CH4 estimated from milk MIR spectra: model on data from 7 countries and 2 measurement techniques


  • Vanlierde, A. , Dehareng, F. , Gengler, N. , Froidmont, E. , Kreuzer, M. , Grandl, F. , Khula, B. , Lund, P. , Olijhoek, D. . , Eugene, M. , Martin, C. , Bell, M. , Mcparland, S. & Soyeurt, H. (2018). CH4 estimated from milk MIR spectra: model on data from 7 countries and 2 measurement techniques. Proceedings in: EAAP, Dubrovnik (Croatia), August 2018,
Type Conference Proceedings
Year of conference 2018
Title CH4 estimated from milk MIR spectra: model on data from 7 countries and 2 measurement techniques
Conference name EAAP
Conference location Dubrovnik (Croatia)
conference Date August 2018
Abstract Availability of a robust proxy to estimate individual daily methane (CH4) emissions from dairy cows would be valuable especially for large scale studies, for instance with genetic purpose. Milk mid infrared (MIR) spectrum present potential to meet this aim as it can be obtained routinely at reasonable cost through milk recording process. Development of prediction equation requires as much variability as possible in the calibration reference data set to improve the accuracy and ensure the robustness of the model. This last point is particularly challenging regarding the methane prediction equation from milk MIR spectra as CH4 is not a direct milk component but an indirect phenotype linked to milk composition through ruminal fermentations which theoretically influence both. To increase the variability of the calibration set, two datasets including CH4 measurements and corresponding milk MIR spectra have been merged: the first contains 532 data from 156 cows of Ireland and Belgium with CH4 measurements obtained with SF6 tracer technique; the second reach 584 data from 147 cows of Switzerland, United Kingdom, France, Denmark and Germany. In addition of the calibration using the raw reference values, a second calibration was performed with a reduction of 8% to CH4 values from chambers evaluate the need to correct the potential method bias in accordance with literature. A 5-groups cross-validation was performed to test the robustness of the models. The new equations showed a R² and a standard error of cross-validation of 0.63 and 62 g/day respectively for the calibration on raw values and 0.65 and 59 g/day when respiration chamber values are adjusted. The slight improvement due to adjustment of chamber measurement does not permit to conclude that this correction is needed. However the new equation includes more variability (cows, diets and country specific information) and statistics confirming its potential as proxy especially for genetic evaluation.
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Authors Vanlierde, A., Dehareng, F., Gengler, N., Froidmont, E., Kreuzer, M., Grandl, F., Khula, B., Lund, P., Olijhoek, D. ., Eugene, M., Martin, C., Bell, M., Mcparland, S., Soyeurt, H.