Researchers at the University of Nebraska-Lincoln have received a $2.3 million grant to explore selectively breeding beef cattle to lower methane emissions.
UNL is the only U.S. university to receive a grant through the $27 million international project. The initiative is funded by the Bezos Earth Fund, a philanthropic effort of billionaire Amazon owner Jeff Bezos, and the Global Methane Hub, an international philanthropic organization focused on reducing methane emissions.
Most of the grant money will pay for research on breeding low-methane traits into sheep and cattle across North America, South America, Europe, Africa and Australia. UNL will lead the research on low-methane beef genetics in commercial and crossbred cattle populations across the United States.
The UNL team, led by professor Matt Spangler, will collect and analyze methane data from beef cattle to “better understand the role genetics plays in methane production and its relationship with traits of economic importance to cattle producers,” according to a news release.
"I think this is a really exciting opportunity to engage graduate students and have them trained in really cutting-edge science that includes genomics and facets of the microbiome, and the opportunity to engage with international collaborators," Spangler said in an interview.
Methane, a potent greenhouse gas that traps heat in the atmosphere, is estimated to be responsible for about 30% of the rise in global temperatures since the industrial revolution. According to the Environmental Protection Agency, significant reductions in methane emissions would have a “rapid and significant effect” on global warming.
The agriculture sector is the largest source of methane emissions in the United States, and cattle are the largest contributors to livestock-related methane emissions. Cattle and other animals such as sheep and goats produce methane through a process called enteric fermentation.
Selective breeding has been used in dairy and beef cattle for decades. Farmers breed dairy cows to produce more milk, be resilient to disease and increase fertility. Beef cattle are bred to have an increased size and muscle mass and to produce high-quality meat.
With the discovery that some animals, even those in the same herd, emit significantly less methane than others, the idea of selectively breeding cattle to produce less methane has emerged as a potential tool for worldwide methane reduction.
A 2023 study published in the Journal of Dairy Science found that dairy cows with naturally lower methane emissions did not produce less or lower-quality milk. Other studies have suggested that the low-methane trait is at least partially heritable, which means it could be selectively bred.
"There's very strong evidence in the scientific literature that shows that enteric methane emission from beef cattle is moderately heritable," Spangler said. "The exact genetic mechanisms are not known, but we have evidence to say it's a complex trait, meaning there are thousands upon thousands, perhaps, of DNA variants that individually contribute to an animal's genetic propensity to emit methane.
"The goal is to produce genetic selection tools to enable breeders to be able to select for animals that perhaps produce less methane, but I think it's important to understand that would need to be done in concert with selection for other traits at the same time."
In the Global Methane Hub’s six-year strategic plan, genetic research is established as a priority. By 2030, the goal is to establish reference populations by genotyping between 20,000 and 50,000 animals within a population to identify, on a large scale, the traits that could lead to lower methane emissions.
Spangler said the first step is to acquire the equipment to gather methane data from such a large animal population. Then, he said, it's a matter of collecting and analyzing that data to begin to "uncover the answers to the questions we sought out to answer."
The research will span five years, and Spangler said he will collaborate with a colleague based in Kansas to gather additional data.
