Milk Fat Depression: Old Problem With New Insights
Milk fat depression has existed for a long time and the most accepted theory is the "biohydrogenation theory" which basically states that specific intermediates of unsaturated fatty acid biohydrogenation inhibit mammary gland lipogenic enzymes which leads to decrease in milk fat synthesis.
New insights on milk fat depression:
Recent studies have shown that several conjugated linoleic acid (CLA) and other intermediates such as trans-C18:1 FA have been associated with milk fat depression.
Another aspect of milk fat depression is the determination of time of induction and recovery.
Knowing these times could help nutritionist understand what caused milk fat depression and how long should they wait until they observe an improvement in milk fat.
Studies have also showed that feeding low fiber and high oil diet decreases milk fat synthesis in 7 days and the same cows will take around 10 days to recover milk fat to a similar original value.
The amount and type of intermediate unsaturated fatty acids of biohydrogenation in the rumen will depend on a few factors - mainly the amount of unsaturated fatty acids intake and rumen pH.
Main sources of unsaturated fatty acids are grains in the silages, supplement such as corn grain and in some co-products such s DDGS.
Second important factor that increases the risk of MFD is lower rumen pH which is affected by the amount and type of starch in the diet as well as level of effective fiber supplied by forages.
Another study was also conducted to see if short term variation in the concentration of unsaturated fat will have negative effects on milk production and its components.
Result shows that cows in high variation group had lower dry matter intake and milk production.
In the short term, high variation did not have any effect on milk components but in the long term it did.
The complexity of variables affecting milk fat implies the need for more precision formulation and lab analysis.
Hence, it is important to have precise formulation models to help understand nutrients for available ingredients, the variability and how these measurements and their interactions with forage quality, mixing, sorting, overcrowding and so on to help improve performance of the cows.