The modulation of milk components is a key point for dairy farmers and nutritionists and has long been studied by ruminant physiologists. In this scenario, the control of the milk fat production and composition in dairy cows might help farmers to drive market demands.
Milk fat is mainly constituted by triglycerides composed by fatty acids and glycerol and, in general, no single dietary factor is responsible for milk fat changes but there are several interactions among various dietary components.
The milk fat constituents are synthesized from different precursors derived by digestion: volatile fatty acids (VFA: acetic, propionic, butyric), long-chain FA (LCFA), glucose. On the other hand, the secretion of milk fat could be inhibited by other products from digestion like the trans10, cis12-CLA and proteins.
The dietary modifications are a relatively simple way to modulate milk fat concentration (MFC) and yield (MFY), as these effects are quite rapid and reversible. Milk fat depression (MFD) is an extreme example of nutritional modulation of milk fat, with reductions in MFC of up to 50%. These reasons and the possibility to control milk fat concentration and yield through the dairy cows ration are stimulating challenges and it might be interesting to predict changes in milk fat content. This convinced us to develop the NDS Milk Fat Sub-model in order to estimate the expected variations in MFC and MFY. Based on published studies and on the ability of NDS to predict, at some level, the pattern of individual nutrients derived from digestion, the sub-model quantifies and compares the effects on milk fat secretion, due to variations in several diet parameters and nutrient supply derived from digestion in order to predict changes in milk fat.
The aim of the sub-model was to assess if nutrients derived from digestion could have a significant effect on milk fat variations. The approach used derives from Maxim et al. (2011): it predicts changes in nutrient supply as a result of dietary modifications and then determines further responses in MFC and MFY, making the hypothesis that milk fat secretion can be dependent by the digestive flow of these different nutrients.
This will allow us to
foreseen the variations of MFC and MFY by summing the individual responses to
the digestive flows, which vary simultaneously with dietary changes.
The sub-model describes the effects on milk fat secretion and responses to the changes in the supply of two diet parameters:
- peNDF % DM
- rumen degraded starch % DM
and of seven nutrients derived from digestion for which enough published studies are available:
- the volatile fatty acids (VFA)
§ acetic
§ propionic
§ butyric
- glucose
- t10,c12-CLA
- long-chain FA (LCFA)
- proteins
It also indirectly considers the possible interferences from breed and days in milk.
The nutrients differ in their effects and you can find below a quick description of the expected responses.
Physical effective NDF (peNDF)
Each robust model predicting milk fat in dairy cows should consider the physically effective NDF in order to incorporate information on particle length and chemical NDF content of the diet, so that it can increase the accuracy of the prediction. However, contrary to other available models for milk fat prediction, peNDF is only one of the several components involved in the NDS Milk Fat Sub-model. It used to explain digesta stratification in the reticulorumen and rumination activity and, therefore, the ruminal buffering capacity and pH, and how these factors positively affect milk fat content. It does not represent the key factor involved in the prediction.
Rumen degraded starch (RDS)
Milk fat content also depends on rumen degraded starch in the diet. It is widely described in many studies that when it is included at high levels, the reduction of the content of degradable starch, increasing at the same time the peNDF content of the diet, tends to increase milk fat content, indicating that the balance between dietary peNDF and degradable starch is able to affect fat concentration in the milk for dairy cows by affecting rumen fermentation and metabolism. For this main reason, RDS is one of the factors involved in the model.
Volatile fatty acids (VFA)
Volatile fatty acids production from digestion of carbohydrates has a different impact according to the VFA considered:
§ acetate linearly increases MFC and MFY with cows in late lactation that tend to have a lower response;
§ propionate linearly decreases MFC and MFY. The higher is the initial MFC the highest will be the reduction;
§ butyrate significantly increases MFC and MFY without any interfering factors related to animals or diets.
Glucose
The increase of glucose availability reduces the arterial concentrations of milk fat precursors and significantly decreases MFC and MFY. The reduction tends to be curvilinear. Like the propionate, the higher is the initial MFC the highest will be the expected reduction of milk fat.
t10,c12-Conjugated Linoleic Acid
Strong available evidences indicate that, some situations of milk fat depression are due to changes in rumen biohydrogenation of unsaturated fatty acids and the passage of specific intermediates (e.g. trans-10, cis-12 conjugated linoleic acid) out of the rumen that subsequently reduces milk fat synthesis in the mammary gland.
Significant decreases in milk fat could be caused by small amount of t10,c12-CLA (1 to 2 g/day) passing to the small intestine (SI). De Veth et al. (2004) found a curvilinear relationship which is included in the model in order to evaluate the negative responses for MFC and MFY, changing the predicting level of this CLA isomer in the SI.
Long-chain FA (LCFA)
In general, long-chain fatty acids available at the small intestine, linearly increased MFC and MFY and the addition of fat to the diet can increase milk fat. However, this response is not constant and is often related to the amount and type of fat being fed. Unsaturated fatty acids have the potential to inhibit fat synthesis in the mammary gland. On the other hand, saturated fatty acids (e.g. palmitic acid - C16:0 - and stearic acid - C18:0) are inert in the rumen and MFC and MFY often increase when saturated fatty acids are fed.
For these reasons, the NDS Milk Fat Sub-model not only takes in account the amount of long-chain FA passing to the SI, but also considers the FA profile (SFA vs. MUFA and PUFA) in order to better predict milk fat responses.
Proteins
Proteins have the lowest effect, consistent with an indirect effect of proteins on milk fat (the supply of proteins increases mammary lipid synthesis). However, the duodenal supply of proteins (MP) tend to increase MFY and to decrease MFC. The responses are significant, even though, several interfering factors have relevant effects on these responses.