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Embrapa Researchers Study Nelore Cattle to Reduce Methane

Researchers at the Brazilian Agricultural Research Corporation (Embrapa) are investigating the relationship between Nelore cattle genetics and gut microbiota to reduce methane emissions and improve feed efficiency. The study aims to find ways to mitigate the environmental impact of livestock farming while maximizing the energy animals derive from their diet.

Methane production consumes animal energy

The production of methane in the rumen—the largest stomach chamber—is a natural part of bovine digestion, but it carries both environmental and economic costs.

To address this, the Embrapa team uses molecular biology techniques to analyze DNA, RNA, proteins, and other chemical substances simultaneously.

Our data shows how variation in microbial gene expression is directly linked to two factors of great economic and environmental interest: the volume of methane produced and feed efficiency.

Luciana Regitano, researcher at Embrapa Pecuária Sudeste

Embrapa Researchers Study Nelore Cattle to Reduce Methane
Photo: agencia.fapesp.br

Redirecting hydrogen to beneficial pathways

Developing additives to block methane formation requires careful metabolic management.

To prevent this, researchers are exploring ways to redirect that hydrogen into different metabolic routes.

Our strategies aim not only to inhibit the enzyme that generates methane, but also to redirect that hydrogen toward beneficial metabolic pathways, such as the production of propionate, an essential fatty acid that serves as an important energy source for cattle.

Luciana Regitano, researcher at Embrapa Pecuária Sudeste

EMBRAPA RESEARCH REVEALS LINK BETWEEN MICROBIOME AND FEED EFFICIENCY IN NELORE CATTLE

The research team is working to develop biotechnological targets for new feed additives, including probiotics, inhibitors, and microRNA-based technologies. Regitano explained that because microRNAs are stable and can be reproduced artificially, they could serve as a foundation for new regulatory technologies. The next phase of the research involves isolating these microorganisms and expressing their proteins in laboratory tests to experimentally validate findings from computer simulations.

Microbial colonization starts on pasture

A study conducted in partnership with the Brazilian Association of Zebu Breeders (ABCZ) tracked Nelore cattle from their initial pasture phase through to final fattening in feedlots. The research found that the microbes established while the animals are young have long-term effects on their growth and efficiency.

Researchers identified various microorganisms present during the early pasture stage that correlate strongly with feed efficiency and weight gain measured months later when the animals are in confinement.

Embrapa Researchers Study Nelore Cattle to Reduce Methane
Photo: agroemcampo.ig.com.br

We identified several microorganisms present during the initial grazing phase that correlate strongly with feed efficiency and weight gain measured months later, once the animal is in feedlot confinement.

Luciana Regitano, researcher at Embrapa Pecuária Sudeste

Economic barriers to genetic selection

While researchers have found genetic markers in cattle associated with specific microbes, using this information for direct breeding remains difficult. Unlike in poultry, pig, or dairy farming, the current technology for beef cattle does not yet offer a favorable financial return for selecting animals based solely on their microbiota.

The high cost of sequencing the microbiota compared to the resulting gains in production or emission reductions is a primary obstacle, according to Agência FAPESP. However, the agency notes that Embrapa is working with geneticists to evaluate if new methodologies and cheaper sequencing technologies might change this economic equation in the future.

To address the fact that approximately 48% of the microbial species found in Nelore cattle may be unknown to science, researchers have partnered with experts in bioinformatics and artificial intelligence (AI).