Новый метод селекции скота позволит снизить выбросы метана на 14% без ущерба для продуктивности

New research at SRUC suggests ruminal microbiome-driven breeding could offer a practical way to reduce methane emissions from cattle while protecting, and potentially improving, growth rates and feed efficiency. By identifying microbial traits linked to both lower emissions and production efficiency, researchers are exploring how breeding decisions could support more climate-conscious beef production without sacrificing animal performance. Q. What is microbiome-driven breeding? A. Microbiome-driven breeding is a way to select cattle for low methane emissions using the composition of the microbes in the rumen. These microbes ferment fibrous roughage that is impossible for the cattle’s own enzymes to digest, such as grass, converting it into nutrients the animal uses to produce meat and milk. Therefore, it is more than just identifying low-emitting cattle, as the composition of microbes in the rumen is also linked to feed efficiency and growth rate. In particular, when these microbes are replicating very quickly, they produce substantial nutrients for the cattle and less excess hydrogen, which methanogenic archaea use to produce the by-product, methane. Therefore, there is a win-win situation when using microbiome-driven breeding to reduce the unnecessary by-product methane, which is released through the mouth and nose into the atmosphere, while simultaneously improving cattle productivity. Q. How does microbiome-driven breeding reduce methane emissions? A. The microbiome can be thought of as the root-cause trait that can be selected for instead of selecting for the output, namely methane emissions released into the atmosphere. In addition, selecting for the composition of the microbiome would be a better criterion because it is more cost-effective to determine than measuring methane emissions, which are hard and costly to obtain from large cohorts of individual cattle. SRUC studies investigating the role of the microbiome in methane emissions use respiration chambers at the Beef and Sheep Research Centre, which measure the highly potent greenhouse gas accurately. Based on data from these chambers, high-emitting beef cattle have almost twice as much methane per kg of dry matter intake as low-emitting animals under equal conditions, meaning they were fed the same diet, were of the same breed, and were born and raised on SRUC’s farm. This indicates substantial variation in the trait, which has been shown to be heritable. Genetic selection requires genetic variation, which means this trait is something we can select for. Q. How can it prioritise growth rates? A. Once it was established that the microbiome has an impact on methane mitigation, further research investigated what microbes, particularly the functional genes they carry, are beneficial for methane reduction. So far, the researchers have identified the abundances of 3,600 microbial genes. They have identified which of these genes have abundances that are highly heritable and highly correlated to methane emissions. They also investigated the correlation between abundances of microbial genes and growth. The 41 microbial genes with highly heritable abundances and the most favourable genetic correlations with methane mitigation and growth rate were selected. The researchers then investigated the impact of two breeding strategies. In the first strategy, the goal was to reduce methane emissions and increase average daily gain using these 41 microbial genes. The second strategy was to reduce methane production based on the abundances of the most informative microbial genes that solely impact methane emissions. These microbial genes were entirely different from those used in the first strategy. The predicted selection response to the second strategy showed that methane production can be substantially reduced by microbiome-driven breeding, but it comes at the cost of average daily gain, which decreases. Therefore, the better breeding strategy was the first one, which used the 41 microbial genes that were also favourably correlated with growth. The overall reduction in methane emissions is not as high, but it protects the average daily gain, and the methane reduction is still 14% per generation and can cumulatively decrease further over future generations. At present, a practical selection trial with breeding company Genus has selected four sires with high genomic estimated breeding values (EBVs) for methane emissions and four sires with low genomic EBVs based solely on the composition of the microbiome of the first cohort of progeny. This first cohort of cattle was performance-tested for feed efficiency and was not measured for methane emissions. In the second cohort of progeny from these sires, there was a difference of up to 24% in methane emissions between progeny groups from high- and low-emitting sires, measured using the gold-standard technique, respiration chambers. Q. How possible is microbiome-driven breeding at scale? A. One way to implement this would be to use microbiome-driven breeding to estimate sire genomic EBVs and then use those sires via artificial insemination (AI) or natural service. It would be more effective using AI; however, the suckler sector does not use AI to the same degree as the dairy sector. It has to be remembered here that only 50% of the sire’s genetic merit to reduce methane emissions is passed on to the offspring. Replacement rate is the determining factor for how quickly this would be integrated into the herd. Using Agrecalc, modelling emissions across a spectrum of real farms showed that within 10 years, accounting for the initial implementation phase, the reduction in methane was 21%. Another faster option would be to use an animal genomic SNP key, developed based on SRUC’s previous data on the rumen microbiome, for heifers or cows. This would allow farmers to select the best heifers and cull the poorest cows in the herd. Here, farmers would simply take a blood or hair sample and then select suckler cattle without needing to measure methane emissions directly. By indirectly improving the efficiency of the rumen microbiome, this approach would also improve feed efficiency and growth rate.