Milk Component Price Variation – Should I Change Ration Formulation?

Amaferm and Milk Fat

Milk fat prices recently have been significantly higher than the historical average.  It is important to understand this critical milk component and the income opportunity it represents.  Rumen fermentation and health are quite important in the regulation of milk fat yield as noted by Dr. Juan Marquez in his article below.  Maximizing fiber digestion results in more acetate and butyrate, which are the major precursors of de-novo milk fat.  Amaferm is the most researched and validated product in the marketplace to increase and maximize the digestion of forage fiber.

Milk Component Price Variation – Should I Change Ration Formulation?

Juan Castro Marquez, PhD

Federal Milk Marketing Order (FMMO) milk fat prices have steadily risen over the course of the past year, 2015, and were $3.18/lb. and $2.91/lb. in November and December, respectively.  The 7-year average for the FMMO milk fat price (years 2009 to 2015) is $1.90/lb.  Given the relatively high prices for milk fat recently, should ration formulation change in response to rising prices?

Milk Fat Review

Milk fat is composed of fatty acids of various chain lengths and differing in saturation and unsaturation. These fatty acids are derived from synthesis by the cow or from preformed fatty acids.  Preformed fatty acids are characterized by being 16 carbons in length or greater and are supplied to the mammary gland from dietary absorbed fatty acids and body fat mobilization.  The proportion of preformed fatty acids is often related to intake of dietary fat, rate of body fat mobilization, and level of milk fat depression.  Fatty acids synthesized by the cow’s mammary gland (de-novo) vary from 4 carbons up to 16 carbons in length.  The lipid synthesis machinery that manufactures fatty acids is tightly regulated by certain fatty acids derived by altered rumen biohydrogenation.  Up to a 50% decrease in yield and significant decreases in the lipid synthesis machinery are observed during milk fat depression (Bauman et al., 2011).  Mitigating the risk factors that cause down regulation of de-novo synthesis of milk fat yield should be a priority of ration formulation especially since de-novo usually contribute ~50% of overall milk fat yield.  The precursors of de-novo milk fat synthesis are primarily acetate and β-hydroxybutyrate, which are derived from fiber digestion.

The yield of milk fat by dairy cows is complex, however, our knowledge of manipulating de-novo yield of milk fat has greatly increased and for preformed fatty acids, a plethora of various fatty acids supplements are currently marketed.  When feeding increasing levels of dietary fat, saturated fat as an example, understanding of partitioning of that fat towards extraction by the mammary gland or towards growth (body stores) is an ongoing discussion and is not represented by current nutrition models. Maximizing fiber digestion to generate precursors of de-novo milk fat as well as attempting to maximize up regulation of lipid synthesis machinery in the mammary gland should be an overarching goal to maximize milk fat yield in an economical manner when milk fat prices dictate.

Milk Fat Depression – Altered Biohydrogenation

Incomplete biohydrogenation of unsaturated fatty acids by the rumen microflora may result in the outflow of extremely potent intermediates (e.g. trans-10, cis-12 CLA) that can significantly down regulate activity of de-novo milk fat synthesis in the mammary gland.  Unsaturated fatty acids (e.g. corn oil, soybean oil, etc.) are inherently toxic to rumen microbiome; hence biohydrogenation of these unsaturated fatty acids to saturated fatty acids occurs in the rumen as a detoxifying mechanism.

Significant research has been conducted towards mitigating altered biohydrogenation and includes strategies such as: managing unsaturated fatty acid intake, rumen pH, management (slug feeding), microbial population shifts, etc. Given that milk fat prices are 53% higher than the 7-year average, it is important to remember that rumen fermentation and health are quite important in the regulation of milk fat yield.  Strategies that enhance de-novo synthesis of milk fat should be considered.

Nutrition Model limitations

Do current modeling programs help us formulate profit-maximizing diets with consideration for milk component prices, nutrient prices, and feed ingredient prices? No, they don’t as most programs only consider feed ingredient prices.  Today, most nutrition software programs are based on the assumption of nutrient requirements (NRC, 2001, CNCPS v6.5).  Nutrient requirements infer that there is no response when nutrients are consumed above the cow’s described requirements (St-Pierre and Weiss, 2012).  Nutrition models currently assume a constant efficiency of conversion, 64% for energy and 67% for protein, into milk up to the point of nutrient requirement.  When energy and protein are supplied in excess of the described cow requirements, the efficiency is suggested to be 0% for energy and protein for incorporation into milk (NRC, 2001, CNCPS v6.5).  A number of research studies have shown that the efficiency of converting protein to milk protein diminishes as protein supply is increased.  When protein is supplied close to model predicted requirements, the marginal efficiency is estimated to be in the range of 21 to 28% (Hanigan et al., 1998, Doepel et al., 2004) and the marginal efficiency does not go to 0% when protein supply exceeds model predicted protein requirement.

We should also recognize that overfeeding of some nutrients and/or reducing unsaturated fatty acid intake below or above industry suggested targets likely will result in some type of response which is not suggested by our nutrient requirement based models.

Most models “optimize” based on the objective of “least cost” to meet a specified set of nutrient requirements while few if any models possess the ability optimize with the objective of “profit maximization”.  Least cost diet formulation ignores nutrient and milk component prices and assumes nutrient requirements exist and that efficiency of nutrient utilization is constant.  A review of farm data from 95 dairy farms located in Pennsylvania showed that least cost diet formulation on average did not result in the maximum IOFC and instead feeding high cost diets in certain months over the 4-year study resulted in greater IOFC achieved by the farms (Buza et al., 2014).  Given the observation that some farms, if not most farms, are not using least cost ration formulation strategies (Buza et al., 2014), the assumption could be made that some nutritionists are actually relying on human intelligence for optimal dietary formulation which likely involves a profit maximization strategy and consideration of milk component and nutrient prices.

Human Intelligence is still needed

Since nutrition models are based on the concept of nutrient requirements, the ability of predicting responses to nutrients and finding an optimal dietary formulation of nutrient concentrations that consider the changes in price of milk fat are extremely limited and require the knowledge of the nutritionist to make changes outside of the model.

Milk fat is the most variable component in milk and our understanding of the regulation of milk fat synthesis from nutrition and management has increased greatly (Lock and VanAmburgh, 2012).  Qualitative relationships like the mechanisms of milk fat depression have been well documented. However, to date, current models lack quantitative description of these complex relationships, hence, human intelligence is still vastly needed for decision-making of ration formulation (Allen, 2011).

Strategies like reducing sources of polyunsaturated fatty acid intake (DDGS, etc.), minimizing risk factors that alter rumen fermentation, increasing the digestion of forage and amount of forage (physical effective fiber) in the diet, or supplementing of saturated fat supplements (e.g high C16:0 supplements) represent just a few of the well documented tactics for increasing milk fat yield (Lock and VanAmburgh, 2012).  These strategies as well as other approaches for increasing milk fat yield should be pursued with greater emphasis given the current economic value of milk fat.

Increasing fiber digestion, rumen fermentation dynamics, and/or reducing altered biohydrogenation often increase de-novo synthesis of milk fatty acids by the mammary gland.  A survey of 430 dairy farms in the Northeastern US found that increased proportion of de-novo fatty acids in milk fat was positively correlated with increased milk fat and milk protein bulk tank concentrations on these surveyed farms (Barbano et al., 2014).  While improving de-novo yield of milk fat is complex (e.g. multitude of factors and interactions affect rumen fermentation), we should recognize that currently improvements in de-novo yield of milk fat result in greater financial benefit than typically observed given the rise in milk fat prices.

We also need to recognize that dietary formulation adjustments to reduce altered rumen biohydrogenation and milk fat depression will not result in immediate responses.  Recent research has shown at least a 2 to 3-week lag for the full response from a dietary intervention to be observed (Rico and Harvatine, 2014).  The change in rumen microbial metabolism and/or shifts in population are speculated to be rate-limiting steps in the adaptation when diet intervention occurs (Rico and Harvatine, 2014), therefore, expectations of an immediate response in milk fat yield from dietary intervention is unlikely and instead a 2 to 3-week time lag is more likely.

Recognize that approximately ~50% of milk fat originates from de-novo synthesis by the mammary gland and rumen fermentation plays a major role in regulation and yield of these fatty acids.  Given that the economic value of milk fat is now much higher, increased investment in improving de-novo yield of milk fat is warranted.

In summary, we should recognize that nutrient costs and milk component prices vary dramatically on dairy farms; hence, the optimal supplementation and/or formulation strategies should likely change in response to milk component price changes.

References

Allen, M. S. (2011). Mind over Models. Tri-State Dairy Nutrition Conference, Fort Wayne, IN, The Ohio State University.

Baldwin, R.L., 1995. Modeling Ruminant Digestion and Metabolism. Chapman and Hall, London, UK.

Barbano, D. M., C. Melilli, and T. Overton (2014). Advanced use of FTIR Spectra of Milk for Feeding and Health Management. Proc. Cornell Nutr. Conf. Dept of Animal Sci., Syracuse, NY.

Bauman, D. E., K. J. Harvatine, and A. L. Lock. 2011. Nutrigenomics, Rumen-Derived Bioactive Fatty Acids, and the Regulation of Milk Fat Synthesis. Annu. Rev. Nutr. 31:299-319.

Buza, M. H., L. A. Holden, R. A. White, and V. A. Ishler (2014). “Evaluating the effect of ration composition on income over feed cost and milk yield.” Journal of Dairy Science 97(5): 3073-3080.

Doepel, L., D. Pacheco, J. J. Kennelly, M. D. Hanigan, I. F. Lopez, and H. Lapierre (2004). “Milk Protein Synthesis as a Function of Amino Acid Supply.” Journal of Dairy Science 87: 1279-1297.

Hanigan, M. D., J.P. Cant, D.C. Weakley, and J.L. Beckett (1998). “An evaluation of postabsorptive protein and amino acid metabolism in the lactating dairy cow ” Journal of Dairy Science 81: 3385-3401.

Lock, A. L., and M. E. VanAmburgh (2012). Feeding for Milk Components. Western Dairy Management Conference, Reno, NV.

NRC. 2001. Nutrient Requirements of Dairy Cattle. 7th ed. Natl. Acad. Press, Washington, DC.

Rico, D.E., and K. J. Harvatine (2013). “Induction of and recovery from milk fat depression occurs progressively in dairy cows switched between diets that differ in fiber and oil concentration” Journal of Dairy Science 96: 6621-6630.

St-Pierre, N. R., and W. P. Weiss (2012). Nutrient Requirements, Responses, and Feed Efficiency. 4-State Dairy Nutrition & Management Conference, Dubuque, Iowa.

VanAmburgh, M. E., E. A. Collao-Saenz, R.J. Higgs, D. A. Ross, E. B. Recktenwald, E. Raffrenato, L. E. Chase, T. R. Overton, J. K. Mills, and A. Foskolos (2015). “The Cornell Net Carbohydrate and Protein System: Updates to the model and evaluation of version 6.5.” Journal of Dairy Science 98(9): 6361-6380.