Quantifying and mitigating methane emission from dairy cows: is there a quick solution?

Methane is an important and potent greenhouse gas that contributes to climate change by trapping heat in the atmosphere. From a climate point of view, methane comes second after carbon dioxide having a global warming potential of 28-34 times more than carbon dioxide. Due to its short lifespan in the atmosphere (around 10 years), the effects of management strategies will be measurable in short term. The concentration of methane in the atmosphere has increased significantly (about 150%) within the past two centuries. The agriculture sector and emissions from livestock (both enteric and manure emissions) do contribute up to 39% of total global anthropogenic emissions.

Other sources including rice cultivation, leakages from oil, gas and coal production and biomass burning do contribute to the global anthropogenic methane emissions as well. Methane is produced in the rumen of dairy cows as a result of anaerobic feed digestion process. Methane emission in dairy cows is considered an energy loss to the animal and based on many factors, such as the total amount of food dairy cows eat, dietary composition, and ingredients, it can range from 2-10% of gross energy loss from the animals. There are different techniques able to either measure or estimate methane emissions from dairy cows. The simplest laboratory technique is the in vitro method, a procedure that requires taking rumen fluid from cannulated cows and bringing it to the laboratory for analysis by following the procedure of the in vitro technique.

Additional modeling work is then required based on the data we get from the in vitro method for better estimates of in vivo methane emission. Mathematical modeling is another method for predicting methane emission from dairy cows, which is based on applying key parameters (such as the amount of total intake, dietary fiber/fat content, and digestibility of the diet) in mathematical models. The golden method for measuring methane emission in live animals is the respiration chamber followed by the GreenFeed method. Animals are kept in chambers or required to visit a hood chamber system (GreenFeed) so that methane is measured continuously. The question remains how can we reduce methane emissions from dairy cows?

There are many strategies for reducing methane emissions from dairy cows. Actually, dietary strategies is the most developed because successful applications will give a rapid and direct change in methane emissions. The use of additives, such as the red algae Asparagopsis taxiformis to date is the most effective strategy that can reduce methane emission in dairy cows by up to 90%. The problem is the cost of cultivating, harvesting, freeze-drying, shipment of the algae, and possible health issues for dairy cows. The other promising strategy is the use of a chemical 3-Nitrooxypropanol (3NOP) which compared to the algae is much cheaper to use, but with a slighter inhibitory effect on methane emission in dairy cows. Non-additive dietary strategies is to date the cheapest and more sustainable approach to reducing methane emissions from dairy cows.

Studies have shown that using oats instead of barley in the diet of dairy cows reduces methane emissions (5-10%) without any adverse effects on productivity and health. Studies have shown that milk quality does also improve in cows fed with oats compared with barley. This approach is more towards a sustainable strategy and more favorable when it comes to human consumption and milk quality.

A more realistic alternative, is improving feed efficiency, which is already included in breeding programs in some countries within the EU and beyond to boost the economics of milk production in dairy cows and could be used as a strategy to reduce methane emission per unit of product. Potential of ration formulation  and breeding for improved feed efficiency is so far the most sustainable strategy as it promises to increase the value of resource use (mainly feed) while reducing the environmental footprint, which is a win-win scenario.