Effects of a combination of feed additives on methane production, diet digestibility, and animal performance in lactating dairy cows

Provimi Holding B.V., Research Centre De Viersprong, Veilingweg 23, NL-5334LD, Velddriel, The Netherlands.
Journal of Dairy Science (Impact Factor: 2.57). 03/2011; 94(3):1445-54. DOI: 10.3168/jds.2010-3635
Source: PubMed


Two experiments were conducted to assess the effects of a mixture of dietary additives on enteric methane production, rumen fermentation, diet digestibility, energy balance, and animal performance in lactating dairy cows. Identical diets were fed in both experiments. The mixture of feed additives investigated contained lauric acid, myristic acid, linseed oil, and calcium fumarate. These additives were included at 0.4, 1.2, 1.5, and 0.7% of dietary dry matter, respectively (treatment ADD). Experimental fat sources were exchanged for a rumen inert source of fat in the control diet (treatment CON) to maintain isolipidic rations. Cows (experiment 1, n=20; experiment 2, n=12) were fed restricted amounts of feed to avoid confounding effects of dry matter intake on methane production. In experiment 1, methane production and energy balance were studied using open-circuit indirect calorimetry. In experiment 2, 10 rumen-fistulated animals were used to measure rumen fermentation characteristics. In both experiments animal performance was monitored. The inclusion of dietary additives decreased methane emissions (g/d) by 10%. Milk yield and milk fat content tended to be lower for ADD in experiment 1. In experiment 2, milk production was not affected by ADD, but milk fat content was lower. Fat- and protein-corrected milk was lower for ADD in both experiments. Milk urea nitrogen content was lowered by ADD in experiment 1 and tended to be lower in experiment 2. Apparent total tract digestibility of fat, but not that of starch or neutral detergent fiber, was higher for ADD. Energy retention did not differ between treatments. The decrease in methane production (g/d) was not evident when methane emission was expressed per kilogram of milk produced. Feeding ADD resulted in increases of C12:0 and C14:0 and the intermediates of linseed oil biohydrogenation in milk in both experiments. In experiment 2, ADD-fed cows tended to have a decreased number of protozoa in rumen fluid when compared with that in control cows. Total volatile fatty acid concentrations were lower for ADD, whereas molar proportions of propionate increased at the expense of acetate and butyrate.

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    • "Although several studies reported changes in MFA along with changes in CH 4 production (e.g. Sauer et al. 1998; Hristov et al. 2009; Van Zijderveld et al. 2011), variations in MFA profiles and CH 4 were not consistent across studies, which makes it difficult to draw conclusions from individual experiments on the relationships between MFA and CH 4 and more generally, on the ability of MFA to predict CH 4 . In the same sense, models aiming at the prediction of CH 4 (Chilliard et al. 2009; Dijkstra et al. 2011; Mohammed et al. 2011 ) have few MFA in common. "
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    ABSTRACT: Relationships between milk fatty acids (MFA) and methane (CH 4 ) emissions from dairy cattle were explored. Data from a total of 12 studies including 39 treatments were gathered in the database. Methane was expressed as daily emissions (g/d), relative to dry matter intake (g/kg), milk production (g/kg milk) and body weight (g/kg). The univariate correlations between MFA and CH 4 were based on absolute means and on relative changes of each treatment compared with its corresponding control. Saturated fatty acids, odd- and branched-chain FA and long-chain poly-unsaturated FA were positively related to CH 4 , while cis - and trans- isomers of mono-unsaturated FA were negatively related to CH 4 . However, most of the coefficients of determination ( R 2 ) of these univariate regressions ranged from 0·2 to 0·7, indicating that individual MFA only explain a limited part of the variation in CH 4 . Significant relationships between MFA and CH 4 varied depending on the unit in which emissions were expressed. Similarly, some MFA seemed more suited to predict relative changes in CH 4 emissions rather than absolute amounts. The present findings contribute to the exploration of the potential of MFA as biomarkers for CH 4 emissions from dairy cattle, although differences between studies in the detail of MFA analysis and hence the number of MFA reported in the current study, complicates this kind of literature survey.
    No preview · Article · Jan 2016 · The Journal of Agricultural Science
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    • "The experimental unit for data measured in the respiration chambers (in particular gaseous exchange, N and energy balance parameters) therefore consisted of a pair of cows. The respiration chambers have been described in detail by Verstegen et al. (1987) and Van Zijderveld et al. (2011b). Cows had free access to drinking water throughout the experiment. "
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    ABSTRACT: The objective of this study was to investigate the effects of starch varying in rate of fermentation and level of inclusion in the diet in exchange for fiber on methane (CH4) production of dairy cows. Forty Holstein-Friesian lactating dairy cows of which 16 were rumen cannulated were grouped in 10 blocks of 4 cows each. Cows received diets consisting of 60% grass silage and 40% concentrate (dry matter basis). Cows within block were randomly assigned to 1 of 4 different diets composed of concentrates that varied in rate of starch fermentation [slowly (S) vs. rapidly (R) rumen fermentable; native vs. gelatinized corn grain] and level of starch (low vs. high; 270 vs. 530 g/kg of concentrate dry matter). Results of rumen in situ incubations confirmed that the fractional rate of degradation of starch was higher for R than S starch. Effective rumen degradability of organic matter was higher for high than low starch and also higher for R than S starch. Increased level of starch, but not starch fermentability, decreased dry matter intake and daily CH4 production. Milk yield (mean 24.0 ± 1.02 kg/d), milk fat content (mean 5.05 ± 0.16%), and milk protein content (mean 3.64 ± 0.05%) did not differ between diets. Methane expressed per kilogram of fat- and protein-corrected milk, per kilogram of dry matter intake, or as a fraction of gross energy intake did not differ between diets. Methane expressed per kilogram of estimated rumen-fermentable organic matter (eRFOM) was higher for S than R starch–based diets (47.4 vs. 42.6 g/kg of eRFOM) and for low than high starch–based diets (46.9 vs. 43.1 g/kg of eRFOM). Apparent total-tract digestibility of neutral detergent fiber and crude protein were not affected by diets, but starch digestibility was higher for diets based on R starch (97.2%) compared with S starch (95.5%). Both total volatile fatty acid concentration (109.2 vs. 97.5 mM) and propionate proportion (16.5 vs. 15.8 mol/100 mol) were higher for R starch– compared with S starch–based diets but unaffected by the level of starch. Total N excretion in feces plus urine and N retained were unaffected by dietary treatments, and similarly energy intake and output of energy in milk expressed per unit of metabolic body weight were not affected by treatments. In conclusion, an increased rate of starch fermentation and increased level of starch in the diet of dairy cattle reduced CH4 produced per unit of eRFOM but did not affect CH4 production per unit of feed dry matter intake or per unit of milk produced.
    Full-text · Article · Nov 2014 · Journal of Dairy Science
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    • "The purpose of this paper is to evaluate the equations used to predict CH 4 emissions from dairy cows in some of the whole farm models currently described in the literature. Materials and methods Databases The databases compiled for this evaluation consists of two subsets of data: (1) a literature derived treatment average database (TRT) (37 data points from seven studies: Tyrrell & Moe, 1972; Moe et al., 1973; Moe & Tyrrell, 1977, 1979a; Holter et al., 1990, 1992; Sauer et al., 1998) and (2) a database of individual cow data (IND) obtained from published and unpublished studies [169 data points from nine studies: Beever et al., 1998; Fredeen et al., 2002a, b, 2003 (unpublished results); Odongo et al., 2007, 2008; Van Knegsel et al., 2007; Van Zijderveld et al., 2008; DE Beever, unpublished results]. Care was taken with selection of unpublished results to ensure measurements and values were of adequate quality. "
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    ABSTRACT: The importance of evaluating greenhouse gas (GHG) emissions from dairy cows within the whole farm setting is being realized as more important than evaluating these emissions in isolation. Current whole farm models aimed at evaluating GHG emissions make use of simple regression equations to predict enteric methane (CH4) production. The objective of the current paper is to evaluate the performance of nine CH4 prediction equations that are currently being used in whole farm GHG models. Data used to evaluate the prediction equations came from a collection of individual (IND) and treatment averaged (TRT) data. Equations were compared based on mean square prediction error (MSPE) and concordance correlation coefficient (CCC) analysis. In general, predictions were poor, with root MSPE (as a percentage of observed mean) values ranging from 20.2 to 52.5 for the IND database and from 24.0 to 38.2 for the TRT database and CCC values ranging from 0.009 to 0.493 for the IND database and from 0.000 to 0.271 for the TRT database. Overall, the equations of Moe & Tyrrell and IPCC Tier II performed best on the IND dataset, and the equations of Moe & Tyrrell and Kirchgeßner et al., performed best on the TRT dataset. Results show that the simple more generalized equations performed worse than those that attempted to represent important aspects of diet composition, but in general significant amounts of bias and deviation of the regression slope from unity existed for all equations. The low prediction accuracy of CH4 equations in whole farm models may introduce substantial error into inventories of GHG emissions and lead to incorrect mitigation recommendations.
    Full-text · Article · Dec 2010 · Global Change Biology
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