STSM: Prediction of methane emissions from milk MIR spectra

COST action:	FA 1302 - METHAGENE
Reference code:	ECOST-STSM-FA1302-131215-070555
STSM Applicant:	Florian Grandl
Home institution:	Qualitas AG, Switzerland
Host institutions:	Walloon Agricultural Research Centre - Valorisation of Agricultural Products and Université de Liège – Gembloux Agro-Bio Tech; Belgium
STSM period:	13 to 18 December 2015

Blog post after STSM
My name is Florian Grandl and I work at Qualitas AG in Switzerland. Qualitas AG is the genetic evaluation centre of the Swiss dairy cattle breeding organisations and is involved in research and development in the field of functional and novel traits such as methane emission and feed efficiency in dairy cattle. We have established collaborations with CRA-W and ULg GxABT for assessing milk MIR spectral data as an indicator trait for methane emissions.
During my one-week stay in Belgium, we analysed a spectral data set from cows of which methane measurements were available and a data set of spectral data from Swiss routine milk recording. Methane was predicted with the existing prediction equation developed at ULg GxABT/CRA-W. The primary goal of the STSM was to train the work with spectral data and to collect information on how to establish the collaboration on a routine base.
For the first data set, the methane measurement method was GreenFeed, and the methane data were aggregated to a daily average for each cow from all measurements in the week of the milk recording. The correlation between these aggregated measurements and the predicted methane from the milk MIR spectra was low. Further analyses are needed to improve the use of GreenFeed data to be helpful for further developing the MIR spectra prediction equation for methane. The second data set analysed consisted of spectral data from three months of routine milk recording in Swiss Holstein cows. The results of predicted methane from these cows looked very promising and showed patterns as expected. Only a smaller data set than planned could be analysed, as the data preparation turned out to be more time consuming than expected. More in-depth analyses will be conducted with more data and also with new prediction equations containing Swiss methane data in the calibration data set for deriving the equation.
I would like to thank both host institutions for giving me the opportunity to visit them. I cordially thank Amélie Vanlierde, Clément Grelet and Frédéric Dehareng of CRA-W and the group of Nicolas Gengler at ULg GxABT, particularly Marie-Laure Vanrobays and Frédéric Colinet, for their warm welcome, the fruitful discussions, and the interesting time I had at your place. I really look forward to our further collaboration.