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

Large-scale measurement of enteric methane (CH 4 ) from individual animals is a prerequisite for estimation of genetic parameters and prediction of breeding values. Direct measurement of individual CH 4 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 CH 4 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 CH 4 proxies to develop robust enteric CH 4 prediction models. Data on dairy cattle CH 4 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 CH 4 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 to combine heterogeneous individual animal data on proxies for CH 4 to build robust models for prediction of CH 4 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 CH 4 emissions.