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Food, fibre and pharmaceuticals from animals
RESEARCH ARTICLE (Open Access)

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 A
+ Author Affiliations
- Author Affiliations

A 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

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.


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