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Improving robustness and accuracy of predicted daily methane emissions of dairy cows using milk midāinfrared spectra
Vanlierde, AmƩlie Dehareng, FrƩdƩric Gengler, Nicolas Froidmont, Eric McParland, Sinead Kreuzer, Michael Bell, Matthew Lund, Peter Martin, CƩcile Kuhla, Bjƶrn
Vanlierde, AmƩlie
Dehareng, FrƩdƩric
Gengler, Nicolas
Froidmont, Eric
McParland, Sinead
Kreuzer, Michael
Bell, Matthew
Lund, Peter
Martin, CĆ©cile
Kuhla, Bjƶrn
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2020-11-22
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Vanlierde, A., Dehareng, F., Gengler, N., Froidmont, E., McParland, S., Kreuzer, M., Bell, M., Lund, P., Martin, C., Kuhla, B. and Soyeurt, H. (2020), Improving robustness and accuracy of predicted daily methane emissions of dairy cows using milk midāinfrared spectra. J Sci Food Agric. https://doi.org/10.1002/jsfa.10969
Abstract
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 midāinfrared (FTāMIR) 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; gādā1) collected using the SF6 tracer technique (n = 513) and measurements using respiration chambers (RC, n = 576). Furthermore, in addition to the milk FTāMIR 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 gādā1) 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 (crossāvalidation statistics: R2 = 0.68 and standard error = 57āg CH4 dā1).
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 largeāscale studies (e.g. animal genetic evaluation) where robustness is paramount for accurate predictions across a range of animal conditions. Ā© 2020 Society of Chemical Industry