Enteric methane research and mitigation strategies for pastoral-based beef cattle production systems

Improving Human Diets and Welfare through Using Herbivore-Based Foods: 2. Environmental Consequences and Mitigations

Animal-sourced foods are important for human nutrition and health, but they can have a negative impact on the environment. These impacts can result in land use tensions associated with population growth and the loss of native forests and wetlands during agricultural expansion. Increased greenhouse gas emissions, and high water use but poor water quality outcomes can also be associated. Life cycle analysis from cradle-to-distribution has shown that novel plant-based meat alternatives can have an environmental footprint lower than that of beef finished in feedlots, but higher than for beef raised on well-managed grazed pastures. However, several technologies and practices can be used to mitigate impacts. These include ensuring that grazing occurs when feed quality is high, the use of dietary additives, breeding of animals with higher growth rates and increased fecundity, rumen microbial manipulations through the use of vaccines, soil management to reduce nitrous oxide emission, management systems to improve carbon sequestration, improved nutrient use efficacy throughout the food chain, incorporating maize silage along with grasslands, use of cover crops, low-emission composting barns, covered manure storages, and direct injection of animal slurry into soil. The technologies and systems that help mitigate or actually provide solutions to the environmental impact are under constant refinement to enable ever-more efficient production systems to allow for the provision of animal-sourced foods to an ever-increasing population.

Effects of dietary supplementation with 3-nitrooxypropanol on enteric methane production, rumen fermentation, and performance in young growing beef cattle offered a 50:50 forage:concentrate diet

There is an urgent requirement internationally to reduce enteric methane (CH4) emissions from ruminants to meet greenhouse gas emissions reduction targets. Dietary supplementation with feed additives is one possible strategy under investigation as an effective solution. The effects of the CH4 inhibitor 3-nitrooxypropanol (3-NOP) at reducing CH4 emissions in beef have been shown mainly in adult cattle consuming backgrounding and high-energy finishing diets. In this study, the effects of dietary supplementation of young growing (≤6 mo) beef cattle with 3-NOP were examined in a 50:50 forage:concentrate diet. A total of 68 Dairy × Beef (Aberdeen Angus and Hereford dairy cross) male calves (≤6 mo of age at the start of experiment, body weight: 147 ± 38 kg) underwent a 3-wk acclimatization period and were then assigned to one of two treatments in a completely randomized block design. Dietary treatments were (1) control, placebo (no 3-NOP), and (2) 3-NOP applied at 150 mg kg-1 DM. Calves were fed a partial mixed ration for 12 wk. Body weight was recorded weekly and feed intake daily using the Calan Broadbent feeding system. Methane and hydrogen emissions were measured using the GreenFeed system. Total weight gained, dry matter intake (DMI), and average daily gain were not affected by 3-NOP (P > 0.05) supplementation. On average, the inclusion of 3-NOP decreased (P < 0.001) CH4 emissions: g d-1; g kg-1 DMI; by 30.6% and 27.2%, respectively, during the study with a greater reduction occurring over time. Incorporating 3-NOP into beef cattle diets is an efficient solution to decrease CH4 emissions during indoor feeding and when offered 50:50 forage:concentrate diet.

Unveiling microbial biomarkers of ruminant methane emission through machine learning

Background

Enteric methane from cow burps, which results from microbial fermentation of high-fiber feed in the rumen, is a significant contributor to greenhouse gas emissions. A promising strategy to address this problem is microbiome-based precision feed, which involves identifying key microorganisms for methane production. While machine learning algorithms have shown success in associating human gut microbiome with various human diseases, there have been limited efforts to employ these algorithms to establish microbial biomarkers for methane emissions in ruminants.

Methods

In this study, we aim to identify potential methane biomarkers for methane emission from ruminants by employing regression algorithms commonly used in human microbiome studies, coupled with different feature selection methods. To achieve this, we analyzed the microbiome compositions and identified possible confounding metadata variables in two large public datasets of Holstein cows. Using both the microbiome features and identified metadata variables, we trained different regressors to predict methane emission. With the optimized models, permutation tests were used to determine feature importance to find informative microbial features.

Results

Among the regression algorithms tested, random forest regression outperformed others and allowed the identification of several crucial microbial taxa for methane emission as members of the native rumen microbiome, including the genera Piromyces, Succinivibrionaceae UCG-002, and Acetobacter. Additionally, our results revealed that certain herd locations and feed composition markers, such as the lipid intake and neutral-detergent fiber intake, are also predictive features for methane emissions.

Conclusion

We demonstrated that machine learning, particularly regression algorithms, can effectively predict cow methane emissions and identify relevant rumen microorganisms. Our findings offer valuable insights for the development of microbiome-based precision feed strategies aiming at reducing methane emissions.