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Milk fatty acid analysis: How will it help your farm?

Marc-Antoine Guesthier and Maxime Tarte for Progressive Dairy Published on 09 December 2019

Canada’s dairy industry has made great progress in access and usage of data available on-farm in the past years. Information on milk, specifically data on milk component and urea analysis at each delivery, can improve our knowledge of the feed efficiency of our herds and dramatically speed up the response time to a change.

In 2014, Dave Barbano of Cornell University presented a new method of near-infrared analysis to quantify the fatty acid content of milk. This technique allows us to measure fatty acid levels in milk more quickly and more economically than traditional laboratory techniques. As this analysis starts to make its way into the Canadian dairy industry, how can this information be useful to a producer?



The proportion of fatty acids present in milk allows us to better characterize how the rumen is working and then adapt our rations. The types of fat analyzed in milk are divided into three categories: de novo, mixed and preformed. These so-called de novo fats are short-chain fatty acids (normally less than 15 carbons) produced by the mammary gland. They represent roughly 50% of the fatty acid content in milk. The precursor molecules are acetate and butyrate volatile fatty acids produced in the rumen from fermentation of the diet. Preformed long-chain fatty acids (e.g., stearic acid or C18:0) come mostly from the diet, rumen fermentation and mobilized from bodyfat reserves of the animal when needed. Mixed fatty acid (e.g., palmitic acid or C16:0) comes from a mixture of the two processes.

Volatile fatty acids formed in the rumen affect the level of de novo fatty acids in milk and can provide us important information about the health of its microbiota. Volatile fatty acids are the main sources of energy for dairy cows and a better prediction of their abundance allows us to adjust and optimize our rations. In addition, different studies have established a clear link between the de novo concentration of fatty acids in milk and the level of milkfat (Figure 1).

Relationship between the amount of de novo fat per 100 grams of milk and milk fat percentageThese findings show a correlation between de novo milk fatty acid percentage and milkfat percentage, which helps us link milk fatty acid profile to animal performance.

Interpretation of the fatty acid profile in milk is constantly evolving in this area of expertise. The possibilities are great for this technology, and the information on fatty acids in milk allows us to work on five major themes: feed efficiency, rumen health, dairy cow performance, environmental impact of the ration and milk quality. Understanding the fatty acid composition in milk can allow us to characterize rumen function and performances. Using fatty acid composition, we can calculate a safety and a productivity index that allow us to determine if our rations are optimized.

It also tells us if the use of fibre and rapidly fermented ingredients are properly balanced. For example, high de novo fatty acids in milk would indicate a good ruminal performance and thus a good safety index. However, if very high, it can also mean a lower productivity index, meaning there is room to improve profitability and increase milk production. The ration should be perfectly balanced to optimize the use of available energy in the diet and fermentation process in the rumen. At the same time, rations should minimize the risk of causing negative effects of a low pH, such as acidosis. This can be validated with the milk fatty acid composition. Figure 2 illustrates the junction between the productivity index and the safety index.


Optimal performance zone

Milk fatty acid profile can be used as a biomarker of rumen health and performance. This biomarker can help us identify an imbalance between cellulolytic and amylolytic flora and calculate a ruminal health index (RHI) and fibre activity index (FAI) as shown in Figure 3.

Fibre activity index and ruminal helath index

The level of amylolytic and cellulolytic bacteria has been linked with the composition of some specific fatty acids that can be measured in milk. Using this information, it’s possible to develop an FAI to measure and maximise the production of milkfat. In this case, a more effective fibre digestion will result in a higher milkfat. This is directly linked with the level of de novo fat composition in milk.

Also, the RHI can help us measure the risk of an imbalance in rumen bacteria flora and evaluate rumen pH level. With this analysis, we can react more quickly to correct a problem before it affects the animal and has consequences on health and milk income.

Analysis of milk fatty acids could become a good indicator of rations that are too low in energy, too rich in unsaturated oil, have a lack of effective fibre or are high in starch. Analysis of these biomarkers can help us target cows in ketosis or at risk of displaced abomasum when milk is tested at a cow level. It can also help us react more rapidly to the impact of a new forage.


Using this concept, we can calculate the safety and profitability index on farms, as briefly described above. These indexes allow us to better understand opportunities and see challenges on farms sooner. Figure 4 shows an example over time of a herd monitored for these two indexes for two years.

Safety index and profitability index

We can observe that during the summer of 2016, the productivity index went below the red-dashed line. This was accompanied by a lower safety index.

Producers should aim for the highest productivity index as possible and a safety index slightly above the red dash line. In this scenario, the producer is not only maximizing performance of the ration but also maximizing milk production.

In conclusion, the evaluation of the milk fatty acids profile is an interesting tool, not only to measure the performance of dairy cows but also as a diagnostic tool to monitor the ruminal health of dairy animals. Assessment of milk quality, environmental impact using methane and carbon dioxide predictions are also possible with a biomarker tool, allowing better evaluation of the performance of rations used on the farm. In this era of access to information, a new source of data is always interesting and must be accompanied by powerful tools allowing us to interpret data and improve on-farm performance.  end mark

Maxime Tarte is a dairy technical specialist with Cargill Animal Nutrition & Health.

References omitted but are available upon request. Click here to email an editor.

Marc-Antoine Guesthier
  • Marc-Antoine Guesthier

  • Dairy Technology Development
  • Purina/Cargill Animal Nutrition & Health
  • Email Marc-Antoine Guesthier