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

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

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

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

Effects of replacement of barley with oats on milk fatty acid composition in dairy cows fed grass silage-based diets

This study consists of milk fatty acid (FA) data collected during 2 in vivo experiments. For this study, 8 cows from each experiment were included in a replicated 4 × 4 Latin square design. At the start of experiment 1 (Exp1) cows were at (mean ± standard deviation) 87 ± 34.6 d in milk, 625 ± 85.0 kg of body weight, and 32.1 ± 4.17 kg/d milk yield and at the start of experiment 2 (Exp2) cows were at 74 ± 18.2 d in milk, 629 ± 87.0 kg of body weight, and 37.0 ± 3.2 kg/d milk yield. In Exp1, we examined the effects of gradual replacement of barley with hulled oats (oats with hulls) on milk FA composition. The basal diet was grass silage and rapeseed meal (58 and 10% of diet DM, respectively), and the 4 grain supplements were formulated so that barley was gradually replaced by hulled oats at levels of 0, 33, 67, and 100% on dry matter basis. In Exp2, we examined (1) the effects of replacing barley with both hulled and dehulled oats (oats without hulls) and (2) the effects of gradual replacement of hulled oats with dehulled oats on milk FA composition. The basal diet was grass silage and rapeseed meal (60 and 10% of diet DM, respectively), and the 4 pelleted experimental concentrates were barley, hulled oats, a 50:50 mixture of hulled and dehulled oats, and dehulled oats on dry matter basis. In Exp1, gradual replacement of barley with hulled oats decreased relative proportions of 14:0, 16:0, and total saturated FA (SFA) in milk fat linearly, whereas proportions of 18:0, 18:1, total monounsaturated FA, and total cis unsaturated FA increased linearly. Transfer efficiency of total C18 decreased linearly when barley was replaced by hulled oats in Exp1. In Exp2, relative proportions of 14:0, 16:0, and total SFA were lower, whereas proportions of 18:0, 18:1, monounsaturated FA, and cis unsaturated FA were higher in milk from cows fed the oat diets than in milk from cows fed the barley diet. Moreover, in Exp2, gradual replacement of hulled oats with dehulled oats slightly decreased the relative proportion of 14:0 in milk fat but did not affect the proportions of 16:0, 18:0, 18:1, total SFA, monounsaturated FA, trans FA, or polyunsaturated FA. In Exp2, transfer efficiency of total C18 was lower when cows were fed the oat diets than when fed the barley diet and decreased linearly when hulled oats were replaced with dehulled oats. Predictions of daily CH₄ emissions (g/d) using the on-farm available variables energy-corrected milk yield and body weight were not markedly improved by including milk concentrations of individual milk FA in prediction equations. In conclusion, replacement of barley with oats as a concentrate supplement for dairy cows fed a grass silage-based diet could offer a practical strategy to change the FA composition of milk to be more in accordance with international dietary guidelines regarding consumption of SFA.

Major Causes of Variation of External Appearance, Chemical Composition, Texture, and Color Traits of 37 Categories of Cheeses

Cheeses are produced by many different procedures, giving rise to many types differing in ripening time, size, shape, chemical composition, color, texture, and sensory properties. As the first step in a large project, our aim was to characterize and quantify the major sources of variation in cheese characteristics by sampling 1050 different cheeses manufactured by over 100 producers and grouped into 37 categories (16 with protected designation of origin, 4 traditional cheese categories, 3 pasta filata cheese categories, 5 flavored cheese categories, 2 goat milk categories, and 7 other categories ranging from very fresh to very hard cheeses). We obtained 17 traits from each cheese (shape, height, diameter, weight, moisture, fat, protein, water soluble nitrogen, ash, pH, 5 color traits, firmness, and adhesiveness). The main groups of cheese categories were characterized and are discussed in terms of the effects of the prevalent area of origin/feeding system, species of lactating females, main cheese-making technologies, and additives used. The results will allow us to proceed with the further steps, which will address the interrelationships among the different traits characterizing cheeses, detailed analyses of the nutrients affecting human health and sensorial fingerprinting.

Selecting for Feed Efficient Cows Will Help to Reduce Methane Gas Emissions

In the last decade, several countries have included feed efficiency (as residual feed intake; RFI) in their breeding goal. Recent studies showed that RFI is favorably correlated with methane emissions. Thus, selecting for lower emitting animals indirectly through RFI could be a short-term strategy in order to achieve the intended reduction set by the EU Commission (-55% for 2030). The objectives were to 1) estimate genetic parameters for six methane traits, including genetic correlations between methane traits, production, and feed efficiency traits, 2) evaluate the expected correlated response of methane traits when selecting for feed efficiency with or without including methane, 3) quantify the impact of reducing methane emissions in dairy cattle using the Danish Holstein population as an example. A total of 26,664 CH4 breath records from 647 Danish Holstein cows measured over 7 years in a research farm were analyzed. Records on dry matter intake (DMI), body weight (BW), and energy corrected milk (ECM) were also available. Methane traits were methane concentration (MeC, ppm), methane production (MeP; g/d), methane yield (MeY; g CH4/kg DMI), methane intensity (MeI; g CH4/kg ECM), residual methane concentration (RMeC), residual methane production (RMeP, g/d), and two definitions of residual feed intake with or without including body weight change (RFI1, RFI2). The estimated heritability of MeC was 0.20 ± 0.05 and for MeP, it was 0.21 ± 0.05, whereas heritability estimates for MeY and MeI were 0.22 ± 0.05 and 0.18 ± 0.04, and for the RMeC and RMeP, they were 0.23 ± 0.06 and 0.16 ± 0.02, respectively. Genetic correlations between methane traits ranged from moderate to highly correlated (0.48 ± 0.16–0.98 ± 0.01). Genetic correlations between methane traits and feed efficiency were all positive, ranging from 0.05 ± 0.20 (MeI-RFI2) to 0.76 ± 0.09 (MeP-RFI2). Selection index calculations showed that selecting for feed efficiency has a positive impact on reducing methane emissions’ expected response, independently of the trait used (MeP, RMeP, or MeI). Nevertheless, adding a negative economic value for methane would accelerate the response and help to reach the reduction goal in fewer generations. Therefore, including methane in the breeding goal seems to be a faster way to achieve the desired methane emission reductions in dairy cattle.

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

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

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

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

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

Changes in nutrient balance, methane emissions, physiologic biomarkers, and production performance in goats fed different forage-to-concentrate ratios during lactation

The objective was to determine the effect forage-to-concentrate (F:C) ratio and stage of lactation on methane emissions, digestibility, nutrient balance, lactation performance, and metabolic responses in lactating goats. Twenty Murciano-Granadina dairy goats were used in an experiment divided into 3 periods: early (30 d), mid (100 d), and late (170 d) lactation. All goats were fed a diet with 35:65 F:C (FCL) during early-lactation. Then, 1 group (n = 10 goats) remained on FCL through mid- and late-lactation while the other group (n = 10 goats) was fed a diet with 50:50 F:C at mid-lactation (FCM) and 65:35 (FCH) at late lactation. A greater proportion of concentrate in the diet was associated with greater overall intake and digestibility (P < 0.05). Energy balance was negative in early-lactation (-77 kJ/kg of BW0.75, on average) and positive for FCL at mid- and late-lactation (13 and 35 kJ/kg of BW0.75, respectively). Goats fed FCM and FCH maintained negative energy balance throughout lactation. Plasma concentrations of non-esterified fatty acids at mid-lactation were greater for FCM than FCL (680 mEq/L), and at late-lactation concentrations were greater for FCH and FCL (856 mEq/L). A similar response was detected for plasma β-hydroxybutyrate. Methane emission was greater (P < 0.05) for FCM than FCH (1.7 g CH4/d). This study demonstrated that differences in F:C across stages of lactation lead to distinct metabolic responses at the level of the rumen and tissues.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

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

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

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

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

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

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

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

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

Enteric Methane Emissions of Dairy Cows Predicted from Fatty Acid Profiles of Milk, Cream, Cheese, Ricotta, Whey, and Scotta

Enteric methane emissions (EME) of ruminants contribute to global climate change, but any attempt to reduce it will need an easy, inexpensive, and accurate method of quantification. We used a promising indirect method for estimating EMEs of lactating dairy cows based on the analysis of the fatty acid (FA) profile of their milk. The aim of this preliminary study was to assess milk from four single samplings (morning whole, evening whole, evening partially skimmed, and vat milks) as alternatives to reference whole milk samples from two milkings. Three fresh products (cream, cheese, and ricotta), two by-products (whey and scotta), and two long-ripened cheeses (6 and 12 months) were also assessed as alternative sources of information to reference milk. The 11 alternative matrices were obtained from seven experimental cheese- and ricotta-making sessions carried out every two weeks following the artisanal Malga cheese-making procedure using milk from 148 dairy cows kept on summer highland pastures. A total of 131 samples of milk, dairy products, and by-products were analyzed to determine the milk composition and to obtain detailed FA profiles using bi-dimensional gas-chromatography. Two equations taken from a published meta-analysis of methane emissions measured in the respiration chambers of cows on 30 different diets were applied to the proportions of butyric, iso-palmitic, iso-oleic, vaccenic, oleic, and linoleic acids out of total FAs to predict methane yield per kg of dry matter ingested and methane intensity per kg of fat and protein corrected milk produced by the cows. Methane yield and intensity could be predicted from single milk samples with good accuracy (trueness and precision) with respect to those predicted from reference milk. The fresh products (cream, cheese and ricotta) generally showed good levels of trueness but low precision for predicting both EME traits, which means that a greater number of samples needs to be analyzed. Among by-products, whey could be a viable alternative source of information for predicting both EME traits, whereas scotta overestimated both traits and showed low precision (due also to its very low fat content). Long-ripened cheeses were found to be less valuable sources of information, although six-month cheese could, with specific correction factors, be acceptable sources of information for predicting the methane yield of lactating cows. These preliminary results need to be confirmed by further study on different dairy systems and cheese-making technologies but offer new insight into a possible easy method for monitoring the EME at the field level along the dairy chain.

Methane prediction based on individual or groups of milk fatty acids for dairy cows fed rations with or without linseed

Milk fatty acids (MFA) are a proxy for the prediction of CH₄ emission from cows, and prediction differs with diet. Our objectives were (1) to compare the effect of diets on the relation between MFA profile and measured CH₄ production, (2) to predict CH₄ production based on 6 data sets differing in the number and type of MFA, and (3) to test whether additional inclusion of energy-corrected milk (ECM) yield or dry matter intake (DMI) as explanatory variables improves predictions. Twenty dairy cows were used. Four diets were used based on corn silage (CS) or grass silage (GS) without (L0) or with linseed (LS) supplementation. Ten cows were fed CS-L0 and CS-LS and the other 10 cows were fed GS-L0 and GS-LS in random order. In feeding wk 5 of each diet, CH₄ production (L/d) was measured in respiration chambers for 48 h and milk was analyzed for MFA concentrations by gas chromatography. Specific CH₄ prediction equations were obtained for L0-, LS-, GS-, and CS-based diets and for all 4 diets collectively and validated by an internal cross-validation. Models were developed containing either 43 identified MFA or a reduced set of 7 groups of biochemically related MFA plus C16:0 and C18:0. The CS and LS diets reduced CH₄ production compared with GS and L0 diets, respectively. Methane yield (L/kg of DMI) reduction by LS was higher with CS than GS diets. The concentrations of C18:1 trans and n-3 MFA differed among GS and CS diets. The LS diets resulted in a higher proportion of unsaturated MFA at the expense of saturated MFA. When using the data set of 43 individual MFA to predict CH₄ production (L/d), the cross-validation coefficient of determination (R²_CV) ranged from 0.47 to 0.92. When using groups of MFA variables, the R²_CV ranged from 0.31 to 0.84. The fit parameters of the latter models were improved by inclusion of ECM or DMI, but not when added to the data set of 43 MFA for all diets pooled. Models based on GS diets always had a lower prediction potential (R²_CV = 0.31 to 0.71) compared with data from CS diets (R²_CV = 0.56 to 0.92). Models based on LS diets produced lower prediction with data sets with reduced MFA variables (R²_CV = 0.62 to 0.68) compared with L0 diets (R²_CV = 0.67 to 0.80). The MFA C18:1 cis-9 and C24:0 and the monounsaturated FA occurred most often in models. In conclusion, models with a reduced number of MFA variables and ECM or DMI are suitable for CH₄ prediction, and CH₄ prediction equations based on diets containing linseed resulted in lower prediction accuracy.

The fatty acid composition of Estonian and Latvian retail milk; implications for human nutrition compared with a designer milk

The study reported in this Research Communication compared retail milks' FA profiles from two neighbouring countries, estimated the potential contributions of these milks and a designer milk (achieved by changing the diet of the dairy cow) to the recommended human dietary intake of FA, and predicted (based on the milk FA profile) methane emission from dairy cows. Retail milks in Estonia and Latvia were purchased from supermarkets monthly for one year. To compare the FA composition of retail milk with designer milk with an increased PUFA content, the bulk milk FA profile from a separate field trial was used. Milk FA concentrations of the two neighbouring countries were affected by state, season and their interaction, while the main influence on all these factors were different feeding practices (grazing availability, forage to concentrate ratio and legume-rich silages vs. maize silages). Three cups (600 mL; fat content 2·5 g/100 g) of Estonian, or Latvian retail milk or designer milk per day contributed more to the recommended intakes of saturated FA (SFA) (42·5, 42·7, 38·7%, respectively) than other FA. Compared to the retail milks, α-linolenic acid estimated intake was almost doubled by designer milk consumption (19·7% of adequate intake) without influencing summed intakes of SFA and trans FA. There were state and seasonal differences in the predicted methane outputs of dairy cattle based on retail milk FA. Although the FA profiles of retail milks in the two neighbouring countries were affected by state and season, an appreciable increase in human dietary intakes of beneficial fatty acids from milk, and concomitant reduction in methane emissions from dairy cows, can be achieved only by targeted feeding.

Short communication: The effect of linseed oil and DGAT1 K232A polymorphism on the methane emission prediction potential of milk fatty acids

Several in vivo CH₄ measurement techniques have been developed but are not suitable for precise and accurate large-scale measurements; hence, proxies for CH₄ emissions in dairy cattle have been proposed, including the milk fatty acid (MFA) profile. The aim of the present study was to determine whether recently developed MFA-based prediction equations for CH₄ emission are applicable to dairy cows with different diacylglycerol o-acyltransferase 1 (DGAT1) K232A polymorphism and fed diets with and without linseed oil. Data from a crossover design experiment were used, encompassing 2 dietary treatments (i.e., a control diet and a linseed oil diet, with a difference in dietary fat content of 22 g/kg of dry matter) and 24 lactating Holstein-Friesian cows (i.e., 12 cows with DGAT1 KK genotype and 12 cows with DGAT1 AA genotype). Enteric CH₄ production was measured in climate respiration chambers and the MFA profile was analyzed using gas chromatography. Observed CH₄ emissions were compared with CH₄ emissions predicted by previously developed MFA-based CH₄ prediction equations. The results indicate that different types of diets (i.e., with or without linseed oil), but not the DGAT1 K232A polymorphism, affect the ability of previously derived prediction equations to predict CH₄ emission. However, the concordance correlation coefficient was smaller than or equal to 0.30 for both dietary treatments separately, both DGAT1 genotypes separately, and the complete data set. We therefore concluded that previously derived MFA-based CH₄ prediction equations can neither accurately nor precisely predict CH₄ emissions of dairy cows managed under strategies differing from those under which the original prediction equations were developed.

The relationship between milk metabolome and methane emission of Holstein Friesian dairy cows: Metabolic interpretation and prediction potential

This study aimed to quantify the relationship between CH₄ emission and fatty acids, volatile metabolites, and nonvolatile metabolites in milk of dairy cows fed forage-based diets. Data from 6 studies were used, including 27 dietary treatments and 123 individual observations from lactating Holstein-Friesian cows. These dietary treatments covered a large range of forage-based diets, with different qualities and proportions of grass silage and corn silage. Methane emission was measured in climate respiration chambers and expressed as production (g per day), yield (g per kg of dry matter intake; DMI), and intensity (g per kg of fat- and protein-corrected milk; FPCM). Milk samples were analyzed for fatty acids by gas chromatography, for volatile metabolites by gas chromatography-mass spectrometry, and for nonvolatile metabolites by nuclear magnetic resonance. Dry matter intake was 15.9 ± 1.90 kg/d (mean ± SD), FPCM yield was 25.2 ± 4.57 kg/d, CH₄ production was 359 ± 51.1 g/d, CH₄ yield was 22.6 ± 2.31 g/kg of DMI, and CH₄ intensity was 14.5 ± 2.59 g/kg of FPCM. The results show that changes in individual milk metabolite concentrations can be related to the ruminal CH₄ production pathways. Several of these relationships were diet driven, whereas some were partly dependent on FPCM yield. Next, prediction models were developed and subsequently evaluated based on root mean square error of prediction (RMSEP), concordance correlation coefficient (CCC) analysis, and random 10-fold cross-validation. The best models with milk fatty acids (in g/100 g of fatty acids; MFA) alone predicted CH₄ production, yield, and intensity with a RMSEP of 34 g/d, 2.0 g/kg of DMI, and 1.7 g/kg of FPCM, and with a CCC of 0.67, 0.44, and 0.75, respectively. The CH₄ prediction potential of both volatile metabolites alone and nonvolatile metabolites alone was low, regardless of the unit of CH₄ emission, as evidenced by the low CCC values (<0.35). The best models combining the 3 types of metabolites as selection variables resulted in the inclusion of only MFA for CH₄ production and CH₄ yield. For CH₄ intensity, MFA, volatile metabolites, and nonvolatile metabolites were included in the prediction model. This resulted in a small improvement in prediction potential (CCC of 0.80; RMSEP of 1.5 g/kg of FPCM) relative to MFA alone. These results indicate that volatile and nonvolatile metabolites in milk contain some information to increase our understanding of enteric CH₄ production of dairy cows, but that it is not worthwhile to determine the volatile and nonvolatile metabolites in milk to estimate CH₄ emission of dairy cows. We conclude that MFA have moderate potential to predict CH₄ emission of dairy cattle fed forage-based diets, and that the models can aid in the effort to understand and mitigate CH₄ emissions of dairy cows.

Dairy system, parity, and lactation stage affect enteric methane production, yield, and intensity per kilogram of milk and cheese predicted from gas chromatography fatty acids

Ruminants (and milk production) contribute to global climate change through enteric methane emissions (EME), and any attempt to reduce them is complicated by the fact that they are difficult and expensive to measure directly. In the case of dairy cows, a promising indirect method of estimating EME is to use the milk fatty acid profile as a proxy, as a relationship exists between microbial activity in the rumen and the molecules available for milk synthesis in the mammary gland. In the present study, we analyzed the detailed fatty acid profiles (through gas chromatography) of a large number of milk samples from 1,158 Brown Swiss cows reared on 85 farms with the aim of testing in the field 2 equations for estimating EME taken from a published meta-analysis. The average estimated methane yield (CH₄ emission per kg of dry matter intake, 21.34 ± 1.60 g/kg) and methane intensity (per kg of corrected milk, 14.17 ± 1.78 g/kg), and the derived methane production (CH₄ emissions per day per cow, 357 ± 109 g/d) were similar to those previously published. Using data from model cheese makings from individual cows, we also calculated estimated methane intensity per kilogram of fresh cheese (99.7 ± 16.4 g/kg) and cheese solids (207.5 ± 30.9 g/kg). Dairy system affected all EME estimates. Traditional dairy farms, and modern farms including corn silage in the TMR exhibited greater estimated methane intensities. We found very wide variability in estimated EME traits among different farms within dairy system (0.33 to 0.61 of total variance), suggesting the need to modify the farms' feeding regimens and management practices to mitigate emissions. Among the individual factors, parity order affected all estimated EME traits excepted methane yield, with an increase from first lactation to the following ones. Lactation stage exhibited more favorable estimated EME traits during early lactation, concomitant with the availability of nutrients from body tissue mobilization for mammary synthesis of milk. Our results showed a coherence between the EME traits estimated from the analysis of milk fatty acids and the expectations according to current knowledge. Further research is needed to validate the results obtained in this study in other breeds and populations, to assess the magnitude of the genetic variation and the potential of these phenotypes to be exploited in breeding programs with the aim to mitigate emissions.

Application of meta-omics techniques to understand greenhouse gas emissions originating from ruminal metabolism

Methane emissions from ruminal fermentation contribute significantly to total anthropological greenhouse gas (GHG) emissions. New meta-omics technologies are beginning to revolutionise our understanding of the rumen microbial community structure, metabolic potential and metabolic activity. Here we explore these developments in relation to GHG emissions. Microbial rumen community analyses based on small subunit ribosomal RNA sequence analysis are not yet predictive of methane emissions from individual animals or treatments. Few metagenomics studies have been directly related to GHG emissions. In these studies, the main genes that differed in abundance between high and low methane emitters included archaeal genes involved in methanogenesis, with others that were not apparently related to methane metabolism. Unlike the taxonomic analysis up to now, the gene sets from metagenomes may have predictive value. Furthermore, metagenomic analysis predicts metabolic function better than only a taxonomic description, because different taxa share genes with the same function. Metatranscriptomics, the study of mRNA transcript abundance, should help to understand the dynamic of microbial activity rather than the gene abundance; to date, only one study has related the expression levels of methanogenic genes to methane emissions, where gene abundance failed to do so. Metaproteomics describes the proteins present in the ecosystem, and is therefore arguably a better indication of microbial metabolism. Both two-dimensional polyacrylamide gel electrophoresis and shotgun peptide sequencing methods have been used for ruminal analysis. In our unpublished studies, both methods showed an abundance of archaeal methanogenic enzymes, but neither was able to discriminate high and low emitters. Metabolomics can take several forms that appear to have predictive value for methane emissions; ruminal metabolites, milk fatty acid profiles, faecal long-chain alcohols and urinary metabolites have all shown promising results. Rumen microbial amino acid metabolism lies at the root of excessive nitrogen emissions from ruminants, yet only indirect inferences for nitrogen emissions can be drawn from meta-omics studies published so far. Annotation of meta-omics data depends on databases that are generally weak in rumen microbial entries. The Hungate 1000 project and Global Rumen Census initiatives are therefore essential to improve the interpretation of sequence/metabolic information.

Can concentrations of trans octadecenoic acids in milk fat be used to predict methane yields of dairy cows?

There is a need to develop simple, accurate methods for predicting methane emissions, yields and intensities of dairy cows. Several studies have focussed on the relationship between the concentrations of trans-10 plus trans-11 C18:1 fatty acids in milk fat and methane yield. The aim of the present study was to perform a meta-analysis to quantify relationships between the concentrations of various trans isomers of C18:1 in milk fat and methane emissions (g/day), methane yield (g/kg dry-matter intake) and methane intensity (g/kg energy-corrected milk yield). Data were from seven experiments encompassing 23 different diets and 220 observations of milk fatty acid concentrations and methane emissions. Univariate linear mixed-effects regression models were fitted to the data with the linear term as a fixed effect and with experiment and observation within experiment as random effects. Concentrations of trans-9, trans-10, trans-11 and trans-10 plus trans-11 isomers of C18:1 were poorly related to methane emissions, yields and intensities, with the best relationships being between trans-10 C18:1 and methane emissions (R2 = 0.356), trans-10 C18:1 and methane yield (R2 = 0.265) and trans-10 plus trans-11 C18:1 and methane intensity (R2 = 0.124). The data indicated that the relationships between trans-10 C18:1 and methane metrics were not linear, but were biphasic and better described by an exponential model. However, even exponential models poorly fitted the data. It is concluded that the concentrations of trans isomers of C18:1 have limited potential to accurately predict methane emissions, yields or intensities of dairy cows.

Predictions of methane emission levels and categories based on milk fatty acid profiles from dairy cows

Milk fatty acid (MFA) have already been used to model methane (CH4) emissions from dairy cows. However, the data sets used to develop these models covered limited variation in dietary conditions, reducing the robustness of the predictions. In this study, a data set containing 140 observations from nine experiments (41 Holstein cows) was used to develop models predicting CH4 expressed as g/day, g/kg dry matter intake (DMI) and g/kg milk. The data set was divided into a training (n=112) and a test data set (n=28) for model development and validation, respectively. A generalized linear mixed model was fitted to the data using the marginal R 2 (m) and the Akaike information criterion to evaluate the models. The coefficient of determination of validation (R 2 (v)) for different models developed ranged between 0.18 and 0.41. Form the intake-related parameters, only inclusion of total DMI improved the prediction (R 2 (v)=0.58). In addition, in an attempt to further explore the relationships between MFA and CH4 emissions, the data set was split into three categories according to CH4 emissions: LOW (lowest 25% CH4 emissions); HIGH (highest 25% CH4 emissions); and MEDIUM (50% remaining observations). An ANOVA revealed that concentrations of several MFA differed for observations in HIGH compared with observations in LOW. Furthermore, the Gini coefficient was used to describe the MFA distribution for groups of MFA in each CH4 emission category. The relative distribution of the MFA, particularly of the odd- and branched-chain fatty acids and mono-unsaturated fatty acids of observations in category HIGH differed from those in the other categories. Finally, in an attempt to validate the potential of MFA to identify cases of high or low emissions, the observations were re-classified into HIGH, MEDIUM and LOW according to the proportion of each individual MFA. The proportion of observations correctly classified were recorded. This was done for each individual MFA and for the calculated Gini coefficients, finding that a maximum of 67% of observations were correctly classified as HIGH CH4 (trans-12 C18:1) and a maximum of 58% of observations correctly classified as LOW CH4 (cis-9 C17:1). Gini coefficients did not improve this classification. These results suggest that MFA are not yet reliable predictors of specific amounts of CH4 emitted by a cow, while holding a modest potential to differentiate cases of high or low emissions.

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

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

Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy

Enteric methane (CH4 ) production is among the main targets of greenhouse gas mitigation practices for the dairy industry. A simple, robust and inexpensive measurement technique applicable on a large scale to estimate CH4 emission from dairy cattle would therefore be valuable. Milk fatty acids (MFA) are related to CH4 production because of the common biochemical pathway between CH4 and fatty acids in the rumen. A summary of studies that investigated the predictive power of MFA composition for CH4 emission indicated good potential, with predictive power ranging between 47% and 95%. Until recently, gas chromatography (GC) was the principal method used to determine the MFA profile, but GC is unsuitable for routine analysis. This has led to the application of mid-infrared (MIR) spectroscopy. The major advantages of using MIR spectroscopy to predict CH4 emission include its simplicity and potential practical application at large scale. Disadvantages include the inability to predict important MFA for CH4 prediction, and the moderate predictive power for CH4 emission. It may not be sufficient to predict CH4 emission based on MIR alone. Integration with other factors, like feed intake, nutrient composition of the feed, parity, and lactation stage may improve the prediction of CH4 emission using MIR spectra. © 2016 Society of Chemical Industry.

Changes throughout lactation in phenotypic and genetic correlations between methane emissions and milk fatty acid contents predicted from milk mid-infrared spectra

The aim of this study was to estimate phenotypic and genetic correlations between methane production (Mp) and milk fatty acid contents of first-parity Walloon Holstein cows throughout lactation. Calibration equations predicting daily Mp (g/d) and milk fatty acid contents (g/100 dL of milk) were applied on milk mid-infrared spectra related to Walloon milk recording. A total of 241,236 predictions of Mp and milk fatty acids were used. These data were collected between 5 and 305 d in milk in 33,555 first-parity Holstein cows from 626 herds. Pedigree data included 109,975 animals. Bivariate (i.e., Mp and a fatty acid trait) random regression test-day models were developed to estimate phenotypic and genetic parameters of Mp and milk fatty acids. Individual short-chain fatty acids (SCFA) and groups of saturated fatty acids, SCFA, and medium-chain fatty acids showed positive phenotypic and genetic correlations with Mp (from 0.10 to 0.16 and from 0.23 to 0.30 for phenotypic and genetic correlations, respectively), whereas individual long-chain fatty acids (LCFA), and groups of LCFA, monounsaturated fatty acids, and unsaturated fatty acids showed null to positive phenotypic and genetic correlations with Mp (from -0.03 to 0.13 and from -0.02 to 0.32 for phenotypic and genetic correlations, respectively). However, these correlations changed throughout lactation. First, de novo individual and group fatty acids (i.e., C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, SCFA group) showed low phenotypic or genetic correlations (or both) in early lactation and higher at the end of lactation. In contrast, phenotypic and genetic correlations between Mp and C16:0, which could be de novo synthetized or derived from blood lipids, were more stable during lactation. This fatty acid is the most abundant fatty acid of the saturated fatty acid and medium-chain fatty acid groups of which correlations with Mp showed the same pattern across lactation. Phenotypic and genetic correlations between Mp and C17:0 and C18:0 were low in early lactation and increased afterward. Phenotypic and genetic correlations between Mp and C18:1 cis-9 originating from the blood lipids were negative in early lactation and increased afterward to become null from 18 wk until the end of lactation. Correlations between Mp and groups of LCFA, monounsaturated fatty acids, and unsaturated fatty acids showed a similar or intermediate pattern across lactation compared with fatty acids that compose them. Finally, these results indicate that correlations between Mp and milk fatty acids vary following lactation stage of the cow, a fact still often ignored when trying to predict Mp from milk fatty acid profile.

Effects of lactic acid bacteria silage inoculation on methane emission and productivity of Holstein Friesian dairy cattle

Inoculants of lactic acid bacteria (LAB) are used to improve silage quality and prevent spoilage via increased production of lactic acid and other organic acids and a rapid decline in silage pH. The addition of LAB inoculants to silage has been associated with increases in silage digestibility, dry matter intake (DMI), and milk yield. Given the potential change in silage and rumen fermentation conditions accompanying these silage additives, the aim of this study was to investigate the effect of LAB silage inoculants on DMI, digestibility, milk yield, milk composition, and methane (CH4) production from dairy cows in vivo. Eight mid-lactation Holstein-Friesian dairy cows were grouped into 2 blocks of 4 cows (multiparous and primiparous) and used in a 4×4 double Latin square design with 21-d periods. Methane emissions were measured by indirect calorimetry. Treatments were grass silage (mainly ryegrass) with no inoculant (GS), with a long-term inoculant (applied at harvest; GS+L), with a short-term inoculant (applied 16h before feeding; GS+S), or with both long and short-term inoculants (GS+L+S). All diets consisted of grass silage and concentrate (75:25 on a dry matter basis). The long-term inoculant consisted of a 10:20:70 mixture of Lactobacillus plantarum, Lactococcus lactis, and Lactobacillus buchneri, and the short-term inoculant was a preparation of Lc. lactis. Dry matter intake was not affected by long-term or short-term silage inoculation, nor was dietary neutral detergent fiber or fat digestibility, or N or energy balance. Milk composition (except milk urea) and fat and protein-corrected milk yield were not affected by long- or short-term silage inoculation, nor was milk microbial count. However, milk yield tended to be greater with long-term silage inoculation. Methane expressed in units of grams per day, grams per kilogram of DMI, grams per kilogram of milk, or grams per kilogram of fat and protein-corrected milk yield was not affected by long- or short-term silage inoculation. However, CH4 expressed in units of kilojoules per kilogram of metabolic body weight per day tended to be greater with long-term silage inoculation. Results of this study indicate minimal responses in animal performance to both long- and short-term inoculation of grass silage with LAB. Strain and dose differences as well as different basal silages and ensiling conditions are likely responsible for the lack of significant effects observed here, although positive effects have been observed in other studies.

Body fat mobilization in early lactation influences methane production of dairy cows

Long-chain fatty acids mobilized during early lactation of dairy cows are increasingly used as energy substrate at the expense of acetate. As the synthesis of acetate in the rumen is closely linked to methane (CH4) production, we hypothesized that decreased acetate utilization would result in lower ruminal acetate levels and thus CH4 production. Twenty heifers were sampled for blood, rumen fluid and milk, and CH4 production was measured in respiration chambers in week -4, +5, +13 and +42 relative to first parturition. Based on plasma non-esterified fatty acid (NEFA) concentration determined in week +5, animals were grouped to the ten highest (HM; NEFA > 580 μmol) and ten lowest (LM; NEFA < 580 μmol) mobilizing cows. Dry matter intake (DMI), milk yield and ruminal short-chain fatty acids did not differ between groups, but CH4/DMI was lower in HM cows in week +5. There was a negative regression between plasma NEFA and plasma acetate, between plasma NEFA and CH4/DMI and between plasma cholecystokinin and CH4/DMI in week +5. Our data show for the first time that fat mobilization of the host in early lactation is inversely related with ruminal CH4 production and that this effect is not attributed to different DMI.

Milk metabolome relates enteric methane emission to milk synthesis and energy metabolism pathways

Methane (CH4) emission of dairy cows contributes significantly to the carbon footprint of the dairy chain; therefore, a better understanding of CH4 formation is urgently needed. The present study explored the milk metabolome by gas chromatography-mass spectrometry (milk volatile metabolites) and nuclear magnetic resonance (milk nonvolatile metabolites) to better understand the biological pathways involved in CH4 emission in dairy cattle. Data were used from a randomized block design experiment with 32 multiparous Holstein-Friesian cows and 4 diets. All diets had a roughage:concentrate ratio of 80:20 (dry matter basis) and the roughage was grass silage (GS), corn silage (CS), or a mixture of both (67% GS, 33% CS; 33% GS, 67% CS). Methane emission was measured in climate respiration chambers and expressed as CH4 yield (per unit of dry matter intake) and CH4 intensity (per unit of fat- and protein-corrected milk; FPCM). No volatile or nonvolatile metabolite was positively related to CH4 yield, and acetone (measured as a volatile and as a nonvolatile metabolite) was negatively related to CH4 yield. The volatile metabolites 1-heptanol-decanol, 3-nonanone, ethanol, and tetrahydrofuran were positively related to CH4 intensity. None of the volatile metabolites was negatively related to CH4 intensity. The nonvolatile metabolites acetoacetate, creatinine, ethanol, formate, methylmalonate, and N-acetylsugar A were positively related to CH4 intensity, and uridine diphosphate (UDP)-hexose B and citrate were negatively related to CH4 intensity. Several volatile and nonvolatile metabolites that were correlated with CH4 intensity also were correlated with FPCM and not significantly related to CH4 intensity anymore when FPCM was included as covariate. This suggests that changes in these milk metabolites may be related to changes in milk yield or metabolic processes involved in milk synthesis. The UDP-hexose B was correlated with FPCM, whereas citrate was not. Both metabolites were still related to CH4 intensity when FPCM was included as covariate. The UDP-hexose B is an intermediate of lactose metabolism, and citrate is an important intermediate of Krebs cycle-related energy processes. Therefore, the negative correlation of UDP-hexose B and citrate with CH4 intensity may reflect a decrease in metabolic activity in the mammary gland. Our results suggest that an integrative approach including milk yield and composition, and dietary and animal traits will help to explain the biological metabolism of dairy cows in relation to methane CH4 emission.

Relationships between milk fatty acid profiles and enteric methane production in dairy cattle fed grass- or grass silage-based diets

We quantified relationships between methane production and milk fatty acid (FA) profile in dairy cattle fed grass- or grass silage-based diets, and determined whether recent prediction equations for methane, based on a wide variety of diets, are applicable to grass- and grass silage-based diets. Data from three studies were used, encompassing four grass herbage and 14 grass silage treatments and 132 individual cow observations. Methane production was measured using respiration chambers and milk fatty acids (FAs) analysed using gas chromatography. The proportion of grass or grass silage (dry matter (DM) basis) was 0.80 ± 0.037. Methane yield averaged 22.3 ± 2.10 g/kg DM intake (DMI) and 14.2 ± 2.90 g/kg fat- and protein-corrected milk (FPCM). Mixed model univariate regression including a random study effect on intercept was applied to predict methane yield, with individual milk FA concentrations (g/100 g FA) as fixed effects. Of the 42 milk FAs identified, no single FA had a strong positive correlation (r; strong correlation defined as |r| ≥ 0.50) with methane yield (g/kg DMI), and cis-12 C18:1 and cis-9,12,15 C18:3 had a strong negative correlation with methane yield (g/kg DMI). C14:0 iso, C15:0, C15:0 iso, C15:0 anteiso, C16:0, C20:0, cis-11,14 C20:2, cis-5,8,11,14 C20:4, C22:0, cis-7,10,13,16,19 C22:5 and C24:0 had a strong positive correlation with methane yield (g/kg FPCM), and trans-15+cis-11 C18:1, cis-9 C18:1, and cis-11 C20:1 had a strong negative correlation with methane yield (g/kg FPCM). Observed methane yield was compared with methane yield predicted by the equations of van Lingen et al. (2014; Journal of Dairy Science 97, 7115–7132). These equations did not accurately predict methane yield as grams per kilogram DMI (concordance correlation coefficient (CCC) = 0.13) or as grams per kilogram FPCM (CCC = 0.22), in particular related to large differences in standard deviation between predicted and observed values. In conclusion, quantitative relationships between milk FA profile and methane yield in cattle fed grass- or grass silage-based diets differ from those determined for other types of diets.

Prediction and evaluation of enteric methane emissions from lactating dairy cows using different levels of covariate information

The dairy sector contributes to global warming through enteric methane (CH4) emissions. Methane is also a loss of energy to the ruminant. Several studies have developed CH4 prediction models to assess mitigation strategies to reduce emissions. However, the majority of these models have low predictive ability or require numerous inputs that are often not readily available in commercial dairy operations. In this context, the objective of the present paper was to develop CH4 prediction models by using varying levels of information available at the farm level. The seven complexity levels used the following information: (1) dietary nutrient composition, (2) milk yield and composition, (3) Levels 1 and 2, (4) Level 3 plus dry matter intake (DMI), (5) Level 4 plus bodyweight, (6) Level 2 plus DMI, and (7) DMI only. Models were fitted to 489 individual enteric-CH4 measurements from 30 indirect calorimetry studies and evaluated with an independent database comprising 215 treatment means from 62 studies collected from the literature. Within each complexity level, all possible mixed-effect models were fitted and those with the lowest values of Akaike or Bayesian information criteria were selected using lme4 package in R. Models were evaluated using mean square prediction error (MSPE) based statistic, root MSPE (RMSPE) to observation standard deviation ratio, concordance correlation coefficient and Nash–Sutcliffe efficiency methods. All fitted models performed well with an acceptable error estimates (RMSPE as a percentage of observed mean (RMSPE%) = 16–24%), with more than two-thirds of total error originating from random bias. Overall, models with DMI were more accurate (RMSPE% = 16–20%) than those without (RMSPE% = 20–24%). Although the best prediction model (RMSPE% = 16%) was developed using Level 5 information, a model using Level 2 information is recommended for on-farm methane estimates if DMI is not measured. The proposed models offer easy and practical tools to dairy producers for predicting CH4 emissions and evaluating CH4 mitigation strategies.

Milk production and composition, and methane emissions from dairy cows fed lucerne hay with forage brassica or chicory

Forage brassica and chicory crops provide an alternative to perennial grass pastures as a forage supply for grazing dairy cows during summer, but there is little information about their effects on milk production and methane (CH4) emissions. Thirty-two Holstein–Friesian cows were fed for 10 days on a diet of lucerne cubes (750 g/kg DM) and grain (250 g/kg DM) (CON) or diets in which forage brassica (410 g/kg DM, FBR) or reproductive-stage chicory (410 g/kg DM, RCH) were offered with lucerne cubes (340 g/kg DM) and grain (250 g/kg DM). Cows offered the FBR diet produced more energy-corrected milk (25.4 kg/day) than did cows offered the CON diet (22.7 kg/day, P = 0.001), even though DM intake was not different for cows between the two groups (20.6 kg/day on average). In contrast, cows offered the RCH diet produced less energy-corrected milk (19.3 kg/day) than did cows in the other two groups (P = 0.001), reflecting the lower DM intake by cows offered the RCH diet (17.7 kg/day, P < 0.01). Methane yield (g CH4/kg DMI) was lower (P < 0.01) on the CON (21.0) and FBR (20.5) diets than on the RCH diet (26.1). Methane intensity (g/kg energy-corrected milk) was different (P < 0.01) for all diets, with CON (19.4) being intermediate, FBR (17.3) lowest and RCH (23.8) the greatest. Diet type was associated with differences in the proportions of only a small number of specific milk fatty acids, and differences in proportions of specific fatty acids were not related to CH4 emissions.