AminoCow Incorporation of Current Nutrition Concepts in Software for Dairy Ration Balancing

Transcription

1 Background AminoCow Incorporation of Current Nutrition Concepts in Software for Dairy Ration Balancing M.J. Stevenson1, W. Heimbeck2 and R.A. Patton3 The origin of AminoCow Dairy Ration Evaluator software was in response to field nutritionists requests for a practical approach to determining when and how to utilize rumen-protected methionine sources such as Mepron. At the time, CPM/ CNCPS was the only publicly available program balancing amino acids in dairy rations. Unfortunately, field nutritionists reported this program was too time-consuming and complicated for routine field use. Therefore, the challenge was undertaken to develop a program with the following objectives: - scientifically sound, employing current research - easy to use, with practical application - to provide an educational vehicle for current nutritional concepts To fulfill the second objective, the decision was made to employ a spreadsheet format similar to that taken by the Spartan dairy ration balancing program. The decision was also made to operate in the Microsoft Windows environment, so that procedures, similar to those used in other common programs, would accelerate learning program operation. This paper walks through operation of the AminoCow program discussing the underlying nutrition concepts with a view to reducing safety factors employed in dairy ration formulation. Cow inputs By defining Cow Type (lactating, dry or heifer) on the Cow Description page, a drop down box containing breed, and for lactating cows, a lactation number appears. When a selection is made, the cow description fields are populated and can be overwritten with herd / group specific numbers. These values will be used to define nutrient requirements and predicted dry matter intake. The specific equations used are provided in the help files for the AminoCow program. Weight is the primary determinant of maintenance requirements. Within AminoCow maintenance requirements for amino acids are based upon estimates of protein turnover determined in the research of Garlick (1984) and MacRae (1989). Breed and weight are used to calculate weight gain necessary to achieve mature size and the nutrients required to do so. Condition score and days in milk are used to calculate required change in reserve gain per day to achieve a body condition score of 3.5 at dry off. 1Degussa Corporation, Kennesaw GA. 2Degussa AG, Hanau, Germany. 3Nittany Nutrition, Mifflinburg PA. 49

2 Milk production and composition are the predominant factors defining energy and amino acid requirements for lactating cows. Days in milk does factor in to amino acid requirements based on research by Overton (1998) since amino acids will be required for gluconeogenic purposes in the early lactation period. For growing heifers, daily weight gain will be the dominant factor. Amino acid requirements are also affected by endogenous protein losses which are largely related in magnitude to dry matter intake. Amino acid requirements are defined at the metabolizable level i.e. absorbed from the digestive tract into circulating blood and available for productive purposes. Thus, while it is recognized, for example, that a certain quantity of lysine must be provided to supply the lysine exported in milk protein, an assumed efficiency must also be applied to get from presence in blood to output in milk. Note that this does not equate to the mammary transfer efficiency since individual amino acids are used and catabolized in varying proportions in different body tissues. (Lapierre et al., 2006). An example of the required input for the Cow Description in AminoCow is provided in Figure 1. Since the cow inputs drive the requirements (as with any ration balancing program), Figure 1. Cow Description from AminoCow. 50

3 it is important that the inputs match the herd/group situation. Substantially, increasing (by >5 to 10%) milk yield and/or components as lead factors or production goals can seriously compromise efficiency, profitability and environmental sustainability. Commonly, despite the elevated production, intake remains unchanged for the ration evaluation. This does not match the real world and represents an extra safety factor, requiring an elevation of nutrient concentration. For example, elevating milk production from 75 lbs actual, by 20% to a challenge (or lead factor) level of 90 lbs, will increase metabolizable protein required by in excess of 300 grams. This would require an increase in crude protein of greater than one pound. If the ration is formulated with a dry matter intake of 50 lbs (consistent with the 75 lbs milk), to deliver this additional pound of protein requires an increase in ration crude protein content of about 2% (i.e 17% to 19%). However, milk production drives intake so normal increases in dry matter intake associated with the increased production to 90 lbs would cover part of the required protein increase, thereby diminishing the concentration required by 50% or more. Thus, judicious and realistic decisions need to be made regarding production inputs to avoid adding cost and increasing excretion of surplus nutrients. Use of actual dry matter intake and a lead factor of 5 lbs of milk or less are recommended. Critical Nutrients When it comes to reducing safety factors in order to enhance profitability and reduce nutrient excretion, including the nutrients that are required and measurable is important. In this context, however, for ruminants the nutrients required by rumen microbes must be considered, in addition those required directly by cows. Factors affecting nutrient availability must also be considered. The nutrients included on the feed analysis screen in AminoCow are shown in Figure 2. Recommended Analyses For the production drivers, energy and protein/amino acids, the following nutrient analyses were deemed critical: dry matter, crude protein, NDICP, ADICP, NDF, ADF, starch, sugar, lignin, fat and ash. Each of these contributes terms to the calculation of energy available for microbial growth within AminoCow (note all supply and requirement equations are fully referenced and available within the help files for AminoCow). The approach borrows heavily from NRC Dairy (2001) but with corrections for fat, RUP and VFA. The lignin term, it can be argued should be replaced by in vitro assay of NDF digestibility, since it is used primarily to calculate an NDF digestibility. Due, however, to the debate over length of incubation, lab to lab variation and lack of biological validation (Oba and Allen, 2005) this change is not yet warranted. Appropriate values for the foregoing nutrient analyses are critical to estimation of the amino acid supplies from rumen microbial protein. Amino Acids 51

4 Figure 2. Nutrients in AminoCow. For the supply of amino acids via rumen escape protein, crude protein and proper characteri-zation of ingredient type are important to accurate prediction of supply. This is because amino acid values are derived by regression equations relating each specific essential amino acid to the protein level for that specific ingredient. These regression equations are based on the numerous amino acid analyses conducted on feed ingredients by Degussa and compiled most recently in AminoDat (2006). Individual amino acid values pertinent for the feed ingredient are made available for formulation purposes via this practical approach. Values for rumen undegraded protein, as % of crude protein, in the ingredient database have been derived from in situ research. Digestibility of the bypass protein/amino acids has a default value of 80%, but is fully editable so that adjustments can be made for ingredients where processing may reduce digestibility. While limited research indicates the potential for a wide range of digestibilities within certain ingredients (Stern et al., 1995; Erasmus et al., 1994) an industry standard to assess this parameter is still lacking. Upon heating of ingredients, lysine in particular is susceptible to participating in Maillard reactions with carbohydrates thereby binding the lysine such that it is not available. Recommendations to 52

5 oversupply lysine are a measure to guard against these effects. Discounting the lysine in suspect ingredients may be a more cost effective safety factor to employ. Carbohydrates Carbohydrate supply as fiber, is essential to maintenance of a healthy rumen environment. While neutral detergent fiber is the most relevant measure for dairy cattle, Amino- Cow pro-vides ADF and effective NDF values as other measures useful in assessing fiber adequacy. Several guidelines for fiber adequacy are also calculated within AminoCow, including forage NDF as % total NDF, NDF intake as % bodyweight, forage NDF as %DM and forage ADF intake. This underlines the importance of fiber in dairy nutrition. Fiber adequacy, as the foundation of dairy nutrition is also important to the avoidance of extreme rumen conditions that would invalidate calculations of protein degradation and microbial yield. Integral with the fiber needed to maintain rumen health are fermentable carbohydrates to fuel microbial growth. Non-fiber carbohydrate (NFC), however, is neither consistent with respect to rate nor end product of fermentation. Hencem the need to subdivide NFC into the measurable entities of starch, sugar and neutral detergent solubles was identified. A consistent approach to the analysis of starch and sugar was needed, so the analysis protocol recommended by Hall (2000) was used wherever possible in developing the feed ingredient database. Collaboration with Cumberland Valley Analytical Services permitted adherence to this analytical protocol in putting together the database values. Ward et al,, (2003a, 2003b) established that starch and sugar content of forages varies widely and shows no strong correlation with other common analytes. Thus, laboratory analysis of starch and sugar is strongly recommended to establish proper values for ration formulation. Sugar in the research of Broderick and Radloff (2004) showed production parameters were optimized at about 5% of ration DM. This is summarized in the bar graphs below. The 5% sugar level corresponds with guidelines for lactating cows presented later. While starch is the dominant fermentable carbohydrate in dairy rations, due to grain type, processing conditions, variety etc., the proportion which is fermentable in the rumen is variable. Fermentability as % starch has, thus, been included in AminoCow based on research evaluations for individual ingredients. The lack of a suitable laboratory procedure, with biological validation, to measure fermentable starch routinely is a shortcoming, but efforts are being made to correct this (e.g. Hoffman and Shaver, 2006). Since glucose may be the most limiting nutrient for high producing cows (Herdt, 1988), AminoCow calculates the term glucose precursor (equal to sugar plus fermentable starch) to capture the major components that will be fermented to propionic acid which can then be converted to glucose. Rumen Degradable Protein The foregoing discussion of fiber and fermentable carbohydrate defines the main 53

6 Figure 3-6. Effects of Sugar Level on Intake and Fat-corrected Milk Yield (Broderick and Radloff, 2004). E ffect on Intake T rial #1 E ffec t on Intake T rial # S uga r %DM E ffe c t o n F a t C o r r e c te d M ilk Y ie ld T r ia l # 1 E ffect on Fat Corrected Milk Yield Trial # S u g a r % D M S u g a r %D M requirements for microbial growth, but there is another component needed. That is rumen available nitrogen which may be available as ammonia, peptides or amino acids. While in vitro, an advantage in microbial growth has been demonstrated (Argyle and Baldwin, 1989) when N is supplied as a peptide or amino acid, most rations should provide adequate peptide and amino acid from degradation of feed proteins (Baldwin et al., 1994). Thus, AminoCow treats all RDP sources, whether from NPN or protein, equally. The RDP requirement is established from ration fermentable carbohydrate content according to the relationship NFC / 3.2 = RDP requirement (Hoover and Stokes, 1991). The RDP requirement in AminoCow can be regarded as an optimum, but rations may be balanced with RDP deficiencies in order to take advantage of nitrogen recycling (Cyriac et al., 2006; Kalscheur et al., 2006; Recktenwald and Van Amburgh, 2006; Reynal and Broderick, 2005). AminoCow contains a calculation to discount microbial yield for deficient RDP and, hence, will predict the lower amino acid supply. Visualizing Safety Factors Built into Ration Supply Within AminoCow, it is possible to graph nutrient supply as a % of the requirement. An example is provided in Figure 7 for the essential amino acids. In this instance, supply for all amino acids are at or above requirements. Methionine 54

7 is the amino acid just meeting requirements. Other essential amino acids have apparently been oversupplied in order to ensure adequate methionine supply. This represents an opportunity to reduce protein supplementation to where another amino acid such as lysine is just meeting requirement. Methionine will then be deficient but can be supplied through a rumen-protected methionine source such as Mepron. When this is done the graph of essential amino acid supply versus requirements now looks like that in Figure 8. The improvement in amino acid profile results in lowering amino acid excess that represents a disposal cost and hurts productivity. The nitrogen will be disposed of via urine and represents unnecessary nitrogen pollution. Conclusion While AminoCow has been utilized to demonstrate concepts, those concepts are applicable to other programs. Assuring that the rumen microbial needs are met through the fiber, fermentable carbohydrate and rumen degradable protein supplies will establish conditions for predictable energy and amino acid supply to meet the cow requirements. By Figure 7. AminoCow Graph of Essential Amino Acid Supply versus Requirements. 55

8 Figure 8. AminoCow Graph of Essential Amino Acid Supply versus Requirements where Crude Protein has been Reduced and Rumen-protected Methionine used. meeting the cow s specific requirements for individual essential amino acids, rather than using a proxy such as crude protein, or even metabolizable protein, safety factors can be reduced thereby reducing wastage and enhancing opportunities to achieve profitability and environmental goals. References Argyle, J. L. and R. L. Baldwin Effects of amino acids and peptides on rumen microbial growth yields. J. Dairy Sci. 72: Baldwin, R. L., R. S. Emery and J. P. McNamara Metabolic relationships in the supply of nutrients for milk protein synthesis: integrative modeling. J. Dairy Sci. 77:

9 Broderick, G.A. and W.J. Radloff Effect of molasses supplementation on the production of lactating dairy cows fed diets based on alfalfa and corn silage. J. Dairy Sci. 87: Cyriac, J., A.G. Rius, M.L. McGilliard and M.D. Hanigan Effects of reducing ruminally degradable protein in the diets of lactating dairy cows. J. Dairy Sci. 89: Suppl. 1 M228 (abstract). Degussa AG AminoDat 3.0. Hanau, Germany. Doepel, L., D. Pacheco, J.J. Kennelly, M.D. Hanigan, I.F. Lopez and H. Lapierre Milk protein synthesis as a function of amino acid supply. J. Dairy Sci. 87: Erasmus, L. J., P. M. Botha and H. H. Meissner Effect of protein source on ruminal fermentation and passage of amino acids to the small intestine of lactating cows. J. Dairy Sci. 77: Garlick, P. J Assessment of protein metabolism in the intact animal. Pages in Protein Deposition in Animals. P. J. Buttery and D. B. Lindsay, eds., Butterworth, London. Hall, H. B Neutral Detergent Soluble Carbohydrates Nutritional Relevance and Analysis. University of Florida Bulletin 339. Herdt, T. H Fuel homeostasis in the ruminant. Veterinary Clinics of North America, Food Animal Practice 4: Hoffman, P. C. and R. D. Shaver Predicting the performance of corn silage. Proceedings of the California Animal Nutrition Conference. Pages Hoover, W. H. and S. R. Stokes Balancing carbohydrates and proteins for optimum rumen microbial yield. J. Dairy Sci. 74: Kalscheur, K.F., R.L. Baldwin VI, B.P. Glenn and R.A. Kohn Milk Production of dairy cows fed differing concentrations of rumen-degraded protein. J. Dairy Sci. 89: Lapierre, H., D. Pacheco, R. Berthiaume, D.R. Ouellet, C.G. Schwab, P. Dubreuil, G. Holtrop and G.E. Lobley What is the true supply of amino acids for a dairy cow? J. Dairy Sci. 89: E Suppl

10 MacRae, J. C Protein metabolism relationships with body reserves. Proceedings of the Cornell Nutrition Conference for Feed Manufacturers. Pages National Research Council Nutrient Requirements of Dairy Cattle, Seventh Revised Edition. National Academy Press, Washington, D.C. Oba, M. and M. Allen In vitro digestibility of forages. Proceedings of the Tri State Dairy Nutrition Conference. Pages Overton, T. R Substrate utilization for hepatic gluconeogenesis in the transition dairy cow. Proceedings of the Cornell Nutrition Conference for Feed Manufacturers. Pages Recktenwald, E.B. and M.E. Van Amburgh Examining nitrogen efficiencies in lactating dairy cattle using corn silage based diets. Proceedings of the Cornell Nutrition Conference. Pages Reynal, S.M. and G.A. Broderick Effect of dietary level of rumen-degraded protein on production and nitrogen metabolism in lactating dairy cows. J. Dairy Sci.88: Stern, M. D., S. Calsamiglia and M.I. Endres Estimates of ruminal degradability and post-ruminal digestibility of proteins. Proceedings 4-State Applied Nutrition and Management Conference. Pages Stokes, S. R., W. H. Hoover, T. K. Miller and R. Blauweikel. 1991a. Ruminal digestion and microbial utilization of diets varying in type of carbohydrate and protein. J. Dairy Sci. 74: Stokes, S. R., W. H. Hoover, T. K. Miller and R. P. Manski. 1991b. Impact of carbohydrate and protein levels on bacterial metabolism in continuous culture. J. Dairy Sci. 74: Ward, R.T., M. J. Stevenson and R. A. Patton. 2003a. Relationship of starch content in common forages to dry matter, crude protein, non-fiber carbohydrate and neutral detergent fiber. J. Dairy Sci. 86 (Suppl. 1):284. (Abstr.) Ward, R. T., M. J. Stevenson and R. A. Patton. 2003b. Sugar content in common forages and its relationship to non-fiber carbohydrate percentage. J. Dairy Sci. 86 (Suppl. 1):285. (Abstr.) 58