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

J. de Souza; H. Leskinen; A. L. Lock; K. J. Shingfield; P. Huhtanen

doi:10.1371/journal.pone.0235357

Abstract

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.

Citation: de Souza J, Leskinen H, Lock AL, Shingfield KJ, Huhtanen P (2020) Between-cow variation in milk fatty acids associated with methane production. PLoS ONE 15(8): e0235357. https://doi.org/10.1371/journal.pone.0235357

Editor: Alex V. Chaves, The University of Sydney, AUSTRALIA

Received: December 24, 2019; Accepted: June 14, 2020; Published: August 6, 2020

Copyright: © 2020 de Souza et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and Supporting Information files.

Funding: PH: Grant from Formas (Formas is a government research council for sustainable development) ((https://formas.se/en/start-page.html) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Enteric methane (CH₄) production is one of the main sources of green-house gas (GHG) emissions from dairy production systems, and enteric CH₄ production is among the main targets of GHG mitigation practices for the dairy industry [1]. Therefore, mitigating enteric CH₄ emissions is an approach for improving sustainability and profitability of dairy production systems [2]. Direct measurements of CH₄ are difficult to perform under regular farm conditions; therefore, the development of prediction equations to estimate CH₄ output has gained significance [3–5]. Changes in absorbed fatty acid (FA) composition affected by ruminal metabolism and microbial synthesis of FA can affect milk FA composition [6], and may therefore predict changes in the ruminal fermentation associated with CH₄ emissions. It is well established that de novo synthesis in the mammary gland yields short and medium-chain FA (4 to 14 carbons) and a portion of the 16-carbon FA derived from acetate and to a lesser extent BHBA. The remaining 16-carbon and all of the longer-chain FA (greater than 16 carbons) are taken up from the circulating plasma pool originated from absorption from the digestive tract or mobilization from body reserves. Additionally, odd- and branched-chain FA (OBCFA) in milk fat are largely derived from bacteria leaving the rumen [7] and have been suggested as potential biomarkers for rumen function [6]. The potential utilization of milk FA to predict CH₄ has been studied from direct in vivo measurements [8, 9] and from meta-analysis approaches [10, 11]. These models have selected several different FA as potential CH₄ predictors, which indicates an important influence of other dietary and animal factors influencing these estimates.

Variation in CH₄ production has been also attributed to animal factors [3, 4]. Studies conducted in sheep have shown that the variation in ruminal digesta retention time or passage rate is related to CH₄ emissions, with high CH₄ emitters having a larger rumen volume and digesta pools than low CH₄ emitters [12]. Recently, Cabezas-Garcia et al. [13] reported that variables related to animal physiology, such as variation in digesta retention time, can explain most of the between-animal variations in CH₄ production. Only small variations were observed in rumen fermentation variables, especially stoichiometric Y_CH₄VFA, suggesting a minor contribution of the rumen microbiome to CH₄ production. Since some studies have indicated that potentially several individual milk FA can be used to predict CH₄ emission in lactating dairy cows, the examination of between-animal differences in a data set originating from variations in digestion physiology and different diets is important. Also, because animal variation is likely to be under genetic control, one option to mitigate CH₄ emissions that has been suggested is to select for animals that emit less. Heritability of some major milk FA have been previously determined [14, 15]. However, although a large range in the heritability of specific milk FA were reported [14, 15], they did not report heritability and variation in milk FA directly related with rumen function (i.e. OBCFA and trans-FA). Since potentially several individual milk FA can be used to predict CH₄ emission in lactating dairy cows, the examination of between-animal differences in a data set originating from variations in digestion physiology and different diets is important. Additionally, integration of data related to rumen function with nutrient outflow and milk output may allow for a better understanding of the variables involved in the observed between-animal and between-diets variation. The objective of our meta-analysis was to evaluate b-cow variation and repeatability in omasal fatty acids and milk fatty acids associated with CH₄ emission.

Materials and methods

Data

The dataset was originated from 9 studies [16–25], 29 cows and 33 different diets of rumen cannulated Nordic red dairy cows, conducted using either a Latin square or switch-back design conducted in the Finland (S1 Table). These studies evaluated a wide range of different dietary condition including different forages strategies, forage conservation method, forage: concentrate levels, and supplementation with different fatty acids. The mean forage-to-concentrate ratio of the diets was 57:43 on a DM basis. The concentrate supplements consisted principally of cereal grains, fibrous by-products from the food industry, and protein supplements, typically canola meal. In some studies diets were supplemented with sunflower oil, rapeseed oil, linseed oil or fish oil. Formic acid-treated grass silage was the main forage source, but red clover silage, extensively fermented grass silage (no additives), fresh chopped grass, and barn-dried hay were used in some studies. The diets were fed ad libitum or at 90 to 95% of ad libitum intake as TMR or fixed amounts of concentrate with forage ad libitum. The complete data set consisted of 135 cow/period observations, which were the experimental unit. A minimum pre-condition for inclusion of a study in the meta-analysis was that feed intake, BW, milk production data, fermentation parameters, omasal FA, and milk FA profile were available.

Individual cow intakes and milk yield were recorded daily throughout the experiment, but only measurements for the last 4–7 d were used for analysis. Samples of milk were collected from each cow over 4 consecutive milkings. Milk samples treated with preservative (bronopol; Valio Ltd., Helsinki, Finland) and were stored at 4°C until analyzed for milk components (MilkoScan 133B analyzer; Foss Electric A/S, Hillerød, Denmark). Unpreserved milk samples were also collected at the same time, stored immediately at −20°C, and composited according to milk yield until analyzed for FA composition. Body weight was measured weekly.

Diet digestibility was determined by total feces collection over 4 to 5 days. Digesta flow measurements were conducted using the omasal sampling technique [26] with a triple-marker system [27] based on Co-EDTA or Cr-EDTA, Yb-acetate, and Cr-mordanted straw or indigestible NDF as markers for liquid, small, and large particles, respectively. Rumen fluid samples (n = 7 or 8) were collected at 1.5 intervals (approximately 500 mL) starting just before morning feeding at 0600 h through rumen cannula using a vacuum pump and flexible tube and analyzed for pH, VFA and ammonia N concentrations [28]. Spot samples (500 mL) of digesta entering the omasal canal were collected 3 times daily at 4-h intervals during 4 consecutive days, to cover a 12-h period that was considered representative of the entire feeding cycle composited, and separated into large particle, small particle, and liquid phases. Each phase was freeze-dried and stored at −20°C, whereas subsamples of each fraction collected for FA analysis were stored at −20°C. Metabolizable energy content of experimental diets was calculated from the concentration of digestible nutrients [0.016 × digestible OM in DM (g/kg); Ministry of Agriculture, Fisheries and Food, 1975] determined by total fecal collection. The energy requirement (MJ/d) for maintenance and milk production was calculated as [BW (kg ^0.75) × 0.515 + ECM yield (kg/d) × 5.15]; [29]. Energy balance was calculated as ME intake–ME maintenance–ME production, all expressed as MJ/d.

The VFA ratios acetate/propionate and propionate/butyrate were calculated, and the lipogenic:glucogenic ratio of VFA was determined as (acetate + butyrate)/ propionate. Production of CH₄ per mole of VFA (Y_CH₄VFA) was calculated based on VFA stoichiometry [30] as: where C₂, C₃, and C₄ are molar proportions (mmol/mol) of acetate, propionate, and butyrate, respectively, in the sum of these VFA.

Total lipid in milk, oil supplements, freeze-dried feed samples and omasal digesta were converted to FA methyl esters (FAME) using standard methods [31, 32]. The FAME were quantified using a gas chromatograph equipped with a flame-ionization detector and a CP-Sil 88 column (100 m x 0.25 mm id., 0.2 μm film thickness; Agilent Technologies, Santa Clara, CA).

Statistical analysis

All analysis were performed using the MIXED procedure of SAS (SAS version 9.3, SAS Institute Inc., Cary, NC). Variance components of the selected variables was calculated considering random factors of experiment (Exp), diet within experiment [Diet (Exp)], period within experiment [Period (Exp)], and cow within experiment [Cow (Exp)]. Covariance structure was defined in the model using the TYPE = VC (variance components) option in the RANDOM statement. The standard deviation and coefficient of variation for each factor were calculated as the square root of the variance estimate and standard deviation divided by the respective mean value of each factor.

Repeatability values (Rep) for the most relevant variables associated with enteric CH₄ production were calculated as where and are Cow (Exp) and residual variances, respectively. Repeatability values provide an estimate of the correlation between values from consecutive samples on the same cow, on the same diet, and within the same period of the same experiment. For this study, repeatability was classified as low (<0.25), moderate (0.26–0.50) and high (>0.50).

Empirical models were developed between the variables of interest regarding their biological value by regression analysis within the MIXED procedure of SAS, using the following model: where Y_ij = the expected value for the dependent variable Y observed at level of j of the independent variable X in study i; B₀ = the overall intercept (fixed effect); b₀ = the random effect of study i on the intercept (i = 1, …, 9); B₁ = the regression coefficient of Y on X₁ of Y across all studies (fixed effects), X_1ij = value j of the continuous variable X₁ in study i; bi = the random effect of study i on the regression coefficient of Y on X1 in study i (i = 1, …, 9), and eij = the residual error.

The models included 2 random statements: a random intercept and slope of X₁ with SUBJECT = Diet (Exp), and a random intercept with SUBJECT = Period (Exp), using the TYPE = VC as the covariance structure for both random statements. The method = ML (maximum likelihood) statement was used in the PROC MIXED model syntax. Only one random independent variable was used to avoid overparameterized models and improve convergence [33].

Results

Data description

Mean and ranges of nutrient intake, production responses and rumen fermentation parameters are presented in Table 1. Despite the large differences in diet composition, rumen pH and the proportions of major rumen VFA did not vary greatly compared with the proportions of minor VFA. The large variation in intake of total FA is related to several studies in this data set supplemented different sources of FA.

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Table 1. Minimum, maximum, mean, and standard deviation values for milk yield, milk composition, energy balance, nutrient intake, rumen fermentation, digestibility, and predicted CH₄ in the data set.

https://doi.org/10.1371/journal.pone.0235357.t001

Mean and ranges of proportion of omasal FA are presented in Table 2. As expected, the predominant FA in the omasum was C18:0 with a wide range of it as a proportion of omasal FA. Also, C16:0, trans-11 C18:1, cis-9 C18:1, and cis-9, cis-12 C18:2 represented the main FA present in the omasum. Variation in the proportion of OBCFA was a similar across all of these FA.

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Table 2. Minimum, maximum, mean, and standard deviation values for selected fatty acids (FA) content in omasal digesta in the data set.

https://doi.org/10.1371/journal.pone.0235357.t002

Mean and ranges of milk FA profiles are presented in Table 3. As expected the major FA in milk fat were C16:0, cis-9 C18:1, and C18:0. Although preformed FA were the major FA in milk fat, large variation in the summation of de novo FA, mixed FA and preformed milk FA was also observed. There was a similar variation among the milk OBCFA with iso C17:0 showing the greatest variation. Also, the large variation in milk trans-10 C18:1 and trans-11 C18:1 is related to several studies in this data set which had diets that induced milk fat depression.

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Table 3. Minimum, maximum, mean, and standard deviation values for selected milk fatty acids (FA) in the data set.

https://doi.org/10.1371/journal.pone.0235357.t003

Variance components

In general, the effect of experiment (Exp) was the largest source of variation observed in the data set (not shown). The variance components for rumen fermentation patterns are shown in Table 4. For the molar proportions of acetate and propionate, we observed that the variation related to diet was more than double the b-cow variation, with moderate repeatability, whereas for butyrate and isobutyrate the diet and b-cow variance components were similar. Diet and b-cow variance components were similar for both rumen pH and total VFA, and they were more repeatable than molar proportions of individual VFA. The b-cow variation for Y_CH₄VFA was only 0.010, and repeatability was low.

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Table 4. Variance component estimates for methane estimate and rumen fermentation parameters in dairy cows.

https://doi.org/10.1371/journal.pone.0235357.t004

The variance components for the proportion of omasal FA are presented in Table 5. For the OBCFA including iso C13:0, anteiso C13:0, iso C15:0, iso C16:0, iso C17:0, anteiso C17:0, C17:0 and iso C18:0 the variation associated with diet was greater than the between-cow variation with low repeatability. For anteiso C15:0 and C15:0 the variation associated with diet was also greater than the between-cow variation with moderate repeatability. Although the variation associated with diet for C16:0 was more than double the between-cow variation, this FA had the highest repeatability. C18:0, cis-9 C18:1 and cis-11 C18:1 had low repeatability, and the variation associated with diet was greater than the between-cow variation. Similarly, for trans-10 C18:1, trans-11 C18:1, cis-9, cis-12 C18:2 and cis-9 cis-12 cis-15 C18:3 diet variation was greater than between-cow variation with low repeatability, whereas cis-9 trans-11 C18:2 had diet and between-cow variance components similar and high repeatability.

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Table 5. Variance component estimates of omasal fatty acids (FA) in dairy cows.

https://doi.org/10.1371/journal.pone.0235357.t005

The variance components for milk FA are presented in Table 6. Milk FA can be classified in three groups because they are derived from 2 sources: de novo synthesis in the mammary gland (< 16 carbon FA) and originating from extraction from plasma (> 16 carbon FA). Mixed source FA (16-carbon milk FA) can be originate from both pools. Interestingly, for the summation of milk FA by source (de novo, mixed and preformed), the diet variation was greater than the b-cow variation, but these groups of FA had moderate to high repeatability. For de novo milk FA C4:0, C6:0 and C8:0 the diet variation was approximately 3-fold greater than the b-cow variation; repeatability for these FA was moderate to high. For OBCFA including iso C13:0, anteiso C13:0, iso C16:0, iso C17:0, the diet variation was 2 to 3-fold the b-cow variation with low repeatability. Although diet variation was greater than b-cow variation for iso C15:0, C15:0, anteiso C15:0, anteiso C17:0, C17:0, and iso C18:0, these FA had moderate repeatability. Similarly, diet variation for C16:0 was more than double the b-cow variation, though this FA had the highest repeatability. C18:0, and cis-11 C18:1 had moderate repeatability, and the variation related to diet was greater than the b-cow variation. For both preformed milk FA (cis-9 C18:1 and cis-9, cis-12, cis-15 C18:3) diet variation was greater than the b-cow variation, but repeatability was moderate. For trans-10 C18:1, trans-11 C18:1 and cis-9 trans-11 C18:2 diet variation was greater than b-cow variation with low repeatability.

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Table 6. Variance component estimates of milk fatty acids (FA) in dairy cows.

https://doi.org/10.1371/journal.pone.0235357.t006

Empirical models–simple regressions

Relationships between milk FA and stoichiometric Y_CH₄VFA are presented in Fig 1 and S2 Table. The C4:0 and C6:0 (P < 0.05) were positively related with stoichiometric Y_CH₄VFA. For the OBCFA, anteiso C15:0 (P < 0.01) and C15:0 (P < 0.01) were negatively associated with stoichiometric CH₄VFA. Additionally, milk trans-11 C18:1 (P = 0.02), cis-11 C18:1 (P < 0.01), cis-9, trans-11 C18:2 (P = 0.01), and cis-9, cis-12, cis-15 C18:3 were negatively related with stoichiometric CH₄VFA. There was no relationship between the summation of milk FA by source (de novo, mixed and preformed) and stoichiometric CH₄VFA. We evaluated the relationship between milk OBCFA and rumen VFA (S3 Table). There was no relationship (P > 0.05) between both anteiso C13:0 and iso C13:0 milk FA and concentration of rumen VFA (propionate, valerate, isovalerate, and BCVFA). Milk C15:0 was positively associated with rumen propionate (P < 0.01) and valerate (P = 0.01). Milk anteiso C15:0 was positively associated with rumen propionate (P = 0.05), while milk iso C15:0 was negatively associated with isovalerate (P = 0.01) and BCVFA (P = 0.02). Milk anteiso C17:0 was positively associated with isovalerate (P < 0.01) and BCVFA (P = 0.01), while C17:0 was negatively related to with BCVFA (P = 0.05).

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Fig 1. Influence of selected milk fatty acid (FA) on Y_CH₄VFA estimated by univariate mixed model regression analysis (CH₄VFA = A + BX₁) in dairy cows.

https://doi.org/10.1371/journal.pone.0235357.g001

The relationship between concentrations and flows of omasal OBCFA on milk FA are presented in Figs 2 and 3. We observed positive relationship between concentration of omasal OBCFA and the concentration of milk OBCFA (P < 0.01), as well as positive intercepts for all of the OBCFA evaluated (P < 0.01). Similarly, for most OBCFA, we observed positive relationships between omasal flow of OBCFA and yield of milk OBCFA (P < 0.01) with exception for milk iso C17:0 that was not affected by iso C17:0 omasal flow (P = 0.13). Regarding the intercept values, for all milk OBCFA, we observed positive intercepts (P < 0.01).

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Fig 2. Influence of omasal fatty acid (FA) concentration (g 100 g/ FA) on milk fatty acid concentration (g 100 g/ FA) estimated by univariate mixed model regression analysis (OBCFA = A + BX₁) in dairy cows.

https://doi.org/10.1371/journal.pone.0235357.g002

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Fig 3. Influence of omasal fatty acid (FA) flow (g/d) on milk fatty acid yield (g/d) estimated by univariate mixed model regression analysis (OBCFA = A + BX₁) in dairy cows.

https://doi.org/10.1371/journal.pone.0235357.g003

Empirical models–multiple regressions

We evaluated whether calculated ME balance and proportion of omasal FA would affect milk FA profile (Table 7). The concentration of several milk FA was affected by energy balance. Some milk OBCFA (iso C13:0, iso C15:0, anteiso C15:0, and C15:0) were positively associated with the omasal proportion of their respective FA (all P < 0.01) and by energy balance (P < 0.01). In contrast, the concentration of milk C17:0, iso C18:0, C18:0, and cis-11 C18:1 were positively influenced by omasal proportion of their respective FA (P < 0.01) but negatively associated with energy balance (P < 0.05). For milk cis-9 C18:1, there was no effect of omasal cis-9 C18:1 (P = 0.69), but it was inversely related with energy balance (P < 0.01). We observed minor effects of DMI associated with rumen VFA on milk OBCFA (S4 Table).

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Table 7. Influence of energy balance and composition of omasal fatty acids (FA) on milk FA concentration estimated by multivariate mixed model regression analysis (Milk FA = A + BX₁ + CX₂) in dairy cows.

https://doi.org/10.1371/journal.pone.0235357.t007

Discussion

Lately, several studies have focused on developing reliable and low-cost measures of ruminant enteric CH₄ emissions on an individual-animal basis. Determining the variability among cows offers the potential for genetic selection of animals that have lower CH₄ emissions, which is an attractive mitigation strategy because genetic improvements are cumulative and permanent [34]. Milk FA are a promising CH₄ proxy because of the direct link to microbial digestion in the rumen and energy balance [35]. Additionally, breeding for reduced CH₄ production has been proposed and therefore indicators of CH₄ production based on milk FA are of particular interest [9, 36]. A large range in the heritability of CH₄ production (h²: 0.12 to 0.44) estimated using milk FA has been reported [15], even though the R² of the equations were not much different (0.63 to 0.73). In the present study, repeatability and b-cow variation estimated by variance components were used to identify suitable animal variables of rumen fermentation, omasal FA and milk FA related to b-cow differences in estimated CH₄ emissions. Despite the limited number of observations in our analysis due to our selection criteria focused on the integration of data on fermentation parameters, omasal FA and milk FA, we established important relationships involving animal factors, digestion, omasal flow and milk output related to CH₄.

A limitation in our study is that we did not have a direct measurement of CH₄, but rather we used the approach proposed by Wolin [30] to calculate Y_CH₄VFA. This method could be criticized because it assumes that all fermented substrates have a formula C₆H₁₂O₆, while some carbohydrates deviate from this general formula. Although this consideration is important, these carbohydrates usually comprise only a small part of ruminant diets [30]. Furthermore, deviations from the C₆H₁₂O₆ formula could influence variance component of Diet (Exp) but not that of Cow (Exp), which is the major interest of this study. Additionally, the stoichiometric relationships between VFA production and production of H₂ (substrate for hydrogenotrophic methanogens) suggest that CH₄ emissions are positively associated with the acetate:propionate ratio in ruminal fluid; however, the relationship between CH₄ emission and both VFA and pH are variable in the literature and not as straightforward as expected from theory [35]. In our study, the b-cow coefficient of variation (CV) for Y_CH₄VFA was only 0.01, while the variation in the variance components for diet was 3 times greater than the b-cow variation. Similar to our results, b-cow CV of 0.01, and 0.104 for predicted Y_CH₄VFA, and total CH₄ production were previously reported [13]. Between-animal CV in CH₄ production reported in the literature differ, reflecting differences in feed intake and methodology.

Rumen VFA pattern can be expected to be related to CH₄ production due to changes in H₂ balance, such that high acetate and butyrate production enhance CH₄ production, whereas high propionate production is associated with low CH₄ emissions [37]. In the present study, b-cow variation in rumen VFA pattern was small (CV ranged from 0.01 to 0.05). Similarly, a previous study reported for sheep a CV for CH₄ production of 0.098 [38], whereas the CV for molar proportions of acetate, propionate, and butyrate was 0.011, 0.047, and 0.036, respectively. Greater b-cow variation and repeatability for traits such as digestibility, passage, and efficiency of microbial cell synthesis has been previously reported [13] indicating that between-animal variation in CH₄ may be more closely related to these characteristics than the composition of the rumen microbiome.

The milk fat in ruminants contain greater proportions of saturated FA compared with dietary intake because of extensive biohydrogenation of unsaturated FA in the rumen [39]. During the biohydrogenation of FA, several trans FA intermediates are formed under different dietary conditions [40], and therefore they may also be indicators of changes in rumen function. In our study, for both omasal and milk trans FA (trans-10 C18:1 and trans-11 C18:1) diet variation was 5-fold greater than b-cow variation indicating that rumen conditions influencing the synthesis of these FA was more strongly associated with differences in diets than with differences between the cows. Variance components and repeatability of trans-10 C18:1 and trans-11 C18:1 were similar between omasal flow and milk indicating that these FA are more related with ruminal changes than post-ruminal metabolism. Also, milk trans-11 C18:1, cis-11 C18:1, cis-9, trans-11 C18:2, and cis-9, cis-12, cis-15 C18:3 were negatively related with stoichiometric Y_CH₄VFA. Similarly, a previous study [9] indicated a negative correlation between concentration of cis-9, cis-12, cis-15 C18:3 in milk fat and CH₄ production. Additionally, milk trans-11 C18:1 and cis-11 C18:1 were negative correlated with CH₄ production [10]. Negative association of some unsaturated FA (i.e. cis-9 C18:1) with CH₄ production are expected, especially during negative energy balance where intake and CH₄ production are low compared with cows in positive energy balance. Also, a negative association of unsaturated FA and CH₄ is expected since during biohydrogenation some H₂ are used by rumen bacteria. In addition, increased unsaturated FA in milk may indicate dietary unsaturated fat supplementation and thus decreased intake of fermentable carbohydrates, and or reduce ruminal fermentation of organic matter, and thereby CH₄ production. Furthermore, several trans and cis FA occurring in milk fat are biohydrogenation intermediates of both cis-9, cis-12 C18:2 and cis-9, cis-12, cis-15 C18:3 [39]. Rumen conditions with low fiber and high concentrate diets may induce changes in the extent of biohydrogenation and formation of biohydrogenation intermediates [40]. With reduced rumen pH, the predominant biohydrogenation pathway of cis-9, cis-12 C18:2 may shift to the trans-10 pathway [40]. Therefore, these observations explain the negative correlation obtained between the concentration of some milk trans FA and CH₄ production, whereas diet factors are more strongly related to the differences in these FA than to between-animal differences.

OBCFA are suggested to reflect rumen function including ruminal fermentation pattern, duodenal flow of microbial protein and acidosis [6]. Overall for OBCFA omasal flow the variation associated with diet was considerably greater than the between-cow variation with low repeatability. Furthermore, in our study, we observed weak associations between rumen VFA profile and milk iso and anteiso OBCFA. Similarly, a previous study observed that rumen and milk OBCFA responses were minimal following infusion of large amounts of VFA (acetate, propionate and isovalerate) and suggested that shifts in ruminal OBCFA are primarily affected by altered populations of different rumen microbial strains driven by dietary composition as opposed to altered VFA available in the extracellular space for FA synthesis [41]. In the rumen, de novo FA in bacteria are synthesized by two types of FA synthetases: straight-chain and branched-chain FA synthetase [42]. Linear odd-chain FA are formed when propionyl-CoA, instead of acetyl-CoA, is used as a primer [42]. In our study, we observed that milk C15:0 was positively associated with rumen propionate and valerate, which agrees with previous findings suggesting that C15:0 and C17:0 are formed through elongation of propionate or valerate [6]. Additionally, when we considered DMI in the equations, we also observed a positive effect of propionate and DMI on milk C15:0, while for C17:0 a positive relationship with valerate and DMI was detected. Similar to our results, [43] reported milk concentrations of C15:0 and the sum of C17:0 and cis-9 C17:1 to be positively related to propionate concentration in the rumen. Since it is expected that propionate production is negatively related to CH₄ production, this suggests a negative relationship between the concentration of these OBCFA in milk and CH₄ production. In the present study, we did not observe an association between the proportion of omasal C15:0 and C17:0 and Y_CH₄VFA, while milk C15:0 was negatively related to CH₄VFA. Similarly, the results from previous studies have been equivocal and reporting negative correlations between milk C15:0 and C17:0 and CH₄ production [44] or no significant relationships between these FA with CH4 yield [10].

Importantly, when we evaluated the effect of omasal OBCFA (g/100 g of FA) on their respective milk OBCFA (g/100 g of FA) and the omasal flow of OBCFA (g/d) on their respective yield of milk OBCFA (g/d), we detected positive intercepts, which may indicate endogenous synthesis or elongation in the mammary gland. Similar to our results, a previous study reported greater secretion of C15:0, C17:0, and iso C17:0 in milk fat than could be accounted for by intestinal absorption [45]. In the mammary gland, endogenous chain elongation using propionyl-CoA as precursor [46] explains the occurrence of certain odd-chain FA (i.e. C5:0, C7:0, C9:0 and C11:0) in milk and it may also increase the amount of other odd-chain FA transferred from the duodenum (C13:0, C15:0 and C17:0) into milk. Also, milk secretion of iso C17:0 and anteiso C17:0 in excess of duodenal flows of those FA has been also observed indicating synthesis in tissues [47]. Limited synthesis of the iso 17:0 has also been reported [7], and methodological issues due to coelution of cis-9 C16:1 with anteiso C17:0 [48] are also possible factors that affect these differences between omasal flow and milk FA secretion. In addition to mammary gland, other tissues have also been shown to have the ability to synthesize OBCFA from propionate [49] and, therefore, OBCFA that are present in milk in greater amounts than their respective duodenal flow could partially be a result of the synthesis in the mammary gland and increasing amounts of OBCFA mobilized from other tissues. Repeatability was lower for iso C13:0, iso C15:0, and iso C17:0 omasal flow compared to milk output. Additionally, we observed that milk iso C13:0 and iso C15:0 were positively associated to rumen propionate concentration and feed intake, which in turn suggest that potentially other factors can affect the output of these OBCFA in milk. Therefore, the endogenous synthesis of OBCFA, elongation of some OBCFA into their longer chain equivalents, and synthesis in other tissues may limit their use as biomarkers of rumen function and CH₄ proxy.

Additionally, we observed that energy balance is an important factor influencing milk FA profile. For milk OBCFA, 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. Similar to our results, Craninx et al. [50] reported that OBCFA with chain lengths of 14 or 15 carbon atoms showed an increasing pattern as lactation period progressed and cows entered in positive energy balance, whereas OBCFA with chain lengths of 17 carbon atoms showed the opposite pattern of response. In dairy cows, cis- 9 C18:1, C18:0 and C16:0 are the main FA present in adipose tissue [51]. During early lactation, mobilization of body reserves of fat increases the circulation of these FA and their uptake by the mammary gland. Therefore, the decrease in the concentration of these OBCFA in milk fat may be a dilution effect since other long-chain FA will increase during periods of negative energy balance. Therefore, some of the inconsistency when predicting CH₄ using concentration of milk FA can be explained by energy balance and lactation stage, both being factors that can influence the relationship between milk FA and CH₄ emission.

The short- and medium-chain FA (4 to 14 carbons) and a portion of the 16-carbon FA are derived from de novo synthesis from acetate and to a lesser extent BHBA [40]. Therefore, since acetate and butyrate production in the rumen is associated with H₂ production, some de novo milk FA may be a proxy for CH₄. In contrast to our expectation, we found weak relationships between most de novo milk FA and Y_CH₄VFA, with only the concentration of milk C4:0 and a tendency for C6:0 being positively associated with Y_CH₄VFA. A positive correlation between de novo FA and CH₄ (g/d) has been reported [9], while others reported that C12:0, and C14:0 were positively associated with CH₄ (g/d) [52]. Also, for de novo milk FA C4:0, C6:0 and C8:0 and for C16:0 the diet variation was 3-fold higher than the b-cow variation, but repeatability for these FA was high. Also, a previous study reported that the concentration of C16:0 in milk fat was moderately positively related to CH₄ yield (g/kg of DMI), and concentrations of C6:0, C8:0, and C10:0 in milk fat tended to be weakly positively related to CH₄ yield [10]. Although diet is still the major factor impacting the variance components of de novo FA, these FA seem more promising as proxies for CH₄ parameters as heritability for short and medium chain FA are greater than those for mixed and unsaturated milk FA [53]. However, selecting animals for low C4:0 to C12:0 milk FA may result in lower CH₄, but also may reduce milk fat content and yield due to the correlation between milk de novo FA concentration and these traits.

Although we estimated CH₄ using VFA stoichiometry rather than directly measuring CH₄ our b-cow estimates are in line with previous reports. Additionally, we generated b-cow estimates for trans-FA and OBCFA that have been related with rumen function, but our analysis of omasal flow of FA and animal factors suggest that factors, such as feed intake and energy balance, should be considered because they are likely associated to post-ruminal changes in the appearance of these FA into milk fat. Additionally, a recent study used the equations from the meta-analysis of van Lingen et al. [10] to quantify the CH₄ emissions traits predicted by selected milk FA and to assess their main sources of variation [54]. They reported wide variability in estimated CH₄ emissions traits among different farms within dairy system (TMR fed vs. hay + concentrate) indicating that factors related with feeding management and other animal management practices are likely related to CH₄ emissions [54]. Feeding management factors (i.e. feeding frequency, bunk space, etc.) influence feed intake, slug-feeding, rumen pH and animal behavior [55], which in turn may affect CH₄ emissions. Although we did not characterize and have available data in our data set regarding feed management, we cannot rule out the possibility that factors that influence feed behavior may impact CH₄ estimates.

Conclusion

Our findings demonstrate that for most omasal and milk FA examined, a larger variation can be attributed to dietary factors than b-cow differences with low to moderate repeatability. Even though we observed that some milk FA were positively or negatively associated with Y_CH₄VFA, other factors such as energy balance had a pronounced effect on these estimates. Therefore, this may preclude the utilization of milk FA as a proxy for CH₄ predictions. Based on our dataset, between-animal variation in milk FA profile was small, which may suggest that caution should be exercised when using milk FA to select low-emitting animals in breeding programs. Because of the greater between-diet variability compared with between-animal variation for most milk FA, they may be used as a proxy for detecting differences between diets and farms; however, these differences can also be predicted by empirical models.

Supporting information

S1 Table. Data sources and characteristics of included studies.

https://doi.org/10.1371/journal.pone.0235357.s001

(DOCX)

S2 Table. Influence of milk fatty acid (FA) on stoichiometry methane (CH₄VFA) estimated by univariate mixed model regression analysis (CH₄VFA = A + BX₁) in dairy cows.

https://doi.org/10.1371/journal.pone.0235357.s002

(DOCX)

S3 Table. Influence of rumen VFA on the concentration of milk odd- and branched-chain fatty acids (OBCFA) (g 100 g/ FA), estimated by univariate mixed model regression analysis (OBCFA = A + BX₁) in dairy cows.

https://doi.org/10.1371/journal.pone.0235357.s003

(DOCX)

S4 Table. Influence of rumen VFA, and DMI on milk odd- and branched-chain fatty acids (OBCFA), estimated by bivariate mixed model regression analysis (OBCFA = A + BX₁ + BX₂) in dairy cows.

https://doi.org/10.1371/journal.pone.0235357.s004

(DOCX)

S1 Data.

https://doi.org/10.1371/journal.pone.0235357.s005

(XLSX)

References

1. Hristov AN, Oh J, Firkins JL, Dijkstra J, Kebreab E, Waghorn G, et al. Special topics—Mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. J Anim Sci. 2013;91(11):5045–69. pmid:24045497
- View Article
- PubMed/NCBI
- Google Scholar
2. Shetty N, Difford G, Lassen J, Lovendahl P, Buitenhuis AJ. Predicting methane emissions of lactating Danish Holstein cows using Fourier transform mid-infrared spectroscopy of milk. J Dairy Sci. 2017;100(11):9052–60. pmid:28918149
- View Article
- PubMed/NCBI
- Google Scholar
3. Ellis JL, Kebreab E, Odongo NE, McBride BW, Okine EK, France J. Prediction of methane production from dairy and beef cattle. J Dairy Sci. 2007;90(7):3456–66. pmid:17582129
- View Article
- PubMed/NCBI
- Google Scholar
4. Yan T, Porter MG, Mayne CS. Prediction of methane emission from beef cattle using data measured in indirect open-circuit respiration calorimeters. animal. 2009;3(10):1455–62. pmid:22444941
- View Article
- PubMed/NCBI
- Google Scholar
5. Ramin M, Huhtanen P. Development of equations for predicting methane emissions from ruminants. J Dairy Sci. 2013;96(4):2476–93. pmid:23403199
- View Article
- PubMed/NCBI
- Google Scholar
6. Fievez V, Colman E, Castro-Montoya JM, Stefanov I, Vlaeminck B. Milk odd- and branched-chain fatty acids as biomarkers of rumen function—An update. Anim Feed Sci Tech. 2012;172(1):51–65.
- View Article
- Google Scholar
7. Vlaeminck B, Fievez V, Tamminga S, Dewhurst RJ, van Vuuren A, De Brabander D, et al. Milk odd- and branched-chain fatty acids in relation to the rumen fermentation pattern. J Dairy Sci. 2006;89(10):3954–64. pmid:16960070
- View Article
- PubMed/NCBI
- Google Scholar
8. Chilliard Y, Martin C, Rouel J, Doreau M. Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output. J Dairy Sci. 2009;92(10):5199–211. pmid:19762838
- View Article
- PubMed/NCBI
- Google Scholar
9. Mohammed R, McGinn SM, Beauchemin KA. Prediction of enteric methane output from milk fatty acid concentrations and rumen fermentation parameters in dairy cows fed sunflower, flax, or canola seeds. J Dairy Sci. 2011;94(12):6057–68. pmid:22118093
- View Article
- PubMed/NCBI
- Google Scholar
10. van Lingen HJ, Crompton LA, Hendriks WH, Reynolds CK, Dijkstra J. Meta-analysis of relationships between enteric methane yield and milk fatty acid profile in dairy cattle. J Dairy Sci. 2014;97(11):7115–32. pmid:25218750
- View Article
- PubMed/NCBI
- Google Scholar
11. Castro-Montoya JM, Peiren N, Veneman J, De Baets B, De Campeneere S, Fievez V. Predictions of methane emission levels and categories based on milk fatty acid profiles from dairy cows. animal. 2017;11(7):1153–62. pmid:27974080
- View Article
- PubMed/NCBI
- Google Scholar
12. Pinares-Patino CS, Ulyatt MJ, Lassey KR, Barry TN, Holmes CW. Rumen function and digestion parameters associated with differences between sheep in methane emissions when fed chaffed lucerne hay. J Agri Sci. 2003;140(2):205–14.
- View Article
- Google Scholar
13. Cabezas-Garcia EH, Krizsan SJ, Shingfield KJ, Huhtanen P. Between-cow variation in digestion and rumen fermentation variables associated with methane production. J Dairy Sci. 2017;100(6):4409–24. pmid:28390728
- View Article
- PubMed/NCBI
- Google Scholar
14. Mele M, Dal Zotto R, Cassandro M, Conte G, Serra A, Buccioni A, et al. Genetic parameters for conjugated linoleic acid, selected milk fatty acids, and milk fatty acid unsaturation of Italian Holstein-Friesian cows. J Dairy Sci. 2009;92(1):392–400. pmid:19109297
- View Article
- PubMed/NCBI
- Google Scholar
15. van Engelen S, Bovenhuis H, Dijkstra J, van Arendonk JA, Visker MH. Short communication: Genetic study of methane production predicted from milk fat composition in dairy cows. J Dairy Sci. 2015;98(11):8223–6. pmid:26364110
- View Article
- PubMed/NCBI
- Google Scholar
16. Halmemies-Beauchet-Filleau A, Kairenius P, Ahvenjarvi S, Toivonen V, Huhtanen P, Vanhatalo A, et al. Effect of forage conservation method on plasma lipids, mammary lipogenesis, and milk fatty acid composition in lactating cows fed diets containing a 60:40 forage-to-concentrate ratio. J Dairy Sci. 2013;96(8):5267–89. pmid:23769378
- View Article
- PubMed/NCBI
- Google Scholar
17. Halmemies-Beauchet-Filleau A, Kairenius P, Ahvenjarvi S, Crosley LK, Muetzel S, Huhtanen P, et al. Effect of forage conservation method on ruminal lipid metabolism and microbial ecology in lactating cows fed diets containing a 60:40 forage-to-concentrate ratio. J Dairy Sci. 2013;96(4):2428–47. pmid:23375967
- View Article
- PubMed/NCBI
- Google Scholar
18. Halmemies-Beauchet-Filleau A, Vanhatalo A, Toivonen V, Heikkila T, Lee MR, Shingfield KJ. Effect of replacing grass silage with red clover silage on nutrient digestion, nitrogen metabolism, and milk fat composition in lactating cows fed diets containing a 60:40 forage-to-concentrate ratio. J Dairy Sci. 2014;97(6):3761–76. pmid:24679932
- View Article
- PubMed/NCBI
- Google Scholar
19. Halmemies-Beauchet-Filleau A, Vanhatalo A, Toivonen V, Heikkila T, Lee MR, Shingfield KJ. Effect of replacing grass silage with red clover silage on ruminal lipid metabolism in lactating cows fed diets containing a 60:40 forage-to-concentrate ratio. J Dairy Sci. 2013;96(9):5882–900. pmid:23849641
- View Article
- PubMed/NCBI
- Google Scholar
20. Kairenius P, Leskinen H, Toivonen V, Muetzel S, Ahvenjarvi S, Vanhatalo A, et al. Effect of dietary fish oil supplements alone or in combination with sunflower and linseed oil on ruminal lipid metabolism and bacterial populations in lactating cows. J Dairy Sci. 2018;101(4):3021–35. pmid:29428753
- View Article
- PubMed/NCBI
- Google Scholar
21. Shingfield KJ, Ahvenjärvi S, Vanhatalo A, Huhtanen P, editors. Effect of fish oil alone or in combination with linoleic and linolenic rich oils on rumen biohydrogenation and fatty acid uptake by the mammary gland in the lactating dairy cow. Proc of the II Int Congress on Conjugated Linoleic Acid (CCA): from Experimental Models to Human Application; 2007; Italy.
22. Kairenius P, Arola A, Leskinen H, Toivonen V, Ahvenjarvi S, Vanhatalo A, et al. Dietary fish oil supplements depress milk fat yield and alter milk fatty acid composition in lactating cows fed grass silage-based diets. J Dairy Sci. 2015;98(8):5653–71. pmid:26094222
- View Article
- PubMed/NCBI
- Google Scholar
23. Shingfield KJ, Kairenius P, Arola A, Paillard D, Muetzel S, Ahvenjarvi S, et al. Dietary fish oil supplements modify ruminal biohydrogenation, alter the flow of fatty acids at the omasum, and induce changes in the ruminal Butyrivibrio population in lactating cows. J Nutr. 2012;142(8):1437–48. pmid:22739367
- View Article
- PubMed/NCBI
- Google Scholar
24. Leskinen H, Ventto L, Kairenius P, Shingfield KJ, Vilkki J. Temporal changes in milk fatty acid composition during diet-induced milk fat depression in lactating cows. J Dairy Sci. 2019;102(6):5148–60. pmid:30904304
- View Article
- PubMed/NCBI
- Google Scholar
25. Kairenius P, Toivonen V, Ahvenjärvi S, Vanhatalo A, Givens DI, Shingfield KJ, editors. Effects of rapeseed lipids in the diet on ruminal lipid metabolism and milk fatty acid composition in cows fed grass silage based diets. Proc of the XI Int Symposium on Ruminant Physiology, Digestion, Metabolism and Effects of Nutrition on Reproduction and Welfare; 2009; Clermont-Ferrand, France. Wageningen Academic Publ.
26. Ahvenjarvi S, Vanhatalo A, Huhtanen P, Varvikko T. Determination of reticulo-rumen and whole-stomach digestion in lactating cows by omasal canal or duodenal sampling. Br J Nutr. 2000;83(1):67–77. pmid:10703466
- View Article
- PubMed/NCBI
- Google Scholar
27. France J, Siddons RC. Determination of digesta flow by continuous market infusion. Journal of Theoretical Biology. 1986;121(1):105–19.
- View Article
- Google Scholar
28. Shingfield KJ, Jaakkola S, Huhtanen P. Effect of forage conservation method, concentrate level and propylene glycol on diet digestibility, rumen fermentation, blood metabolite concentrations and nutrient utilisation of dairy cows. Anim Feed Sci Tech. 2002;97(1):1–21.
- View Article
- Google Scholar
29. LUKE. Finnish feed tables and feeding recommendations: 2010. http://www.luke.fi/feedtables
30. Wolin MJ. A Theoretical Rumen Fermentation Balance. J Dairy Sci. 1960;43(10):1452–9.
- View Article
- Google Scholar
31. Shingfield KJ, Ahvenjärvi S, Toivonen V, Ärölä A, Nurmela KVV, Huhtanen P, et al. Effect of dietary fish oil on biohydrogenation of fatty acids and milk fatty acid content in cows. Animal Science. 2003;77(1):165–79.
- View Article
- Google Scholar
32. Halmemies-Beauchet-Filleau A, Kokkonen T, Lampi AM, Toivonen V, Shingfield KJ, Vanhatalo A. Effect of plant oils and camelina expeller on milk fatty acid composition in lactating cows fed diets based on red clover silage. J Dairy Sci. 2011;94(9):4413–30. pmid:21854915
- View Article
- PubMed/NCBI
- Google Scholar
33. St-Pierre NR. Invited review: Integrating quantitative findings from multiple studies using mixed model methodology. J Dairy Sci. 2001;84(4):741–55. pmid:11352149
- View Article
- PubMed/NCBI
- Google Scholar
34. Garnsworthy PC, Craigon J, Hernandez-Medrano JH, Saunders N. On-farm methane measurements during milking correlate with total methane production by individual dairy cows. J Dairy Sci. 2012;95(6):3166–80. pmid:22612952
- View Article
- PubMed/NCBI
- Google Scholar
35. Negussie E, de Haas Y, Dehareng F, Dewhurst RJ, Dijkstra J, Gengler N, et al. Invited review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and their potential for use in management and breeding decisions. J Dairy Sci. 2017;100(4):2433–53. pmid:28161178
- View Article
- PubMed/NCBI
- Google Scholar
36. Dijkstra J, van Zijderveld SM, Apajalahti JA, Bannink A, Gerrits WJJ, Newbold JR, et al. Relationships between methane production and milk fatty acid profiles in dairy cattle. Anim Feed Sci Tech. 2011;166–167:590–5.
- View Article
- Google Scholar
37. Johnson KA, Johnson DE. Methane emissions from cattle. J Anim Sci. 1995;73(8):2483–92. pmid:8567486
- View Article
- PubMed/NCBI
- Google Scholar
38. Pinares-Patino CS, Hickey SM, Young EA, Dodds KG, MacLean S, Molano G, et al. Heritability estimates of methane emissions from sheep. Animal. 2013;7 Suppl 2:316–21.
- View Article
- Google Scholar
39. Shingfield KJ, Bonnet M, Scollan ND. Recent developments in altering the fatty acid composition of ruminant-derived foods. Animal. 2013;7 Suppl 1:132–62.
- View Article
- Google Scholar
40. Bauman DE, Harvatine KJ, Lock AL. Nutrigenomics, rumen-derived bioactive fatty acids, and the regulation of milk fat synthesis. Annu Rev Nutr. 2011;31:299–319. pmid:21568706
- View Article
- PubMed/NCBI
- Google Scholar
41. French EA, Bertics SJ, Armentano LE. Rumen and milk odd- and branched-chain fatty acid proportions are minimally influenced by ruminal volatile fatty acid infusions. J Dairy Sci. 2012;95(4):2015–26. pmid:22459847
- View Article
- PubMed/NCBI
- Google Scholar
42. Kaneda T. Iso- and anteiso-fatty acids in bacteria: biosynthesis, function, and taxonomic significance. Microbiol Rev. 1991;55(2):288–302. pmid:1886522
- View Article
- PubMed/NCBI
- Google Scholar
43. Montoya JC, Bhagwat AM, Peiren N, De Campeneere S, De Baets B, Fievez V. Relationships between odd- and branched-chain fatty acid profiles in milk and calculated enteric methane proportion for lactating dairy cattle. Anim Feed Sci Tech. 2011;166–167:596–602.
- View Article
- Google Scholar
44. Rico DE, Chouinard PY, Hassanat F, Benchaar C, Gervais R. Prediction of enteric methane emissions from Holstein dairy cows fed various forage sources. Animal. 2016;10(2):203–11. pmid:26399308
- View Article
- PubMed/NCBI
- Google Scholar
45. Dewhurst RJ, Moorby JM, Vlaeminck B, Fievez V. Apparent recovery of duodenal odd- and branched-chain fatty acids in milk of dairy cows. J Dairy Sci. 2007;90(4):1775–80. pmid:17369218
- View Article
- PubMed/NCBI
- Google Scholar
46. Massart-Leen AM, Roets E, Peeters G, Verbeke R. Propionate for fatty acid synthesis by the mammary gland of the lactating goat. J Dairy Sci. 1983;66(7):1445–54. pmid:6886173
- View Article
- PubMed/NCBI
- Google Scholar
47. Vlaeminck B, Gervais R, Rahman MM, Gadeyne F, Gorniak M, Doreau M, et al. Postruminal synthesis modifies the odd- and branched-chain fatty acid profile from the duodenum to milk. J Dairy Sci. 2015;98(7):4829–40. pmid:25958291
- View Article
- PubMed/NCBI
- Google Scholar
48. Precht D, Molkentin J. Identification and quantitation of cis/trans C16:1 and C17:1 fatty acid positional isomers in German human milk lipids by thin-layer chromatography and gas chromatography/mass spectrometry. Eur J Lipid Sci Tech. 2000;102(2):102–13.
- View Article
- Google Scholar
49. Berthelot V, Bas P, Schmidely P, Duvaux-Ponter C. Effect of dietary propionate on intake patterns and fatty acid composition of adipose tissues in lambs. Small Rumin Res. 2001;40(1):29–39. pmid:11259873
- View Article
- PubMed/NCBI
- Google Scholar
50. Craninx M, Steen A, Van Laar H, Van Nespen T, Martin-Tereso J, De Baets B, et al. Effect of lactation stage on the odd- and branched-chain milk fatty acids of dairy cattle under grazing and indoor conditions. J Dairy Sci. 2008;91(7):2662–77. pmid:18565925
- View Article
- PubMed/NCBI
- Google Scholar
51. Body DR. The lipid composition of adipose tissue. Prog Lipid Res. 1988;27(1):39–60. pmid:3057509
- View Article
- PubMed/NCBI
- Google Scholar
52. Castro-Montoya JM, De Campeneere S, De Baets B, Fievez V. The potential of milk fatty acids as biomarkers for methane emissions in dairy cows: a quantitative multi-study survey of literature data. J Agri Sci. 2016;154(3):515–31.
- View Article
- Google Scholar
53. Stoop WM, van Arendonk JAM, Heck JML, van Valenberg HJF, Bovenhuis H. Genetic Parameters for Major Milk Fatty Acids and Milk Production Traits of Dutch Holstein-Friesians. Journal of Dairy Science. 2008;91(1):385–94. pmid:18096963
- View Article
- PubMed/NCBI
- Google Scholar
54. Bittante G, Cecchinato A, Schiavon S. 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. J Dairy Sci. 2018;101(2):1752–66. pmid:29224867
- View Article
- PubMed/NCBI
- Google Scholar
55. Devries TJ, von Keyserlingk MA. Time of feed delivery affects the feeding and lying patterns of dairy cows. J Dairy Sci. 2005;88(2):625–31. pmid:15653529
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Hristov AN, Oh J, Firkins JL, Dijkstra J, Kebreab E, Waghorn G, et al. Special topics—Mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. J Anim Sci. 2013;91(11):5045–69. pmid:24045497
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Shetty N, Difford G, Lassen J, Lovendahl P, Buitenhuis AJ. Predicting methane emissions of lactating Danish Holstein cows using Fourier transform mid-infrared spectroscopy of milk. J Dairy Sci. 2017;100(11):9052–60. pmid:28918149
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Ellis JL, Kebreab E, Odongo NE, McBride BW, Okine EK, France J. Prediction of methane production from dairy and beef cattle. J Dairy Sci. 2007;90(7):3456–66. pmid:17582129
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Yan T, Porter MG, Mayne CS. Prediction of methane emission from beef cattle using data measured in indirect open-circuit respiration calorimeters. animal. 2009;3(10):1455–62. pmid:22444941
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Ramin M, Huhtanen P. Development of equations for predicting methane emissions from ruminants. J Dairy Sci. 2013;96(4):2476–93. pmid:23403199
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Fievez V, Colman E, Castro-Montoya JM, Stefanov I, Vlaeminck B. Milk odd- and branched-chain fatty acids as biomarkers of rumen function—An update. Anim Feed Sci Tech. 2012;172(1):51–65.
View Article
Google Scholar

[22] View Article

[23] Google Scholar

[ref7] 7. Vlaeminck B, Fievez V, Tamminga S, Dewhurst RJ, van Vuuren A, De Brabander D, et al. Milk odd- and branched-chain fatty acids in relation to the rumen fermentation pattern. J Dairy Sci. 2006;89(10):3954–64. pmid:16960070
View Article
PubMed/NCBI
Google Scholar

[25] View Article

[26] PubMed/NCBI

[27] Google Scholar

[ref8] 8. Chilliard Y, Martin C, Rouel J, Doreau M. Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output. J Dairy Sci. 2009;92(10):5199–211. pmid:19762838
View Article
PubMed/NCBI
Google Scholar

[29] View Article

[30] PubMed/NCBI

[31] Google Scholar

[ref9] 9. Mohammed R, McGinn SM, Beauchemin KA. Prediction of enteric methane output from milk fatty acid concentrations and rumen fermentation parameters in dairy cows fed sunflower, flax, or canola seeds. J Dairy Sci. 2011;94(12):6057–68. pmid:22118093
View Article
PubMed/NCBI
Google Scholar

[33] View Article

[34] PubMed/NCBI

[35] Google Scholar

[ref10] 10. van Lingen HJ, Crompton LA, Hendriks WH, Reynolds CK, Dijkstra J. Meta-analysis of relationships between enteric methane yield and milk fatty acid profile in dairy cattle. J Dairy Sci. 2014;97(11):7115–32. pmid:25218750
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref11] 11. Castro-Montoya JM, Peiren N, Veneman J, De Baets B, De Campeneere S, Fievez V. Predictions of methane emission levels and categories based on milk fatty acid profiles from dairy cows. animal. 2017;11(7):1153–62. pmid:27974080
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref12] 12. Pinares-Patino CS, Ulyatt MJ, Lassey KR, Barry TN, Holmes CW. Rumen function and digestion parameters associated with differences between sheep in methane emissions when fed chaffed lucerne hay. J Agri Sci. 2003;140(2):205–14.
View Article
Google Scholar

[45] View Article

[46] Google Scholar

[ref13] 13. Cabezas-Garcia EH, Krizsan SJ, Shingfield KJ, Huhtanen P. Between-cow variation in digestion and rumen fermentation variables associated with methane production. J Dairy Sci. 2017;100(6):4409–24. pmid:28390728
View Article
PubMed/NCBI
Google Scholar

[48] View Article

[49] PubMed/NCBI

[50] Google Scholar

[ref14] 14. Mele M, Dal Zotto R, Cassandro M, Conte G, Serra A, Buccioni A, et al. Genetic parameters for conjugated linoleic acid, selected milk fatty acids, and milk fatty acid unsaturation of Italian Holstein-Friesian cows. J Dairy Sci. 2009;92(1):392–400. pmid:19109297
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref15] 15. van Engelen S, Bovenhuis H, Dijkstra J, van Arendonk JA, Visker MH. Short communication: Genetic study of methane production predicted from milk fat composition in dairy cows. J Dairy Sci. 2015;98(11):8223–6. pmid:26364110
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref16] 16. Halmemies-Beauchet-Filleau A, Kairenius P, Ahvenjarvi S, Toivonen V, Huhtanen P, Vanhatalo A, et al. Effect of forage conservation method on plasma lipids, mammary lipogenesis, and milk fatty acid composition in lactating cows fed diets containing a 60:40 forage-to-concentrate ratio. J Dairy Sci. 2013;96(8):5267–89. pmid:23769378
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref17] 17. Halmemies-Beauchet-Filleau A, Kairenius P, Ahvenjarvi S, Crosley LK, Muetzel S, Huhtanen P, et al. Effect of forage conservation method on ruminal lipid metabolism and microbial ecology in lactating cows fed diets containing a 60:40 forage-to-concentrate ratio. J Dairy Sci. 2013;96(4):2428–47. pmid:23375967
View Article
PubMed/NCBI
Google Scholar

[64] View Article

[65] PubMed/NCBI

[66] Google Scholar

[ref18] 18. Halmemies-Beauchet-Filleau A, Vanhatalo A, Toivonen V, Heikkila T, Lee MR, Shingfield KJ. Effect of replacing grass silage with red clover silage on nutrient digestion, nitrogen metabolism, and milk fat composition in lactating cows fed diets containing a 60:40 forage-to-concentrate ratio. J Dairy Sci. 2014;97(6):3761–76. pmid:24679932
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref19] 19. Halmemies-Beauchet-Filleau A, Vanhatalo A, Toivonen V, Heikkila T, Lee MR, Shingfield KJ. Effect of replacing grass silage with red clover silage on ruminal lipid metabolism in lactating cows fed diets containing a 60:40 forage-to-concentrate ratio. J Dairy Sci. 2013;96(9):5882–900. pmid:23849641
View Article
PubMed/NCBI
Google Scholar

[72] View Article

[73] PubMed/NCBI

[74] Google Scholar

[ref20] 20. Kairenius P, Leskinen H, Toivonen V, Muetzel S, Ahvenjarvi S, Vanhatalo A, et al. Effect of dietary fish oil supplements alone or in combination with sunflower and linseed oil on ruminal lipid metabolism and bacterial populations in lactating cows. J Dairy Sci. 2018;101(4):3021–35. pmid:29428753
View Article
PubMed/NCBI
Google Scholar

[76] View Article

[77] PubMed/NCBI

[78] Google Scholar

[ref21] 21. Shingfield KJ, Ahvenjärvi S, Vanhatalo A, Huhtanen P, editors. Effect of fish oil alone or in combination with linoleic and linolenic rich oils on rumen biohydrogenation and fatty acid uptake by the mammary gland in the lactating dairy cow. Proc of the II Int Congress on Conjugated Linoleic Acid (CCA): from Experimental Models to Human Application; 2007; Italy.

[ref22] 22. Kairenius P, Arola A, Leskinen H, Toivonen V, Ahvenjarvi S, Vanhatalo A, et al. Dietary fish oil supplements depress milk fat yield and alter milk fatty acid composition in lactating cows fed grass silage-based diets. J Dairy Sci. 2015;98(8):5653–71. pmid:26094222
View Article
PubMed/NCBI
Google Scholar

[81] View Article

[82] PubMed/NCBI

[83] Google Scholar

[ref23] 23. Shingfield KJ, Kairenius P, Arola A, Paillard D, Muetzel S, Ahvenjarvi S, et al. Dietary fish oil supplements modify ruminal biohydrogenation, alter the flow of fatty acids at the omasum, and induce changes in the ruminal Butyrivibrio population in lactating cows. J Nutr. 2012;142(8):1437–48. pmid:22739367
View Article
PubMed/NCBI
Google Scholar

[85] View Article

[86] PubMed/NCBI

[87] Google Scholar

[ref24] 24. Leskinen H, Ventto L, Kairenius P, Shingfield KJ, Vilkki J. Temporal changes in milk fatty acid composition during diet-induced milk fat depression in lactating cows. J Dairy Sci. 2019;102(6):5148–60. pmid:30904304
View Article
PubMed/NCBI
Google Scholar

[89] View Article

[90] PubMed/NCBI

[91] Google Scholar

[ref25] 25. Kairenius P, Toivonen V, Ahvenjärvi S, Vanhatalo A, Givens DI, Shingfield KJ, editors. Effects of rapeseed lipids in the diet on ruminal lipid metabolism and milk fatty acid composition in cows fed grass silage based diets. Proc of the XI Int Symposium on Ruminant Physiology, Digestion, Metabolism and Effects of Nutrition on Reproduction and Welfare; 2009; Clermont-Ferrand, France. Wageningen Academic Publ.

[ref26] 26. Ahvenjarvi S, Vanhatalo A, Huhtanen P, Varvikko T. Determination of reticulo-rumen and whole-stomach digestion in lactating cows by omasal canal or duodenal sampling. Br J Nutr. 2000;83(1):67–77. pmid:10703466
View Article
PubMed/NCBI
Google Scholar

[94] View Article

[95] PubMed/NCBI

[96] Google Scholar

[ref27] 27. France J, Siddons RC. Determination of digesta flow by continuous market infusion. Journal of Theoretical Biology. 1986;121(1):105–19.
View Article
Google Scholar

[98] View Article

[99] Google Scholar

[ref28] 28. Shingfield KJ, Jaakkola S, Huhtanen P. Effect of forage conservation method, concentrate level and propylene glycol on diet digestibility, rumen fermentation, blood metabolite concentrations and nutrient utilisation of dairy cows. Anim Feed Sci Tech. 2002;97(1):1–21.
View Article
Google Scholar

[101] View Article

[102] Google Scholar

[ref29] 29. LUKE. Finnish feed tables and feeding recommendations: 2010. http://www.luke.fi/feedtables

[ref30] 30. Wolin MJ. A Theoretical Rumen Fermentation Balance. J Dairy Sci. 1960;43(10):1452–9.
View Article
Google Scholar

[105] View Article

[106] Google Scholar

[ref31] 31. Shingfield KJ, Ahvenjärvi S, Toivonen V, Ärölä A, Nurmela KVV, Huhtanen P, et al. Effect of dietary fish oil on biohydrogenation of fatty acids and milk fatty acid content in cows. Animal Science. 2003;77(1):165–79.
View Article
Google Scholar

[108] View Article

[109] Google Scholar

[ref32] 32. Halmemies-Beauchet-Filleau A, Kokkonen T, Lampi AM, Toivonen V, Shingfield KJ, Vanhatalo A. Effect of plant oils and camelina expeller on milk fatty acid composition in lactating cows fed diets based on red clover silage. J Dairy Sci. 2011;94(9):4413–30. pmid:21854915
View Article
PubMed/NCBI
Google Scholar

[111] View Article

[112] PubMed/NCBI

[113] Google Scholar

[ref33] 33. St-Pierre NR. Invited review: Integrating quantitative findings from multiple studies using mixed model methodology. J Dairy Sci. 2001;84(4):741–55. pmid:11352149
View Article
PubMed/NCBI
Google Scholar

[115] View Article

[116] PubMed/NCBI

[117] Google Scholar

[ref34] 34. Garnsworthy PC, Craigon J, Hernandez-Medrano JH, Saunders N. On-farm methane measurements during milking correlate with total methane production by individual dairy cows. J Dairy Sci. 2012;95(6):3166–80. pmid:22612952
View Article
PubMed/NCBI
Google Scholar

[119] View Article

[120] PubMed/NCBI

[121] Google Scholar

[ref35] 35. Negussie E, de Haas Y, Dehareng F, Dewhurst RJ, Dijkstra J, Gengler N, et al. Invited review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and their potential for use in management and breeding decisions. J Dairy Sci. 2017;100(4):2433–53. pmid:28161178
View Article
PubMed/NCBI
Google Scholar

[123] View Article

[124] PubMed/NCBI

[125] Google Scholar

[ref36] 36. Dijkstra J, van Zijderveld SM, Apajalahti JA, Bannink A, Gerrits WJJ, Newbold JR, et al. Relationships between methane production and milk fatty acid profiles in dairy cattle. Anim Feed Sci Tech. 2011;166–167:590–5.
View Article
Google Scholar

[127] View Article

[128] Google Scholar

[ref37] 37. Johnson KA, Johnson DE. Methane emissions from cattle. J Anim Sci. 1995;73(8):2483–92. pmid:8567486
View Article
PubMed/NCBI
Google Scholar

[130] View Article

[131] PubMed/NCBI

[132] Google Scholar

[ref38] 38. Pinares-Patino CS, Hickey SM, Young EA, Dodds KG, MacLean S, Molano G, et al. Heritability estimates of methane emissions from sheep. Animal. 2013;7 Suppl 2:316–21.
View Article
Google Scholar

[134] View Article

[135] Google Scholar

[ref39] 39. Shingfield KJ, Bonnet M, Scollan ND. Recent developments in altering the fatty acid composition of ruminant-derived foods. Animal. 2013;7 Suppl 1:132–62.
View Article
Google Scholar

[137] View Article

[138] Google Scholar

[ref40] 40. Bauman DE, Harvatine KJ, Lock AL. Nutrigenomics, rumen-derived bioactive fatty acids, and the regulation of milk fat synthesis. Annu Rev Nutr. 2011;31:299–319. pmid:21568706
View Article
PubMed/NCBI
Google Scholar

[140] View Article

[141] PubMed/NCBI

[142] Google Scholar

[ref41] 41. French EA, Bertics SJ, Armentano LE. Rumen and milk odd- and branched-chain fatty acid proportions are minimally influenced by ruminal volatile fatty acid infusions. J Dairy Sci. 2012;95(4):2015–26. pmid:22459847
View Article
PubMed/NCBI
Google Scholar

[144] View Article

[145] PubMed/NCBI

[146] Google Scholar

[ref42] 42. Kaneda T. Iso- and anteiso-fatty acids in bacteria: biosynthesis, function, and taxonomic significance. Microbiol Rev. 1991;55(2):288–302. pmid:1886522
View Article
PubMed/NCBI
Google Scholar

[148] View Article

[149] PubMed/NCBI

[150] Google Scholar

[ref43] 43. Montoya JC, Bhagwat AM, Peiren N, De Campeneere S, De Baets B, Fievez V. Relationships between odd- and branched-chain fatty acid profiles in milk and calculated enteric methane proportion for lactating dairy cattle. Anim Feed Sci Tech. 2011;166–167:596–602.
View Article
Google Scholar

[152] View Article

[153] Google Scholar

[ref44] 44. Rico DE, Chouinard PY, Hassanat F, Benchaar C, Gervais R. Prediction of enteric methane emissions from Holstein dairy cows fed various forage sources. Animal. 2016;10(2):203–11. pmid:26399308
View Article
PubMed/NCBI
Google Scholar

[155] View Article

[156] PubMed/NCBI

[157] Google Scholar

[ref45] 45. Dewhurst RJ, Moorby JM, Vlaeminck B, Fievez V. Apparent recovery of duodenal odd- and branched-chain fatty acids in milk of dairy cows. J Dairy Sci. 2007;90(4):1775–80. pmid:17369218
View Article
PubMed/NCBI
Google Scholar

[159] View Article

[160] PubMed/NCBI

[161] Google Scholar

[ref46] 46. Massart-Leen AM, Roets E, Peeters G, Verbeke R. Propionate for fatty acid synthesis by the mammary gland of the lactating goat. J Dairy Sci. 1983;66(7):1445–54. pmid:6886173
View Article
PubMed/NCBI
Google Scholar

[163] View Article

[164] PubMed/NCBI

[165] Google Scholar

[ref47] 47. Vlaeminck B, Gervais R, Rahman MM, Gadeyne F, Gorniak M, Doreau M, et al. Postruminal synthesis modifies the odd- and branched-chain fatty acid profile from the duodenum to milk. J Dairy Sci. 2015;98(7):4829–40. pmid:25958291
View Article
PubMed/NCBI
Google Scholar

[167] View Article

[168] PubMed/NCBI

[169] Google Scholar

[ref48] 48. Precht D, Molkentin J. Identification and quantitation of cis/trans C16:1 and C17:1 fatty acid positional isomers in German human milk lipids by thin-layer chromatography and gas chromatography/mass spectrometry. Eur J Lipid Sci Tech. 2000;102(2):102–13.
View Article
Google Scholar

[171] View Article

[172] Google Scholar

[ref49] 49. Berthelot V, Bas P, Schmidely P, Duvaux-Ponter C. Effect of dietary propionate on intake patterns and fatty acid composition of adipose tissues in lambs. Small Rumin Res. 2001;40(1):29–39. pmid:11259873
View Article
PubMed/NCBI
Google Scholar

[174] View Article

[175] PubMed/NCBI

[176] Google Scholar

[ref50] 50. Craninx M, Steen A, Van Laar H, Van Nespen T, Martin-Tereso J, De Baets B, et al. Effect of lactation stage on the odd- and branched-chain milk fatty acids of dairy cattle under grazing and indoor conditions. J Dairy Sci. 2008;91(7):2662–77. pmid:18565925
View Article
PubMed/NCBI
Google Scholar

[178] View Article

[179] PubMed/NCBI

[180] Google Scholar

[ref51] 51. Body DR. The lipid composition of adipose tissue. Prog Lipid Res. 1988;27(1):39–60. pmid:3057509
View Article
PubMed/NCBI
Google Scholar

[182] View Article

[183] PubMed/NCBI

[184] Google Scholar

[ref52] 52. Castro-Montoya JM, De Campeneere S, De Baets B, Fievez V. The potential of milk fatty acids as biomarkers for methane emissions in dairy cows: a quantitative multi-study survey of literature data. J Agri Sci. 2016;154(3):515–31.
View Article
Google Scholar

[186] View Article

[187] Google Scholar

[ref53] 53. Stoop WM, van Arendonk JAM, Heck JML, van Valenberg HJF, Bovenhuis H. Genetic Parameters for Major Milk Fatty Acids and Milk Production Traits of Dutch Holstein-Friesians. Journal of Dairy Science. 2008;91(1):385–94. pmid:18096963
View Article
PubMed/NCBI
Google Scholar

[189] View Article

[190] PubMed/NCBI

[191] Google Scholar

[ref54] 54. Bittante G, Cecchinato A, Schiavon S. 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. J Dairy Sci. 2018;101(2):1752–66. pmid:29224867
View Article
PubMed/NCBI
Google Scholar

[193] View Article

[194] PubMed/NCBI

[195] Google Scholar

[ref55] 55. Devries TJ, von Keyserlingk MA. Time of feed delivery affects the feeding and lying patterns of dairy cows. J Dairy Sci. 2005;88(2):625–31. pmid:15653529
View Article
PubMed/NCBI
Google Scholar

[197] View Article

[198] PubMed/NCBI

[199] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Data

Statistical analysis

Results

Data description

Variance components

Empirical models–simple regressions

Empirical models–multiple regressions

Discussion

Conclusion

Supporting information

S1 Table. Data sources and characteristics of included studies.

S2 Table. Influence of milk fatty acid (FA) on stoichiometry methane (CH4VFA) estimated by univariate mixed model regression analysis (CH4VFA = A + BX1) in dairy cows.

S3 Table. Influence of rumen VFA on the concentration of milk odd- and branched-chain fatty acids (OBCFA) (g 100 g/ FA), estimated by univariate mixed model regression analysis (OBCFA = A + BX1) in dairy cows.

S4 Table. Influence of rumen VFA, and DMI on milk odd- and branched-chain fatty acids (OBCFA), estimated by bivariate mixed model regression analysis (OBCFA = A + BX1 + BX2) in dairy cows.

S1 Data.

References

S2 Table. Influence of milk fatty acid (FA) on stoichiometry methane (CH₄VFA) estimated by univariate mixed model regression analysis (CH₄VFA = A + BX₁) in dairy cows.

S3 Table. Influence of rumen VFA on the concentration of milk odd- and branched-chain fatty acids (OBCFA) (g 100 g/ FA), estimated by univariate mixed model regression analysis (OBCFA = A + BX₁) in dairy cows.

S4 Table. Influence of rumen VFA, and DMI on milk odd- and branched-chain fatty acids (OBCFA), estimated by bivariate mixed model regression analysis (OBCFA = A + BX₁ + BX₂) in dairy cows.