Combining heterogeneous across-country data for prediction of enteric methane from proxies in dairy cattle

Large-scale measurement of enteric methane (CH4) from individual animals is a prerequisite for
estimation of genetic parameters and prediction of breeding values. Direct measurement of
individual CH4 emissions is logistically demanding and expensive, and correlated traits (proxies)
or models can be used instead as a means to predict emissions. However, most predictive models
tend to be specific and are valid mainly within the circumstances under which they were
developed. Robust prediction models that work across countries and production environments
may be built by combining heterogeneous data from several sources. However, combining
heterogeneous individual animal observations on CH4 proxies from several sources is
challenging and reports are scant in the literature. The main objective of this study was to
combine heterogeneous individual animal observations on CH4 proxies to develop robust enteric
CH4 prediction models. Data on dairy cattle CH4 emissions and related proxies from 16 herds
were made available by 13 research centers across 9 European countries within the Methagene
EU COST Action FA1302 consortium on “Large-scale methane measurements on individual
ruminants for genetic evaluations”. After a thorough editing and harmonization, the final
dataset comprised 48,804 observations from 2,391 cows. Random Forest (RF) models were used
to predict CH4 emissions and to estimate the relative importance of proxies for across-country
predictions. Principal component analysis (PCA) was used to detect potential data stratifications.
Milk yield, milk fat, DIM, BW, herd and country of origin appeared to be the most relevant
proxies in the prediction model. An overall prediction accuracy of 0.81 was estimated from the
combined heterogeneous data. This study is a first attempt to develop methods and approaches tocombine heterogeneous individual animal data on proxies for CH4 to build robust models for
prediction of CH4 emissions across diverse production systems and environments. The
methodology outlined here can be extended to combining heterogeneous data, pedigree
information and genome-wide dense marker information for estimation of genetic parameters
and prediction of breeding values for traits related to dairy system CH4 emissions.