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

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 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: R-2 = 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. (c) 2020 Society of Chemical Industry
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Submitted on : Friday, January 8, 2021 - 3:53:53 PM
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Amélie Vanlierde, Frédéric Dehareng, Nicolas Gengler, Eric Froidmont, Sinead Mcparland, et al.. Improving robustness and accuracy of predicted daily methane emissions of dairy cows using milk mid‐infrared spectra. Journal of the Science of Food and Agriculture, Wiley, 2020, ⟨10.1002/jsfa.10969⟩. ⟨hal-03104053⟩



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