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

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.

Combined Metabolomics and Biochemical Analyses of Serum and Milk Revealed Parity-Related Metabolic Differences in Sanhe Dairy Cattle

The production performance of dairy cattle is closely related to their metabolic state. This study aims to provide a comprehensive understanding of the production performance and metabolic features of Sanhe dairy cattle across different parities, with a specific focus on evaluating variations in milk traits and metabolites in both milk and serum. Sanhe dairy cattle from parities 1 to 4 (S1, n = 10; S2, n = 9; S3, n = 10; and S4, n = 10) at mid-lactation were maintained under the same feeding and management conditions. The milk traits, hydrolyzed milk amino acid levels, serum biochemical parameters, and serum free amino acid levels of the Sanhe dairy cattle were determined. Multiparous Sanhe dairy cattle (S2, S3, and S4) had a greater milk protein content, lower milk lactose content, and lower solids-not-fat content than primiparous Sanhe dairy cattle (S1). Moreover, S1 had a higher ratio of essential to total amino acids (EAAs/TAAs) in both the serum and milk. The serum biochemical results showed the lower glucose and total protein levels in S1 cattle were associated with milk quality. Furthermore, ultra-high-resolution high-performance liquid chromatography with tandem MS analysis (UPLC-MS/MS) identified 86 and 105 differential metabolites in the serum and milk, respectively, and these were mainly involved in amino acid, carbohydrate, and lipid metabolism. S1 and S2/S3/S4 had significantly different metabolic patterns in the serum and milk, and more vitamin B-related metabolites were significantly higher identified in S1 than in multiparous cattle. Among 36 shared differential metabolites in the serum and milk, 10 and 7 metabolites were significantly and strongly correlated with differential physiological indices, respectively. The differential metabolites identified were enriched in key metabolic pathways, illustrating the metabolic characteristics of the serum and milk from Sanhe dairy cattle of different parities. L-phenylalanine, dehydroepiandrosterone, and linoleic acid in the milk and N-acetylornithine in the serum could be used as potential marker metabolites to distinguish between Sanhe dairy cattle with parities of 1-4. In addition, a metabolic map of the serum and milk from the three aspects of carbohydrates, amino acids, and lipids was created for the further analysis and exploration of their relationships. These results reveal significant variations in milk traits and metabolites across different parities of Sanhe dairy cattle, highlighting the influence of parity on the metabolic profiles and production performance. Tailored nutritional strategies based on parity-specific metabolic profiles are recommended to optimize milk production and quality in Sanhe cattle.

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.

Differences in Donor Animal Production Stage Affect Repeatability of In Vitro Rumen Fermentation Kinetics

In vitro gas production techniques (IVGPT) are widely used to screen feeds and feed additives to reduce the number of animals needed for experiments, which in turn, reduces costs and increases animal welfare. However, information about repeatability is scarce. The objective of this study was to evaluate the variation from in vitro gas production fermentations in the same laboratory using the same feed substrate. The source of rumen fluid used in the fermentations was from two different farms with either cannulated lactating dairy cows or cannulated fasting heifers, representing two distinct stages of production (donor types). Seventeen 24 h fermentations, undertaken during a year, were used to evaluate the variation between the following parameters: gas curve parameters, baseline-corrected total gas production (TGP (mL at Standard Temperature and Pressure (STP))/g incubated dry matter (DM)), methane concentration (%) and yield (mL gas at STP/g DM), pH and degraded dry matter (dDM). Significant differences between donor types were found for the pH of the rumen fluid from individual animals and pH of fermented fluid. However, no significant differences were observed within donor type. The means for methane concentration and yield, after 24 h of fermentation, were not significantly different between or within donor types. Rate of early gas production was significantly different between donor types, but baseline-corrected TGP was not significantly different at 24 h. No dDM differences after 24 h of fermentation between or within donor types were detected. Gas production curves were different between donor types, being either a monophasic version of the sigmoidal model or an exponential curve for the heifers and the production animals, respectively. No differences were observed within type. Repeatability of rumen fluid (CVRF), calculated as the coefficient of variation, and the associated parameters, which were investigated, was best for methane yield (CVRFALL = 0.3%) and least for TGP at 3 h (CVRFALL = 3%). Repeatability was dependent on donor type.

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.

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.

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.

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.

Role of Fatty Acids in Milk Fat and the Influence of Selected Factors on Their Variability-A Review

Fatty acids (FAs) of milk fat are considered to be important nutritional components of the diets of a significant portion of the human population and substantially affect human health. With regard to dairy farming, the FA profile is also seen as an important factor in the technological quality of raw milk. In this sense, making targeted modifications to the FA profile has the potential to significantly contribute to the production of dairy products with higher added value. Thus, FAs also have economic importance. Current developments in analytical methods and their increasing efficiency enable the study of FA profiles not only for scientific purposes but also in terms of practical technological applications. It is important to study the sources of variability of FAs in milk, which include population genetics, type of farming, and targeted animal nutrition. It is equally important to study the health and technological impacts of FAs. This review summarizes current knowledge in the field regarding sources of FA variability, including the impact of factors such as: animal nutrition, seasonal feed changes, type of animal farming (conventional and organic), genetic parameters (influence of breed), animal individuality, lactation, and milk yield. Potential practical applications (to improve food technology and consumer health) of FA profile information are also reviewed.