Improving robustness and accuracy of predicted daily methane emissions of dairy cows using milk mid-infrared spectra

[en] BACKGROUND: A robust proxy for estimating methane (CH4) emissions of individual dairy cows would be valuable especially for selective breeding. This study aimed to improve the robustness and accuracy of prediction models that estimate daily CH4 emissions from milk Fourier transform mid-infrared (FT-MIR) spectra by (i) increasing the reference dataset and (ii) adjusting for routinely recorded phenotypic information. Prediction equations for CH4 were developed using a combined dataset including daily CH4 measurements (n = 1089; g d−1) collected using the SF6 tracer technique (n = 513) and measurements using respiration chambers (RC, n = 576). Furthermore, in addition to the milk FT-MIR spectra, the variables of milk yield (MY) on the test day, parity (P) and breed (B) of cows were included in the regression analysis as explanatory variables. RESULTS: Models developed based on a combined RC and SF6 dataset predicted the expected pattern in CH4 values (in g d−1) during a lactation cycle, namely an increase during the first weeks after calving followed by a gradual decrease until the end of lactation. The model including MY, P and B information provided the best prediction results (cross-validation statistics: R2 = 0.68 and standard error = 57 g CH4 d−1). CONCLUSIONS: The models developed accounted for more of the observed variability in CH4 emissions than previously developed models and thus were considered more robust. This approach is suitable for large-scale studies (e.g. animal genetic evaluation) where robustness is paramount for accurate predictions across a range of animal conditions.

Disciplines :

Agriculture & agronomy

Author, co-author :

Vanlierde, Amélie

Dehareng, Frédéric ; Université de Liège - ULiège > Gembloux Agro-Bio Tech

Gengler, Nicolas ; Université de Liège - ULiège > Département GxABT > Ingénierie des productions animales et nutrition

Froidmont, Eric

McParland, Sinead

Kreuzer, Michael

Bell, Matthiew

Lund, Peter

Martin, Cecile

Kuhla, Bjorn

Soyeurt, Hélène ; Université de Liège - ULiège > Département GxABT > Modélisation et développement

Language :

English

Title :

Improving robustness and accuracy of predicted daily methane emissions of dairy cows using milk mid-infrared spectra

Publication date :

2021

Journal title :

Journal of the Science of Food and Agriculture

ISSN :

0022-5142

eISSN :

1097-0010

Publisher :

John Wiley & Sons, Hoboken, United States - New Jersey

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 04 January 2021

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