were positively associated with production of C17 fatty acids but negatively associated with production of C18:0 and cis-9, trans-11 C18:2 (conjugated linoleic acid). This analysis quantified effects of major dietary and cow factors on production of individual fatty acids in milk.