Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-23T07:33:07.377Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  15 December 2016

J. M. Castro-Montoya ,
N. Peiren ,
J. Veneman ,
B. De Baets ,
S. De Campeneere  and
V. Fievez
J. M. Castro-Montoya
Affiliation:
Institute for Agricultural and Fisheries Research, Animal Sciences Unit, Scheldeweg 68, Melle 9090, Belgium Laboratory for Animal Nutrition and Animal Product Quality, Ghent University, Proefhoevestraat 10, Melle 9090, Belgium
N. Peiren
Affiliation:
Institute for Agricultural and Fisheries Research, Animal Sciences Unit, Scheldeweg 68, Melle 9090, Belgium
J. Veneman
Affiliation:
Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3DA, UK Cargill Innovation Center, Veilingweg 23, 5334 LD, Velddriel, The Netherlands
B. De Baets
Affiliation:
Department of Mathematical Modelling, Statistics and Bioinformatics, KERMIT, Ghent University, Coupure links 653, Ghent 9000, Belgium
S. De Campeneere
Affiliation:
Institute for Agricultural and Fisheries Research, Animal Sciences Unit, Scheldeweg 68, Melle 9090, Belgium
V. Fievez*
Affiliation:
Laboratory for Animal Nutrition and Animal Product Quality, Ghent University, Proefhoevestraat 10, Melle 9090, Belgium
*
†E-mail: [email protected]
Get access

Abstract

Milk fatty acid (MFA) have already been used to model methane (CH4) emissions from dairy cows. However, the data sets used to develop these models covered limited variation in dietary conditions, reducing the robustness of the predictions. In this study, a data set containing 140 observations from nine experiments (41 Holstein cows) was used to develop models predicting CH4 expressed as g/day, g/kg dry matter intake (DMI) and g/kg milk. The data set was divided into a training (n=112) and a test data set (n=28) for model development and validation, respectively. A generalized linear mixed model was fitted to the data using the marginal R2(m) and the Akaike information criterion to evaluate the models. The coefficient of determination of validation (R2(v)) for different models developed ranged between 0.18 and 0.41. Form the intake-related parameters, only inclusion of total DMI improved the prediction (R2(v)=0.58). In addition, in an attempt to further explore the relationships between MFA and CH4 emissions, the data set was split into three categories according to CH4 emissions: LOW (lowest 25% CH4 emissions); HIGH (highest 25% CH4 emissions); and MEDIUM (50% remaining observations). An ANOVA revealed that concentrations of several MFA differed for observations in HIGH compared with observations in LOW. Furthermore, the Gini coefficient was used to describe the MFA distribution for groups of MFA in each CH4 emission category. The relative distribution of the MFA, particularly of the odd- and branched-chain fatty acids and mono-unsaturated fatty acids of observations in category HIGH differed from those in the other categories. Finally, in an attempt to validate the potential of MFA to identify cases of high or low emissions, the observations were re-classified into HIGH, MEDIUM and LOW according to the proportion of each individual MFA. The proportion of observations correctly classified were recorded. This was done for each individual MFA and for the calculated Gini coefficients, finding that a maximum of 67% of observations were correctly classified as HIGH CH4 (trans-12 C18:1) and a maximum of 58% of observations correctly classified as LOW CH4 (cis-9 C17:1). Gini coefficients did not improve this classification. These results suggest that MFA are not yet reliable predictors of specific amounts of CH4 emitted by a cow, while holding a modest potential to differentiate cases of high or low emissions.

Keywords

methanemilk fatty acidsprediction modelhigh emissionslow emissions
Type
Research Article
Information
animal , Volume 11 , Issue 7 , July 2017 , pp. 1153 - 1162
Copyright
© The Animal Consortium 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

a

Present address: Department of Animal Nutrition and Rangeland Management in the Tropics and Subtropics, Institute of Agricultural Sciences in the Tropics, Hohenheim University, Fruwirthstr. 31, Institutsgebäude, 112, 70599 Stuttgart, Germany.

References

Bates, D, Maechler, and Bolker, B 2011. lme4: linear mixed models. R Package, version 0.999375-42. http://CRAN.R-project.org/package=lme4.Google Scholar
Castro-Montoya, J, De Campeneere, S, De Baets, B and Fievez, V 2016. The potential of milk fatty acids as biomarkers for methane emissions in dairy cows: a quantitative multi-study survey of literature data. Journal of Agricultural Science 154, 515–531.Google Scholar
Castro-Montoya, J, Peiren, N, Cone, JW, Zweifel, B, Fievez, V and De Campeneere, S 2015. In vivo and in vitro effects of a blend of essential oils on rumen methane mitigation. Livestock Science 180, 134–142.Google Scholar
Castro-Montoya, JM, Bhagwat, A, Peiren, N, De Campeneere, S, De Baets, B and Fievez, V 2011. Relationships between odd- and branched-chain fatty acid profiles in milk and calculated enteric methane proportion for lactating dairy cattle. Animal Feed Science and Technology, Special Issue: Greenhouse Gases in Animal Agriculture – Finding a Balance between Food and Emissions 166–167, 596–602.Google Scholar
Chilliard, Y, Martin, C, Rouel, J and Doreau, M 2009. Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output. Journal of Dairy Science 92, 5199–5211.CrossRefGoogle ScholarPubMed
Chouinard, PY, Girard, V and Brisson, GJ 1997. Performance and profiles of milk fatty acids of cows fed full fat, heat-treated soybeans using various processing methods. Journal of Dairy Science 80, 334–342.Google Scholar
Colman, E, Khafipour, E, Vlaeminck, B, De Baets, B, Plaizier, JC and Fievez, V 2013. Grain-based versus alfalfa-based subacute ruminal acidosis induction experiments: similarities and differences between changes in milk fatty acids. Journal of Dairy Science 96, 4100–4111.Google Scholar
De Campeneere, S and Peiren, N 2012. ILVO’s ruminant respiration facility, Melle, Belgium, Chapter 3. In Technical manual on respiration chamber design (ed. C Pinares and G Waghorn), pp. 43–57. Ministry of Agriculture and Forestry, Wellington, New Zealand.Google Scholar
De Haas, Y, Windig, JJ, Calus, MPL, Dijkstra, J, De Haan, M, Bannink, A and Veerkamp, RF 2011. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. Journal of Dairy Science 94, 6122–6134.Google Scholar
Dijkstra, J, Van Zijderveld, S, Apajalahti, J, Bannink, A, Gerrits, W, Newbold, J, Perdok, H and Berends, H 2011. Relationships between methane production and milk fatty acid profiles in dairy cattle. Animal Feed Science and Technology 166, 590–595.CrossRefGoogle Scholar
Eskildsen, CE, Rasmussen, MA, Engelsen, SB, Larsen, LB, Poulsen, NA and Skov, T 2014. Quantification of individual fatty acids in bovine milk by infrared spectroscopy and chemometrics: understanding predictions of highly collinear reference variables. Journal of Dairy Science 97, 7940–7951.CrossRefGoogle ScholarPubMed
FAOSTAT 2015. Food and Agriculture Organization of the United Nations. Statistical database. FAO, Rome, Italy. Retrieved March 30, 2016, from http://faostat3.fao.org/home/E.Google Scholar
Fulco, AJ 1983. Fatty acid metabolism in bacteria. Progress in Lipid Research 22, 133–160.CrossRefGoogle ScholarPubMed
Gastwirth, JL 1972. The estimation of the Lorenz curve and Gini index. The Review of Economics and Statistics 54, 306–316.Google Scholar
Hegarty, RS, Goopy, JP, Herd, RM and McCorkell, B 2007. Cattle selected for lower residual feed intake have reduced daily methane production. Journal of Animal Sciences 85, 1479–1486.CrossRefGoogle ScholarPubMed
Johnson, P 2014. Extension of Nakagawa and Schielzeth’s R 2 GLMM to random slopes models. Methods in Ecology and Evolution 5, 944–946.CrossRefGoogle ScholarPubMed
Knapp, JR, Laur, GL, Vadas, PA, Weiss, WP and Tricarico, JM 2014. Invited review: enteric methane in dairy cattle production: quantifying the opportunities and impact of reducing emissions. Journal of Dairy Science 97, 3231–3261.Google Scholar
Marzorati, M, Wittebolle, L, Boon, N, Daffonchio, D and Verstraete, W 2008. How to get more out of molecular fingerprints: practical tools for microbial ecology. Environmental Microbiology 10, 1571–1581.Google Scholar
Mertens, B, Boon, N and Verstraete, W 2005. Stereospecific effect of hexachlorocyclohexane on activity and structure of soil methanotrophic communities. Environmental Microbiology 7, 660–669.Google Scholar
Mohammed, R, McGinn, SM and Beauchemin, KA 2011. Prediction of enteric methane output from milk fatty acid concentrations and rumen fermentation parameters in dairy cows fed sunflower, flax, or canola seeds. Journal of Dairy Science 94, 6057–6068.Google Scholar
Morgan, J 1962. The anatomy of income distribution. The Review of Economics and Statistics 44, 270–282.CrossRefGoogle Scholar
Nakagawa, S and Schielzeth, H 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4, 133–142.Google Scholar
Pinheiro, JC and Bates, DM (eds) 2000. Linear mixed-effects models: basic concepts and examples. In Mixed-effects models in S and S-PLUS, pp. 3–56. Springer, New York, New York, USA.Google Scholar
Rico, DE, Chouinard, PY, Hassanat, F, Benchaar, C and Gervais, R 2016. Prediction of enteric methane emissions from Holstein dairy cows fed various forage sources. Animal 10, 203–211.CrossRefGoogle ScholarPubMed
Soyeurt, H, Dehareng, F, Gengler, N, McParland, S, Wall, E, Berry, DP, Coffey, M and Dardenne, P 2011. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. Journal of Dairy Science 94, 1657–1667.Google Scholar
Stefanov, I, Vlaeminck, B and Fievez, V 2010. A novel procedure for routine milk fat extraction based on dichloromethane. Journal of Food Composition and Analysis 23, 852–855.Google Scholar
Storm, I, Hellwing, A, Nielsen, N and Madsen, J 2012. Methods for measuring and estimating methane emission from ruminants. Animals 2, 160–183.Google Scholar
Van Lingen, HJ, Crompton, LA, Hendriks, WH, Reynolds, CK and Dijkstra, J 2014. Meta-analysis of relationships between enteric methane yield and milk fatty acid profile in dairy cattle. Journal of Dairy Science 97, 7115–7132.Google Scholar
Veneman, JB, Muetzel, S, Hart, KJ, Faulkner, CL, Moorby, JM, Perdok, HB and Newbold, CJ 2015. Does dietary mitigation of enteric methane production affect rumen function and animal productivity in dairy cows? PLoS ONE 10, e0140282.Google Scholar
Vlaeminck, B, Gervais, R, Rahman, MM, Gadeyne, F, Gorniak, M, Doreau, M and Fievez, V 2015. Postruminal synthesis modifies the odd-and branched-chain fatty acid profile from the duodenum to milk. Journal of Dairy Science 98, 4829–4840.Google Scholar
Supplementary material: File

Castro-Montoya supplementary material

Tables S1-S4

Download Castro-Montoya supplementary material(File)
File 29 KB