Improving robustness and accuracy of predicted daily methane emissions of dairy cows using milk mid-infrared spectra.
MIR spectra
dairy
methane
milk
phenotype
reference method
Journal
Journal of the science of food and agriculture
ISSN: 1097-0010
Titre abrégé: J Sci Food Agric
Pays: England
ID NLM: 0376334
Informations de publication
Date de publication:
Jun 2021
Jun 2021
Historique:
revised:
10
11
2020
received:
08
04
2019
accepted:
22
11
2020
pubmed:
23
11
2020
medline:
17
7
2021
entrez:
22
11
2020
Statut:
ppublish
Résumé
A robust proxy for estimating methane (CH Models developed based on a combined RC and SF The models developed accounted for more of the observed variability in CH
Sections du résumé
BACKGROUND
BACKGROUND
A robust proxy for estimating methane (CH
RESULTS
RESULTS
Models developed based on a combined RC and SF
CONCLUSIONS
CONCLUSIONS
The models developed accounted for more of the observed variability in CH
Substances chimiques
Methane
OP0UW79H66
Types de publication
Evaluation Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3394-3403Subventions
Organisme : Danish Milk Levy Fund and Aarhus University
Organisme : French National Research Agency (ANR)
Organisme : German Federal Ministry of Food and Agriculture (BMBL)
Organisme : the COST Methagene
Organisme : The OptiMIR
Organisme : GplusE
ID : 613689
Organisme : GreenHouseMilk
ID : 238562
Organisme : European Union's Seventh Framework Programme
Informations de copyright
© 2020 Society of Chemical Industry.
Références
Core Writing Team, in Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, ed. by Pachauri RK and Meyer LA. IPCC, Geneva (2014).
Gerber PJ, Steinfeld H, Henderson B, Mottet A, Opio C, Dijkman J et al. eds, Tackling Climate Change through Livestock: A Global Assessment of Emissions and Mitigation Opportunities. Food and Agriculture Organization of the United Nations (FAO), Rome (2013).
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 100:2433-2453 (2017). https://doi.org/10.3168/jds.2016-12030.
Chilliard Y, Martin C, Rouel J and 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 92:5199-5211 (2009). https://doi.org/10.3168/jds.2009-2375.
van Gastelen S and Dijkstra J, Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy. J Sci Food Agric 96:3963-3968 (2016). https://doi.org/10.1002/jsfa.7718.
Van Lingen HJ, Crompton LA, Hendriks WH, Reynolds CK and Dijkstra J, Meta-analysis of relationships between enteric methane yield and milk fatty acid profile in dairy cattle. J Dairy Sci 97:7115-7132 (2014). https://doi.org/10.3168/jds.2014-8268.
Vanlierde A, Vanrobays M-L, Dehareng F, Froidmont E, Soyeurt H, McParland S et al., Innovative lactation stage dependent prediction of methane emissions from milk mid-infrared spectra. J Dairy Sci 98:5740-5747 (2015). https://doi.org/10.3168/jds.2014-8436.
Vanlierde A, Vanrobays M-L, Gengler N, Dardenne P, Froidmont E, Soyeurt H et al., Milk mid-infrared spectra enable prediction of lactation-stage-dependent methane emissions of dairy cattle within routine population-scale milk recording schemes. Anim Prod Sci 56:258-264 (2016). https://doi.org/10.1071/AN15590.
Vanlierde A, Soyeurt H, Gengler N, Colinet FG, Froidmont E, Kreuzer M et al., Development of an equation for estimating methane emissions of dairy cows from milk Fourier transform mid-infrared spectra by using reference data obtained exclusively from respiration chambers. J Dairy Sci 101:7618-7624 (2018). https://doi.org/10.3168/jds.2018-14472.
Dehareng F, Delfosse C, Froidmont E, Soyeurt H, Martin C, Gengler N et al., Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows. Animal 6:1694-1701 (2012). https://doi.org/10.1017/S1751731112000456.
van Gastelen S, Mollenhorst H, Antunes-Fernandes EC, Hettinga KA, van Burgsteden GG, Dijkstra J et al., Predicting enteric methane emission of dairy cows with milk Fourier-transform infrared spectra and gas chromatography-based milk fatty acid profiles. J Dairy Sci 101:5582-5598 (2018). https://doi.org/10.3168/jds.2017-13052.
Bittante G and Cipolat-Gotet C, Direct and indirect predictions of enteric methane daily production, yield, and intensity per unit of milk and cheese, from fatty acids and milk Fourier-transform infrared spectra. J Dairy Sci 101:7219-7235 (2018). https://doi.org/10.3168/jds.2017-14289.
de Haas Y, Pszczola M, Soyeurt H, Wall E and Lassen J, Invited review. Phenotypes to genetically reduce greenhouse gas emissions in dairying. J Dairy Sci 100:855-870 (2017). https://doi.org/10.3168/jds.2016-11246.
Kandel PB, Vanrobays ML, Vanlierde A, Dehareng F, Froidmont E, Gengler N et al., Genetic parameters of mid-infrared methane predictions and their relationships with milk production traits in Holstein cattle. J Dairy Sci 100:5578-5591 (2017). https://doi.org/10.3168/jds.2016-11954.
Vanrobays M-L, Bastin C, Vandenplas J, Hammami H, Soyeurt H, Vanlierde A et al., Changes throughout lactation in phenotypic and genetic correlations between methane emissions and milk fatty acid contents predicted from milk mid-infrared spectra. J Dairy Sci 99:7247-7260 (2016). https://doi.org/10.3168/jds.2015-10646.
Garnsworthy PC, Craigon J, Hernandez-Medrano JH and Saunders N, Variation among individual dairy cows in methane measurements made on farm during milking. J Dairy Sci 95:3181-3189 (2012). https://doi.org/10.3168/jds.2011-4606.
Davies AMC and Fearn T, Back to basics: calibration statistics. Spectrosc Eur 18:31-32 (2006).
Shetty N, Difford G, Lassen J, Løvendahl P and Buitenhuis AJ, Predicting methane emissions of lactating Danish Holstein cows using Fourier transform mid-infrared spectroscopy of milk. J Dairy Sci 100:9052-9060 (2017). https://doi.org/10.3168/jds.2017-13014.
Grelet C, Fernandez Pierna JA, Dardenne P, Soyeurt H, Vanlierde A, Colinet F et al., Standardization of milk mid-infrared spectrometers for the transfer and use of multiple models. J Dairy Sci 100:7910-7921 (2017). https://doi.org/10.3168/jds.2017-12720.
Soyeurt H, Dehareng F, Gengler N, McParland S, Wall EPBD, Berry DP et al., Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. J Dairy Sci 94:1657-1667 (2011). https://doi.org/10.3168/jds.2010-3408.
Shenk JS and Westerhaus MO, Population definition, sample selection, and calibration procedures for near infrared reflectance spectroscopy. Crop Sci 31:469-474 (1990). https://doi.org/10.2135/cropsci1991.0011183X003100020049x.
Soyeurt H, Bastin C, Colinet FG, Arnould VM-R, Berry DP, Wall E et al., Mid infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis. Animal 6:1830-1838 (2012). https://doi.org/10.1017/S1751731112000791.
Whitfield RG, Gerger ME and Sharp RL, Near-infrared spectrum qualification via Mahalanobis distance determination. Appl Spectrosc 41:1204-1213 (1987).
Despagne F, Massart DL and Chabot P, Development of a robust calibration model for nonlinear in-line process data. Anal Chem 72:1657-1665 (2000).
Bittante G, Cecchinato A and 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 101:1752-1766 (2018). https://doi.org/10.3168/jds.2017-13472.
Mevik BH, Wehrens R and Hovde Liland K, pls: partial least squares and principal component regression. R Package Version 2.7-1 (2019). Available: https://CRAN.R-project.org/package=pls. [May 2019].
Alfons A. cvTools: cross-validation tools for regression models. R Package Version 0.3.2 (2012). Available: https://CRAN.R-project.org/package=cvTools. [May 2019].
Altman DG, Practical Statistics for Medical Research. Chapman & Hall, London (1997).
Niu M, Kebreab E, Hristov AN, Oh J, Arndt C, Bannink A et al., Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database. Glob Chang Biol 24:3369-3389 (2018). https://doi.org/10.1111/gcb.14094.