Milk mid-infrared spectra enable prediction of lactation-stage-dependent methane emissions of dairy cattle within routine population-scale milk recording schemes
Amélie Vanlierde A , Marie-Laure Vanrobays B G , Nicolas Gengler B , Pierre Dardenne A , Eric Froidmont C , Hélène Soyeurt B , Sinead McParland D , Eva Lewis D , Matthew H. Deighton D E , Michaël Mathot F and Frédéric Dehareng AA Walloon Agricultural Research Centre, Valorisation of Agricultural Products Department, Chaussée de Namur, 24, B-5030 Gembloux, Belgium.
B University of Liege – Gembloux Agro-Bio Tech, Agriculture, Bio-engineering and Chemistry Department, Passage des Déportés, 2, B-5030 Gembloux, Belgium.
C Walloon Agricultural Research Centre, Production and Sectors Department, Rue de Liroux, 8, B-5030 Gembloux, Belgium.
D Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland.
E Agriculture Research Division, Department of Economic Development, Jobs, Transport and Resources, Ellinbank Centre, Ellinbank, Vic. 3821, Australia.
F Walloon Agricultural Research Centre, Department of Agriculture and Natural Environment, Rue de Serpont, 100, B-5030 Libramont, Belgium.
G Corresponding author. Email: mlvanrobays@ulg.ac.be
Animal Production Science 56(3) 258-264 https://doi.org/10.1071/AN15590
Submitted: 15 September 2015 Accepted: 24 November 2015 Published: 9 February 2016
Journal Compilation © CSIRO Publishing 2016 Open Access CC BY-NC-ND
Abstract
Mitigating the proportion of energy intake lost as methane could improve the sustainability and profitability of dairy production. As widespread measurement of methane emissions is precluded by current in vivo methods, the development of an easily measured proxy is desirable. An equation has been developed to predict methane from the mid-infrared (MIR) spectra of milk within routine milk-recording programs. The main goals of this study were to improve the prediction equation for methane emissions from milk MIR spectra and to illustrate its already available usefulness as a high throughput phenotypic screening tool. A total of 532 methane measurements considered as reference data (430 ± 129 g of methane/day) linked with milk MIR spectra were obtained from 165 cows using the SF6 technique. A first derivative was applied to the MIR spectra. Constant (P0), linear (P1) and quadratic (P2) modified Legendre polynomials were computed from each cows stage of lactation (days in milk), at the day of SF6 methane measurement. The calibration model was developed using a modified partial least-squares regression on first derivative MIR data points × P0, first derivative MIR data points × P1, and first derivative MIR data points × P2 as variables. The MIR-predicted methane emissions (g/day) showed a calibration coefficient of determination of 0.74, a cross-validation coefficient of determination of 0.70 and a standard error of calibration of 66 g/day. When applied to milk MIR spectra recorded in the Walloon Region of Belgium (≈2 000 000 records), this equation was useful to study lactational, annual, seasonal, and regional methane emissions. We conclude that milk MIR spectra has potential to be used to conduct high throughput screening of lactating dairy cattle for methane emissions. The data generated enable monitoring of methane emissions and production characteristics across and within herds. Milk MIR spectra could now be used for widespread screening of dairy herds in order to develop management and genetic selection tools to reduce methane emissions.
Additional keywords: breeding, management, methane prediction.
References
Bastin C, Gengler N, Soyeurt H (2011) Phenotypic and genetic variability of production traits and milk fatty acid contents across days in milk for Walloon Holstein first-parity cows. Journal of Dairy Science 94, 4152–4163.| Phenotypic and genetic variability of production traits and milk fatty acid contents across days in milk for Walloon Holstein first-parity cows.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXpsVylu7w%3D&md5=7e8f1b8174b99a2c18817553923efb8cCAS | 21787950PubMed |
Beauchemin KA, Kreuzer M, O’Mara F, McAllister TA (2008) Nutritional management for enteric methane abatement: A review. Australian Journal of Experimental Agriculture 48, 21–27.
| Nutritional management for enteric methane abatement: A review.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXovVGn&md5=23b38df440406cdf954ea8c1fc39bac0CAS |
Dehareng F, Delfosse C, Froidmont E, Soyeurt H, Martin C, Gengler N, Vanlierde A, Dardenne P (2012) Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows. Animal 6, 1694–1701.
| Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38Xht1Gis7zP&md5=4aa34f19d46bf8fcba0ccfb516a8a24eCAS | 23031566PubMed |
Friedrichs P, Bastin C, Dehareng F, Wickham B, Massart X (2015) Final OptiMIR Scientific and Expert Meeting: from milk analysis to advisory tools (Palais des Congrès, Namur, Belgium, 16–17 April 2015). Biotechnologie, Agronomie, Société et Environnement 19, 97–124. http://www.pressesagro.be/base/index.php/base/article/view/1152
Garnsworthy PC, Craigon J, Hernandez-Medrano JH, Saunders N (2012) Variation among individual dairy cows in methane measurements made on farm during milking. Journal of Dairy Science 95, 3181–3189.
| Variation among individual dairy cows in methane measurements made on farm during milking.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38Xntlyksbk%3D&md5=6c6728c6d63911fc60254356b4774721CAS | 22612953PubMed |
Gengler N, Tijani A, Wiggans GR, Misztal I (1999) Estimation of (Co)variance function coefficients for test day yield with a expectation-maximization restricted maximum likelihood algorithm. Journal of Dairy Science 82, 1849.e1–1849.e23.
| Estimation of (Co)variance function coefficients for test day yield with a expectation-maximization restricted maximum likelihood algorithm.Crossref | GoogleScholarGoogle Scholar |
Gerber PJ, Steinfeld H, Henderson B, Mottet A, Opio C, Dijkman J, Falcucci A, Tempio G (2013) ‘Tackling climate change through livestock: a global assessment of emissions and mitigation opportunities.’ (Food and Agriculture Organization of the United Nations (FAO): Rome, Italy)
Hammami H, Vandenplas J, Vanrobays M-L, Rekik B, Bastin C, Gengler N (2015) Genetic analysis of heat stress effects on yield traits, udder health, and fatty acids of Walloon Holstein cows. Journal of Dairy Science 98, 4956–4968.
| Genetic analysis of heat stress effects on yield traits, udder health, and fatty acids of Walloon Holstein cows.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXotVWhu70%3D&md5=720944730ec1ea0175ec5fc416a1b284CAS | 25958288PubMed |
Hegarty RS, Goopy JP, Herd RM, McCorkell B (2007) Cattle selected for lower residual feed intake have reduced daily methane production. Journal of Animal Science 85, 1479–1486.
| Cattle selected for lower residual feed intake have reduced daily methane production.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXls1ant7c%3D&md5=8004943b5c2eb95bd7a3c31248e24898CAS | 17296777PubMed |
Hristov A, Oh J, Giallongo F, Frederick T, Harper M, Weeks H, Branco A, Moate P, Deighton M, Williams S, Kindermann M, Duval S (2015) An inhibitor persistantly decreased enteric methane emission from dairy cows with no negative effect on milk production. Proceedings of the National Academy of Sciences of the United States of America 112, 10663–10668.
| An inhibitor persistantly decreased enteric methane emission from dairy cows with no negative effect on milk production.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXht1GrsL3P&md5=1c3d9175995aea8695a264f3f1772926CAS | 26229078PubMed |
Johnson KA, Johnson DE (1995) Methane emissions from cattle. Journal of Animal Science 73, 2483–2492.
| Methane emissions from cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK2MXnsVCntb8%3D&md5=4357890dcfe011b84b6b72819b5c1810CAS | 8567486PubMed |
O’Neill BF, Deighton MH, O’Loughlin BM, Mulligan FJ, Boland TM, O’Donovan M, Lewis E (2011) Effects of a perennial ryegrass diet or total mixed ration diet offered to spring-calving Holstein-Friesian dairy cows on methane emissions, dry matter intake, and milk production. Journal of Dairy Science 94, 1941–1951.
| Effects of a perennial ryegrass diet or total mixed ration diet offered to spring-calving Holstein-Friesian dairy cows on methane emissions, dry matter intake, and milk production.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXnvFChtbg%3D&md5=a2f73a1b3b8cbc422ca6d51cf22dcbcdCAS | 21426985PubMed |
Pickering NK, Oddy VH, Basarab J, Cammack K, Hayes B, Hegarty RS, Lassen J, McEwan JC, Miller S, Pinares-Patiño CS, de Haas Y (2015) Animal board invited review: genetic possibilities to reduce enteric methane emissions from ruminants. Animal 9, 1431–1440.
| Animal board invited review: genetic possibilities to reduce enteric methane emissions from ruminants.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXhtlOnurnN&md5=e528453b2c769598a2e2d1ca33f314efCAS | 26055577PubMed |
Robertson L, Waghorn G (2002) Dairy industry perspectives of methane emissions and production from cattle fed pasture or total mixed rations in New Zealand. In ‘Proceedings of the New Zealand Society of Animal Production’. Volume 62. pp. 213–218. (New Zealand Society of Animal Production: Palmerston North, New Zealand)
Shenk JS, Westerhaus MO (1991) Population definition, sample selection, and calibration procedures for near infrared reflectance spectroscopy. Crop Science 31, 469–474.
| Population definition, sample selection, and calibration procedures for near infrared reflectance spectroscopy.Crossref | GoogleScholarGoogle Scholar |
Soyeurt H, Bastin C, Colinet FG, Arnould VM-R, Berry DP, Wall E, Dehareng F, Nguyen HN, Dardenne P, Schefers J, Vandenplas J, Weigel K, Coffey M, Théron L, Detilleux J, Reding E, Gengler N, McParland S (2012) Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis. Animal 6, 1830–1838.
| Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhsVKltL3F&md5=f94c3af49fc3d0ec72f5c743fa0d4ff0CAS | 22717388PubMed |
Vanlierde A, Vanrobays M-L, Dehareng F, Froidmont E, Soyeurt H, McParland S, Lewis E, Deighton MH, Grandl F, Kreuzer M, Gredler B, Dardenne P, Gengler N (2015) Hot Topic: Innovative lactation stage dependent prediction of methane emissions from milk mid-infrared spectra. Journal of Dairy Science
| Hot Topic: Innovative lactation stage dependent prediction of methane emissions from milk mid-infrared spectra.Crossref | GoogleScholarGoogle Scholar | 26026761PubMed |
Zom RLG, André G, van Vuuren AM (2012) Development of a model for the prediction of feed intake by dairy cows: 1. Prediction of feed intake. Livestock Science 143, 43–57.
| Development of a model for the prediction of feed intake by dairy cows: 1. Prediction of feed intake.Crossref | GoogleScholarGoogle Scholar |
