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Denninger T, Schwarm A, Birkinshaw A, Terranova M, Dohme-Meier F, Münger A, Eggerschwiler L, Bapst B, Wegmann S, Clauss M, Kreuzer M. Immediate effect of Acacia mearnsii tannins on methane emissions and milk fatty acid profiles of dairy cows. Anim Feed Sci Technol 2020. [DOI: 10.1016/j.anifeedsci.2019.114388] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Engelke SW, Daş G, Derno M, Tuchscherer A, Wimmers K, Rychlik M, Kienberger H, Berg W, Kuhla B, Metges CC. Methane prediction based on individual or groups of milk fatty acids for dairy cows fed rations with or without linseed. J Dairy Sci 2018; 102:1788-1802. [PMID: 30594371 DOI: 10.3168/jds.2018-14911] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 10/25/2018] [Indexed: 01/04/2023]
Abstract
Milk fatty acids (MFA) are a proxy for the prediction of CH4 emission from cows, and prediction differs with diet. Our objectives were (1) to compare the effect of diets on the relation between MFA profile and measured CH4 production, (2) to predict CH4 production based on 6 data sets differing in the number and type of MFA, and (3) to test whether additional inclusion of energy-corrected milk (ECM) yield or dry matter intake (DMI) as explanatory variables improves predictions. Twenty dairy cows were used. Four diets were used based on corn silage (CS) or grass silage (GS) without (L0) or with linseed (LS) supplementation. Ten cows were fed CS-L0 and CS-LS and the other 10 cows were fed GS-L0 and GS-LS in random order. In feeding wk 5 of each diet, CH4 production (L/d) was measured in respiration chambers for 48 h and milk was analyzed for MFA concentrations by gas chromatography. Specific CH4 prediction equations were obtained for L0-, LS-, GS-, and CS-based diets and for all 4 diets collectively and validated by an internal cross-validation. Models were developed containing either 43 identified MFA or a reduced set of 7 groups of biochemically related MFA plus C16:0 and C18:0. The CS and LS diets reduced CH4 production compared with GS and L0 diets, respectively. Methane yield (L/kg of DMI) reduction by LS was higher with CS than GS diets. The concentrations of C18:1 trans and n-3 MFA differed among GS and CS diets. The LS diets resulted in a higher proportion of unsaturated MFA at the expense of saturated MFA. When using the data set of 43 individual MFA to predict CH4 production (L/d), the cross-validation coefficient of determination (R2CV) ranged from 0.47 to 0.92. When using groups of MFA variables, the R2CV ranged from 0.31 to 0.84. The fit parameters of the latter models were improved by inclusion of ECM or DMI, but not when added to the data set of 43 MFA for all diets pooled. Models based on GS diets always had a lower prediction potential (R2CV = 0.31 to 0.71) compared with data from CS diets (R2CV = 0.56 to 0.92). Models based on LS diets produced lower prediction with data sets with reduced MFA variables (R2CV = 0.62 to 0.68) compared with L0 diets (R2CV = 0.67 to 0.80). The MFA C18:1 cis-9 and C24:0 and the monounsaturated FA occurred most often in models. In conclusion, models with a reduced number of MFA variables and ECM or DMI are suitable for CH4 prediction, and CH4 prediction equations based on diets containing linseed resulted in lower prediction accuracy.
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Affiliation(s)
- Stefanie W Engelke
- Institute of Nutritional Physiology "Oskar Kellner," Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Gürbüz Daş
- Institute of Nutritional Physiology "Oskar Kellner," Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Michael Derno
- Institute of Nutritional Physiology "Oskar Kellner," Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Armin Tuchscherer
- Institute of Genetics and Biometry, Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Klaus Wimmers
- Institute of Genome Biology, Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Michael Rychlik
- Analytical Food Chemistry, Technical University of Munich, Maximus-von-Imhof-Forum, 85354 Freising, Germany
| | - Hermine Kienberger
- Bavarian Center for Biomolecular Mass Spectrometry, Gregor-Mendel-Strasse 4, 85354 Freising, Germany
| | - Werner Berg
- Department of Technology Assessment and Substance Cycles, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam, Germany
| | - Björn Kuhla
- Institute of Nutritional Physiology "Oskar Kellner," Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Cornelia C Metges
- Institute of Nutritional Physiology "Oskar Kellner," Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany; Nutritional Physiology and Animal Nutrition, Faculty of Agriculture and Environmental Sciences, University of Rostock, 18059 Rostock, Germany.
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van Gastelen S, Mollenhorst H, Antunes-Fernandes E, Hettinga K, van Burgsteden G, Dijkstra J, Rademaker J. Predicting enteric methane emission of dairy cows with milk Fourier-transform infrared spectra and gas chromatography–based milk fatty acid profiles. J Dairy Sci 2018; 101:5582-5598. [DOI: 10.3168/jds.2017-13052] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 02/09/2018] [Indexed: 11/19/2022]
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van Gastelen S, Antunes-Fernandes EC, Hettinga KA, Dijkstra J. Short communication: The effect of linseed oil and DGAT1 K232A polymorphism on the methane emission prediction potential of milk fatty acids. J Dairy Sci 2018; 101:5599-5604. [PMID: 29550127 DOI: 10.3168/jds.2017-14131] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Accepted: 02/06/2018] [Indexed: 01/10/2023]
Abstract
Several in vivo CH4 measurement techniques have been developed but are not suitable for precise and accurate large-scale measurements; hence, proxies for CH4 emissions in dairy cattle have been proposed, including the milk fatty acid (MFA) profile. The aim of the present study was to determine whether recently developed MFA-based prediction equations for CH4 emission are applicable to dairy cows with different diacylglycerol o-acyltransferase 1 (DGAT1) K232A polymorphism and fed diets with and without linseed oil. Data from a crossover design experiment were used, encompassing 2 dietary treatments (i.e., a control diet and a linseed oil diet, with a difference in dietary fat content of 22 g/kg of dry matter) and 24 lactating Holstein-Friesian cows (i.e., 12 cows with DGAT1 KK genotype and 12 cows with DGAT1 AA genotype). Enteric CH4 production was measured in climate respiration chambers and the MFA profile was analyzed using gas chromatography. Observed CH4 emissions were compared with CH4 emissions predicted by previously developed MFA-based CH4 prediction equations. The results indicate that different types of diets (i.e., with or without linseed oil), but not the DGAT1 K232A polymorphism, affect the ability of previously derived prediction equations to predict CH4 emission. However, the concordance correlation coefficient was smaller than or equal to 0.30 for both dietary treatments separately, both DGAT1 genotypes separately, and the complete data set. We therefore concluded that previously derived MFA-based CH4 prediction equations can neither accurately nor precisely predict CH4 emissions of dairy cows managed under strategies differing from those under which the original prediction equations were developed.
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Affiliation(s)
- S van Gastelen
- Top Institute Food and Nutrition, PO Box 557, 6700 AN Wageningen, the Netherlands; Animal Nutrition Group, Wageningen University & Research, PO Box 338, 6700 AH Wageningen, the Netherlands.
| | - E C Antunes-Fernandes
- Top Institute Food and Nutrition, PO Box 557, 6700 AN Wageningen, the Netherlands; Food Quality and Design Group, Wageningen University & Research, PO Box 17, 6700 AH Wageningen, The Netherlands
| | - K A Hettinga
- Food Quality and Design Group, Wageningen University & Research, PO Box 17, 6700 AH Wageningen, The Netherlands
| | - J Dijkstra
- Animal Nutrition Group, Wageningen University & Research, PO Box 338, 6700 AH Wageningen, the Netherlands
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van Gastelen S, Antunes-Fernandes EC, Hettinga KA, Dijkstra J. The relationship between milk metabolome and methane emission of Holstein Friesian dairy cows: Metabolic interpretation and prediction potential. J Dairy Sci 2017; 101:2110-2126. [PMID: 29290428 DOI: 10.3168/jds.2017-13334] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 11/09/2017] [Indexed: 01/04/2023]
Abstract
This study aimed to quantify the relationship between CH4 emission and fatty acids, volatile metabolites, and nonvolatile metabolites in milk of dairy cows fed forage-based diets. Data from 6 studies were used, including 27 dietary treatments and 123 individual observations from lactating Holstein-Friesian cows. These dietary treatments covered a large range of forage-based diets, with different qualities and proportions of grass silage and corn silage. Methane emission was measured in climate respiration chambers and expressed as production (g per day), yield (g per kg of dry matter intake; DMI), and intensity (g per kg of fat- and protein-corrected milk; FPCM). Milk samples were analyzed for fatty acids by gas chromatography, for volatile metabolites by gas chromatography-mass spectrometry, and for nonvolatile metabolites by nuclear magnetic resonance. Dry matter intake was 15.9 ± 1.90 kg/d (mean ± SD), FPCM yield was 25.2 ± 4.57 kg/d, CH4 production was 359 ± 51.1 g/d, CH4 yield was 22.6 ± 2.31 g/kg of DMI, and CH4 intensity was 14.5 ± 2.59 g/kg of FPCM. The results show that changes in individual milk metabolite concentrations can be related to the ruminal CH4 production pathways. Several of these relationships were diet driven, whereas some were partly dependent on FPCM yield. Next, prediction models were developed and subsequently evaluated based on root mean square error of prediction (RMSEP), concordance correlation coefficient (CCC) analysis, and random 10-fold cross-validation. The best models with milk fatty acids (in g/100 g of fatty acids; MFA) alone predicted CH4 production, yield, and intensity with a RMSEP of 34 g/d, 2.0 g/kg of DMI, and 1.7 g/kg of FPCM, and with a CCC of 0.67, 0.44, and 0.75, respectively. The CH4 prediction potential of both volatile metabolites alone and nonvolatile metabolites alone was low, regardless of the unit of CH4 emission, as evidenced by the low CCC values (<0.35). The best models combining the 3 types of metabolites as selection variables resulted in the inclusion of only MFA for CH4 production and CH4 yield. For CH4 intensity, MFA, volatile metabolites, and nonvolatile metabolites were included in the prediction model. This resulted in a small improvement in prediction potential (CCC of 0.80; RMSEP of 1.5 g/kg of FPCM) relative to MFA alone. These results indicate that volatile and nonvolatile metabolites in milk contain some information to increase our understanding of enteric CH4 production of dairy cows, but that it is not worthwhile to determine the volatile and nonvolatile metabolites in milk to estimate CH4 emission of dairy cows. We conclude that MFA have moderate potential to predict CH4 emission of dairy cattle fed forage-based diets, and that the models can aid in the effort to understand and mitigate CH4 emissions of dairy cows.
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Affiliation(s)
- S van Gastelen
- Top Institute Food and Nutrition, PO Box 557, 6700 AN Wageningen, the Netherlands; Animal Nutrition Group, Wageningen University & Research, PO Box 338, 6700 AH Wageningen, the Netherlands.
| | - E C Antunes-Fernandes
- Top Institute Food and Nutrition, PO Box 557, 6700 AN Wageningen, the Netherlands; Food Quality and Design Group, Wageningen University & Research, PO Box 17, 6700 AH Wageningen, the Netherlands
| | - K A Hettinga
- Food Quality and Design Group, Wageningen University & Research, PO Box 17, 6700 AH Wageningen, the Netherlands
| | - J Dijkstra
- Animal Nutrition Group, Wageningen University & Research, PO Box 338, 6700 AH Wageningen, the Netherlands
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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 2017; 101:1752-1766. [PMID: 29224867 DOI: 10.3168/jds.2017-13472] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 10/13/2017] [Indexed: 11/19/2022]
Abstract
Ruminants (and milk production) contribute to global climate change through enteric methane emissions (EME), and any attempt to reduce them is complicated by the fact that they are difficult and expensive to measure directly. In the case of dairy cows, a promising indirect method of estimating EME is to use the milk fatty acid profile as a proxy, as a relationship exists between microbial activity in the rumen and the molecules available for milk synthesis in the mammary gland. In the present study, we analyzed the detailed fatty acid profiles (through gas chromatography) of a large number of milk samples from 1,158 Brown Swiss cows reared on 85 farms with the aim of testing in the field 2 equations for estimating EME taken from a published meta-analysis. The average estimated methane yield (CH4 emission per kg of dry matter intake, 21.34 ± 1.60 g/kg) and methane intensity (per kg of corrected milk, 14.17 ± 1.78 g/kg), and the derived methane production (CH4 emissions per day per cow, 357 ± 109 g/d) were similar to those previously published. Using data from model cheese makings from individual cows, we also calculated estimated methane intensity per kilogram of fresh cheese (99.7 ± 16.4 g/kg) and cheese solids (207.5 ± 30.9 g/kg). Dairy system affected all EME estimates. Traditional dairy farms, and modern farms including corn silage in the TMR exhibited greater estimated methane intensities. We found very wide variability in estimated EME traits among different farms within dairy system (0.33 to 0.61 of total variance), suggesting the need to modify the farms' feeding regimens and management practices to mitigate emissions. Among the individual factors, parity order affected all estimated EME traits excepted methane yield, with an increase from first lactation to the following ones. Lactation stage exhibited more favorable estimated EME traits during early lactation, concomitant with the availability of nutrients from body tissue mobilization for mammary synthesis of milk. Our results showed a coherence between the EME traits estimated from the analysis of milk fatty acids and the expectations according to current knowledge. Further research is needed to validate the results obtained in this study in other breeds and populations, to assess the magnitude of the genetic variation and the potential of these phenotypes to be exploited in breeding programs with the aim to mitigate emissions.
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Affiliation(s)
- Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Padova, Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Padova, Italy.
| | - Stefano Schiavon
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Padova, Italy
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Negussie E, de Haas Y, Dehareng F, Dewhurst R, Dijkstra J, Gengler N, Morgavi D, Soyeurt H, van Gastelen S, Yan T, Biscarini F. 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; 100:2433-2453. [DOI: 10.3168/jds.2016-12030] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 12/07/2016] [Indexed: 01/15/2023]
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Relationships between methane emission of Holstein Friesian dairy cows and fatty acids, volatile metabolites and non-volatile metabolites in milk. Animal 2017; 11:1539-1548. [DOI: 10.1017/s1751731117000295] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
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van Gastelen S, Dijkstra J. Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2016; 96:3963-3968. [PMID: 26996655 DOI: 10.1002/jsfa.7718] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 01/25/2016] [Accepted: 03/13/2016] [Indexed: 06/05/2023]
Abstract
Enteric methane (CH4 ) production is among the main targets of greenhouse gas mitigation practices for the dairy industry. A simple, robust and inexpensive measurement technique applicable on a large scale to estimate CH4 emission from dairy cattle would therefore be valuable. Milk fatty acids (MFA) are related to CH4 production because of the common biochemical pathway between CH4 and fatty acids in the rumen. A summary of studies that investigated the predictive power of MFA composition for CH4 emission indicated good potential, with predictive power ranging between 47% and 95%. Until recently, gas chromatography (GC) was the principal method used to determine the MFA profile, but GC is unsuitable for routine analysis. This has led to the application of mid-infrared (MIR) spectroscopy. The major advantages of using MIR spectroscopy to predict CH4 emission include its simplicity and potential practical application at large scale. Disadvantages include the inability to predict important MFA for CH4 prediction, and the moderate predictive power for CH4 emission. It may not be sufficient to predict CH4 emission based on MIR alone. Integration with other factors, like feed intake, nutrient composition of the feed, parity, and lactation stage may improve the prediction of CH4 emission using MIR spectra. © 2016 Society of Chemical Industry.
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Affiliation(s)
- Sanne van Gastelen
- Top Institute Food and Nutrition, P.O. Box 557, 6700 AN Wageningen, The Netherlands
- Animal Nutrition Group, Wageningen University, P.O. Box 338, 6700 AH Wageningen, The Netherlands
| | - Jan Dijkstra
- Animal Nutrition Group, Wageningen University, P.O. Box 338, 6700 AH Wageningen, The Netherlands
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Bannink A, Warner D, Hatew B, Ellis JL, Dijkstra J. Quantifying effects of grassland management on enteric methane emission. ANIMAL PRODUCTION SCIENCE 2016. [DOI: 10.1071/an15594] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Data on the effect of grassland management on the nutritional characteristics of fresh and conserved grass, and on enteric methane (CH4) emission in dairy cattle, are sparse. In the present study, an extant mechanistic model of enteric fermentation was evaluated against observations on the effect of grassland management on CH4 emission in three trials conducted in climate-controlled respiration chambers. Treatments were nitrogen fertilisation rate, stage of maturity of grass and level of feed intake, and mean data of a total of 18 treatments were used (4 grass herbage treatments and 14 grass silage treatments). There was a wide range of observed organic matter (OM) digestibility (from 68% to 84%) and CH4 emission intensity (from 5.6% to 7.3% of gross energy intake; from 27.4 to 36.9 g CH4/kg digested OM; from 19.7 to 24.6 g CH4/kg dry matter) among treatment means. The model predicted crude protein, fibre and OM digestibility with reasonable accuracy (root of mean square prediction errors as % of observed mean, RMSPE, 6.8%, 7.5% and 3.9%, respectively). For grass silages only, the model-predicted CH4 correlated well (Pearson correlation coefficient 0.73) with the observed CH4 (which varied from 5.7% to 7.2% of gross energy intake), after predicted CH4 was corrected for nitrate consumed with grass silage, acting as hydrogen sink in the rumen. After nitrate correction, there was a systematic under-prediction of 18%, which reduced to 9% when correcting the erroneously predicted rumen volatile fatty acid (VFA) profile (RMSPE 15%). Although a small over-prediction of 3% was obtained for the grass herbages, this increased to 14% when correcting VFA profile. The model predictions showed a systematic difference in CH4 emission from grass herbages and grass silages, which was not supported by the observed data. This is possibly related to the very high content of soluble carbohydrates in grass herbage (an extra 170 g/kg dry matter compared with grass silages) and an erroneous prediction of its fate and contribution to CH4 in the rumen. Erroneous prediction of the VFA profile is likely to be due to different types of diets included in the empirical database used to parameterise VFA yield in the model from those evaluated here. Model representations of feed digestion and VFA profile are key elements to predict enteric CH4 accurately, and with further evaluations, the latter aspect should be emphasised in particular.
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