A Net Carbohydrate and Protein System for Evaluating Cattle Diets: I. Ruminal Fermentation

Roger Murray | Download | HTML Embed
  • Jun 3, 2002
  • Views: 23
  • Page(s): 11
  • Size: 1.14 MB
  • Report

Share

Transcript

1 A Net Carbohydrate and Protein System for Evaluating Cattle Diets: I. Ruminal Fermentation J. B. RusselI*J, J. D. OConnortJ, D. G. Fox+, P. J. Van Soestt, and C. J. Sniffentvz *U.S. Dairy Forage Research Center, ARS, USDA, Madison, WI 53706 and U.S. Plant, Soil, and Nutrition Laboratory, Ithaca, NY 14853 and +Department of Animal Science, Cornell University, Ithaca, NY 14853 ABSTRACT: The Cornell Net Carbohydrate and NSC bacteria is enhanced by as much as 18.7% Protein System (CNCPSI has a kinetic submodel when proteins or peptides are available. The NSC that predicts ruminal fermentation. The ruminal bacteria produce less ammonia when the carbohy- microbial population is divided into bacteria that drate fermentation (growth) rate is rapid, but 34% ferment structural carbohydrate (SC) and those of the ammonia production is insensitive to the that ferment nonstructural carbohydrate (NSC). rate of carbohydrate fermentation. Ammonia Protozoa are accommodated by a decrease in the production rates are moderated by the rate of theoretical maximum growth yield (.50vs .40 g of peptide and amino acid uptake (.07 g of peptide cells per gram of carbohydrate fermented), and the yields are adjusted for maintenance requirements per gram of cells per hour), and peptides and LO5 vs .150 g of cell dry weight per gram of amino acids can pass out of the rumen if the rate carbohydrate fermented per hour for SC and NSC of proteolysis is faster than the rate of peptide bacteria, respectively). Bacterial yield is decreased utilization. The protein-sparing effect of iono- when forage NDF is < 20% (2.5% for every 1 YO phores is accommodated by decreasing the rate of decrease in NDF). The SC bacteria utilize only peptide uptake by 34%. Validation with published ammonia as a N source, but the NSC bacteria can data of microbial flow from the rumen gave a utilize either ammonia or peptides. The yield of regression with a slope of .94 and a n r2 of .88. Key Words: Ruminants, Rumen, Fermentation, Microbial Proteins, Ammonia, Computer Simulation J. Anim. Sci. 1002. 70:3551-3561 Introduction ment. If ruminant nutrition is to progress past the point at which diets are continually tested in In ruminants, feedstuffs are fermented in the virtually infinite combinations, the details of the rumen before gastric and intestinal digestion, and fermentation must be considered. The Cornell Net this fermentation has confounded the prediction of Carbohydrate and Protein System (CNCPS) has a animal performance from dietary ingredients. mechanistic submodel that provides quantitative Over the years, there has been considerable estimates of fermentation end products (the ME improvement in the feeding of ruminants, but this from VFA production, microbial protein and am- progress has been based on an empirical ap- monia) and materials that escape ruminal degra- proach that has largely treated the rumen as a dation (carbohydrates, protein, and undegraded black box. The experience of other scientific peptides). The CNCPS can serve as a research tool disciplines has shown that a mechanistic under- or a guide for practical ration balancing. standing is usually needed for sustained develop Ruminal Ecosystem Present address: P. 0. Box 2077, Corvallis, OR 97339. The quality and quantity of ruminal fermenta- 2Present address: W. H. Miner Agric. Res. Inst., Chazy, NY 12921. tion products are dependent on the types and Received May 24, 1991. activities of microorganisms in the rumen, and the Accepted July 13, 1992. ruminal microbial ecosystem is very diverse (Hun- 3551

2 3552 RUSSELL ET AL. gate, 1966; Bauchop, 1979; Coleman, 1980; Citron et tion losses. Because mammals do not produce al., 1987). Our understanding of ruminal microbial enzymes that can hydrolyze cellulose or hemicel- ecology is also complicated by the fact that there lulose, rapid rates of ruminal fiber digestion are are numerous interrelationships among the vari- desirable. In many dietary situations, much of the ous ruminal microbes (Russell and Hespell, 1981). amino acid N reaching the intestines is of The complexity of the ruminal ecosystem has led microbial origin, and this dependency on many nutritionists to conclude that the ruminal microbial protein means that the efficiency of ecosystem is so complex that it cannot be under- microbial growth can have a pronounced effect on stood or described in quantitative terms. More the ruminant. Ruminal microorganisms can utilize optimistic workers have attempted to model cer- ammonia, but in many cases the ammonia produc- tain aspects of ruminal fermentation (Reichel and tion rate exceeds the utilization rate. Excessive Baldwin, 1976; Baldwin et al., 19771, but the degree ammonia production and absorption increases N of aggregation has differed greatly. The persis- excretion and the energy cost of urea synthesis. tent question has always been, What level of The 15N studies of Nolan (1975) indicated that > aggregation and detail will allow a n adequate 25% of the protein N may be lost in this manner. representation? Volatile fatty acids arising from ruminal fermen- Reichel and Baldwin (19761 presented a linear programming model for evaluating ruminal tation are the primary energy source for the microbial function that used eight groups of animal. Acetate and butyrate are used efficiently ruminal microorganisms. They found that during by fattening animals, but they cannot make a net several solutions of the model, considerable sim- contribution to the glucose supply. Propionate can plification of the rumen microflora occurred. This be used for gluconeogenesis, but a high ratio of implies that current data and concepts, and the propionate to acetate in the rumen is associated hypothesis regarding relative metabolic rate, as with a reduction in milk fat. If the fermentation represented in the model, do not accommodate rate in the rumen is rapid, lactic acid sometimes adequately competition among several rumen accumulates. Lactate can be converted to blood microbial species and, thus, that additional data glucose, but it is a much stronger acid than the and concepts regarding rumen microbial interac- VFA. Lactate accumulation leads to ruminal aci- tions are required. In subsequent years, Baldwin dosis, a decline in fiber digestion, decreased feed and his colleagues discontinued the multimicrobe intake, founder, and, in extreme cases, even death approach and adopted a one-bugmodel in which (Slyter, 1976). In vitro studies indicated that the microbial diversity was not accommodated Bald- efficiency of microbial protein synthesis can win et al., 1977). decline significantly at pH values < 6.0 (Strobel The CNCPS divides the ruminal microbial and Russell, 1986). Methanogenesis provides a n ecosystem into two microbial groups, microbes alternative means of reducing equivalent disposal, that ferment nonstructural carbohydrate (NSC) which allows the microflora to increase ATP and those that ferment structural carbohydrate production, but methane can represent a signifi- (SC). This segregation reflects differences in N cant loss of feed energy. The CNCPS provides utilization and growth efficiency as well as an estimates of VFA energy and ammonia and almost exclusive partition of energy source utiliza- microbial protein production. tion. The SC bacteria ferment only cell wall carbohydrate, use only ammonia as a N source, and do not ferment peptides or amino acids. The Rates vs Amounts NSC bacteria ferment nonstructural carbohy drates (starch pectin, sugars, etc.), use either Fiations for ruminants traditionally have been ammonia or peptides and amino acids as a N balanced according to the amounts of specific feed source, and can produce ammonia. Certain strains of Butyrivibrio fibrisohens can ferment both starch components (crude fiber, ether extract, nitrogen- and cellulose and produce ammonia, but they free extract, and crude protein). Recent work, degrade cellulose a t a much slower rate than do however, has indicated that the rate of feedstuff other cellulolytic bacteria (Bryant, 1973). In the degradation in the rumen can have a profound CNCPS, B. fibrisolvem would be classified as an effect on fermentation end products and on animal NSC fermenter. performance (Nocek and Russell, 1988): 1) if the rate of protein degradation exceeds the rate of carbohydrate fermentation, large quantities of N An Ideal Ruminal Fermentation can be lost as ammonia; 21 if the rate of carbohy- drate fermentation exceeds the protein degrada- Any economic approach to ration balancing tion rate, microbial protein production can should seek to maximize the beneficial aspects of decrease; 3) if feedstuffs are degraded very slowly, ruminal fermentation while minimizing fermenta- rumen fill will decrease intake; and 4) if the

3 CARBOHYDRATE AND PROTEIN SYSTEM FOR CATTLE 3553 degradation rate is slow, some of the feed may microorganism per hour to reflect this limitation escape ruminal fermentation and pass directly to (Hino and Russell, 1985). Peptide flow from the the lower gut. rumen usually accounts for a small percentage of The procedures of Goering and Van Soest (1970) the dietary N (Chen et al., 1987a,b), but the peptide provided a basis for estimating amounts of availa- uptake rate can have a significant effect on the ble fiber, and in vitro and in situ studies have rate of ammonia production (see Protein Fermen- provided data on the rates of fiber digestion. tation and Ammonia Accumulation below). Protein degradation rates can be estimated by The passage of undegraded feed from the rumen enzymatic procedures (Krishnamoorthy et al., can affect nutrient availability. If the passed feed 19821, but starch degradation is still difficult to can be digested in the small intestine (e.g., protein estimate. The fermentation rate of starch can vary and starch), there may be a decrease in fermenta- greatly, and this rate is influenced by feed treat- tion losses (ammonia and methane) and an in- ment (e.g., pelleting), the method of storage (e.g., crease in nutrient retention. However, if the feed is dried shell corn vs high-moisture corn), and the digested poorly postruminally, digestibility will type of cereal grain that is fed (e.g.,barley vs corn; decline. A reduction in digestibility is not always 0rskov, 19761. Until recently, the best estimates of detrimental. If an increase in feed intake counter- starch degradation have come from in situ studies acts the decrease in digestibility, the rate of in which cereal grain was placed in a nylon bag nutrient absorption may increase. The optimal and suspended in the rumen (Nocek, 1988). Better feeding strategy and degree of passage is depen- methods are needed to estimate the rate at which dent on the cost of the feed and the value of starch will be degraded in vivo. A companion animal production. paper (Sniffen et al., 1992) describes the composi- Passage can have a profound effect on the tion and fermentation rates of various feedstuffs. balance of ruminal fermentation products. If car- bohydrates are not digested in the rumen, there will be a decrease in microbial growth and Passage vs Fermentation ammonia utilization. Although the passage of protein from the rumen may be advantageous Much of the feed that enters the rumen is from the standpoint of ammonia accumulation, a fermented, but some feed escapes ruminal degra- reduction in ruminally degraded protein may dation. The fate of the feed is ultimately deter- cause a decrease in the efficiency of microbial mined by the relative rates of fermentation and protein synthesis. The concept that ruminal mi- passage (Waldo and Smith, 1972). Fermentation crobes can only utilize ruminally available feed- rate is an inherent property of the feed, and in the stuffs is obvious; the use of TDN or total digestibil- CNCPS fermentation rates are described by first- ity to predict ruminal microbial yield in many order kinetics (substrate-limited, enzyme excess) instances will cause errors of prediction. and specific rate constants Passage rates are regulated by feed intake, processing (chopping, grinding, and other means of particle NRC Requirements size reduction), and the type of feed that is consumed (e.g., forage vs cereal grain). In the The National Research Council (NRC) of the CNCPS, passage rates can be altered by the user; National Academy of Sciences publishes estimates the guidelines for selecting passage rates are of the nutrient requirements of domestic animals, given in a companion paper (Sniffen et al., 1092). including ruminants. These guidelines are used by For many years it was assumed that proteolysis researchers, extension agents, nutritional consul- was the rate-limiting step in ruminal protein tants, and, ultimately, by animal producers. The degradation and that proteolysis was synonymous 1985 NRC publication about N usage by ruminants with utilization. This assumption was supported discusses various aspects of microbial growth in by the observation that amino acids were usually the rumen, and it provides equations that attempt present a t low concentrations in ruminal fluid to relate various aspects of ruminal function to (Wright and Hungate, 10671, but peptides were not animal performance. These empirical recommen- measured. Winters et al. (1964) noted that ruminal dations have a variety of limitations: 1) microbial fluid contained large amounts of nonammonia N growth in the rumen is driven by TDN or total that could not be precipitated by trichloroacetic digestibility rather than by an estimate of rumi- acid, and in vitro experiments showed that p e p nally available carbohydrate; 2) the dairy cattle tides sometimes accumulated (Mangan, 1972; Flus- equation relating TDN to microbial yield has a sell et al., 1983). These peptide accumulations negative intercept, which may underestimate indicated that certain types of peptides were not microbial protein production at low TDN intakes; readily utilized by ruminal bacteria; the CNCPS 31 microbial growth efficiency is constant; 4) the uses a rate constant of .07 g of peptide per gram of relationship between microbial yield and

4 3554 RUSSELL ET AL. Microorganisms that ferment starch, pectin, and sugars (NSC) grow more rapidly than those that ferment SC and utilize either ammonia or amino acids as a N source. In the CNCPS, the growth rate of both groups is directly proportional to the rate of carbohydrate digestion, so long a s a suitable N source is available (Hungate, 1966; Bryant, 1973; Hespell and Bryant, 1979; Russell and Baldwin, 1981). Empirical models of microbial growth in the Y 23) rumen have often assumed that ruminal microbial growth yields are a constant function of DMI or 0 .2 .4 .6 .8 OM digestion (Nocek and Russell, 19881, and the CARBOHYDRATE FERMENTATION or NRC (1985, 1989) used a static efficiency of 26.12 g of microbial N per kilogram of TDN. The use of GROWTH RATE (% per h) TDN to determine microbial yield ignores the fact Figure 1. The effect of carbohydrate fermentation that most ruminal bacteria are unable to utilize rate (growth rate) on the yield of ruminal bacteria that protein, fat, lipid, or ash as an energy source, and ferment structural carbohydrate (SC) and nonstructural that carbohydrate is the primary energy source for carbohydrate (NSC). The theoretical maximum growth growth (Nocek and Russell, 1988).A newly isolated yield is .4 g of cells per gram of carbohydrate, and the group of ammonia-producing bacteria can utilize maintenance energy requirements for SC and NSC peptides and amino acids as a n energy source bacteria are .05 and .150 g of carbohydrate per gram of (Russell et al., 1988; Chen and Russell, 1989).These bacteria per hour, respectively. Growth rates in the bacteria have a significant effect on ammonia rumen usually range from .05 to .2 h-l. production (see Protein Fermentation and Ammo- nia Accumulation below), but they are present at low numbers in vivo and only account for a small percentage of the microbial protein production. microbial maintenance energy requirement is ig- The use of a static efficiency by the NRC also nored; 5) the microbial population is not parti- ignores the fact that ruminal microorganisms have tioned according to metabolite activity and N maintenance energy requirements. When bacteria requirements; 6) rates of carbohydrate fermenta- grow slowly, a large proportion of the energy is tion are not integrated with rates of protein used to maintain the cells, and in this regard degradation; and 7) feed degradations are fixed maintenance energy is analogous to the fixed and thus not sensitive to variations in feed intake overhead of a business. One can only make a and passage. The CNCPS rumen submodel has profit (growth) after the overhead is met [main- allowances for each of these factors and provides tenance). If cash flow is large (rapid rates of a more quantitative analysis of ruminal fermenta- energy utilization), overhead (maintenance) will tion and nutrient availability. make up a small proportion of the total budget. Because the growth rate of microorganisms in the rumen is sometimes very slow, maintenance Microbial Growth energy can have a significant effect on microbial growth efficiency (Russell and Wallace, 1988). Ruminal microorganisms derive most of their In the derivation of Pirt (19851, bacterial main- energy from the fermentation of carbohydrates, tenance is defined as a time-dependent function and ruminal bacteria may be categorized in a that is directly proportional to cell mass (m = general way according to the type of carbohydrate grams of carbohydrate per gram of bacteria per that they ferment (Russell, 1984). In the CNCPS, hour), and the theoretical maximum yield (YG= the ruminal microorganisms are partitioned into grams of bacteria per gram of carbohydrate) is those that ferment SC and those that ferment defined as the yield that would be obtained if there NSC. Butyrivibrio fibrisolvens has the potential to were no maintenance. The CNCPS uses the Pirt occupy both of these niches, but most species (e.g., derivation to adjust microbial yield as a function cellulolytics vs amylolytics) can be separated by of growth rate, but there are few data concerning this arbitrary classification. the maintenance energy or theoretical growth Microorganisms that ferment cellulose and yields of ruminal microorganisms. hemicellulose (SC) grow slowly and utilize ammo- Isaacson et al. (1975) reported that mixed rumi- nia as a N source for microbial protein synthesis. nal microorganisms had a theoretical maximum

5 CARBOHYDRATE AND PROTEIN SYSTEM FOR CATTLE 3555 yield .5 g of cell dry weight per gram of carbohy- 20 drate. These in vitro experiments were, however, conducted in the absence of protozoa. Protozoal predation leads to a turnover of bacteria in the rumen (Coleman, 1980). The CNCPS makes a n 15 allowance for protozoal predation by decreasing the theoretical maximum growth yield from 50 to 40%. 10 Pure culture experiments indicated that ruminal bacterial maintenance energy was not a constant, and the range was .022 to .187 g of carbohydrate 5 per gram of bacteria per hour (Russell and Baldwin, 1981). Based on these studies, NSC and SC bacteria are assigned maintenance values of .150 and .05 g of carbohydrate per gram of bacteria 0 per hour, respectively. The effect of bacterial 0 2 4 6 8 10 12 14 maintenance on the efficiency of microbial growth is depicted in Figure 1. Over the range of specific Ratio of Amino Acids to growth rates possible in the rumen, yield can vary Total Organic Mattet% by more than threefold. In vitro studies showed that ruminal bacteria Figure 2 . Effect of amino acid nitrogen on the yield of respond in a positive fashion to the provision of ruminal microbial protein in vitro. Redrawn from peptides and amino acids [Russell and Sniffen, Russell and Sniffen (1984). 18841, but it should be realized that SC bacteria are unable to utilize amino N (Bryant, 1973).In the CNCPS, the yield of NSC bacteria is increased as much as 18.7%as the ratio of peptides to NSC plus growth. In vitro studies (Russell et al., 1983) peptides in ruminal fluid increases from 0 to 14%. indicated that microorganisms that ferment NSC Above 14% peptide, there is no further improve- derived 66% of their N from peptides or amino ment in yield (Figure 2). If ammonia becomes acids and 34% of their N from ammonia, and that limiting, yield should theoretically decline, but the this proportion was not influenced by the growth CNCPS does not make any provision for this type rate of the microorganisms. When peptides and of limitation. Because ammonia is one of the amino acids are no longer available, all the N outputs of the model, a n ammonia deficiency can must be derived from ammonia. Because the SC be remedied by adding NPN to the ruminant diet. bacteria do not utilize peptides or amino acids, all Ruminal cellulolytic bacteria require branched- their N must come from ammonia (Bryant, 1973). chain VFA for growth (Bryant, 1973). The current The rumen is well buffered by salivary secre- rumen submodel has a provision for branched- tions, but if the amount of dietary fiber is res- chain VFA, but we are developing a modification tricted and the rate of carbohydrate fermentation to accommodate this aspect of ruminal fermenta- is rapid, pH can decline. The NRC (1985)indicated tion. Because branched-chain VFA are derived that diets containing c 40% forage (20% NDF) from the fermentation of branched-chain amino have lower microbial growth yields; mixed ruminal acids (Russell and Sniffen, 19841, it should be bacteria that were incubated in vitro produced possible to estimate branched-chain VFA from the 50% less protein at pH 5.7 than at 6.7 (Strobel and fermentation of peptide and amino acids. The Russell, 1986). The model uses this latter relation- submodel calculates total ammonia production ship to adjust yield, and pH is predicted from the from true protein and NPN separately, and there- NDF content of the ration. When forage NDF is 2 fore this parameter should be easy to estimate. 20% of the DM, microbial yield is reduced 2.5% for Branched-chain VFA requirements, in turn, could every 1% decrease in NDF. This adjustment will be estimated from the amount of branched-chain probably be most acceptable when the forage is amino acids in SC microbial protein Purser and coarsely chopped and the feeding management Buechler, 1966). A branched-chain VFA deficiency allows a uniform DMI. If the NDF in the ration is could be encountered if high-forage diets are low finely chopped the user can discount yield by 3.0% in true protein and NPN is used as a supplement. per unit of NDF. Additional experiments are Nitrogen utilization (ammonia vs amino N) is needed to relate ruminal pH changes with the pool dependent on peptide availability and the type of of NSC, the carbohydrate fermentation rate, and carbohydrate that has the primary influence on the buffering capacity of saliva and fiber.

6 3556 RUSSELL ET AL. Protein Fermentation and Ammonia When mixed ruminal bacteria were incubated Accumulation with an excess of casein and with amounts of mixed carbohydrates that were inadequate to Most ruminal bacteria are able to use ammonia support maximum growth rates, there was a linear as a N source for microbial protein synthesis, but decline in ammonia production a s the fermenta- ruminal protein fermentation often creates more tion rate (growth rate1 increased, but 14N labeling ammonia than the microorganisms can utilize. In studies indicated that 34% of the ammonia was many cases, 2 25% of the protein can be lost as not influenced by carbohydrate availability (Rus- ammonia (Nolan, 1975). Because protein is the sell et al., 19831. Because B. ruminicola produces most expensive ingredient in many ruminant little ammonia when carbohydrate is available rations, there has been considerable interest in (Russell, 19831, this carbohydrate-insensitive am- reducing ruminal protein fermentation. monia production could not be explained. Proteins are degraded by extracellular enzymes, Recent ruminal enrichments, however, revealed and these proteinases must come in contact with three new species of ruminal bacteria (Russell et proteins through a n interaction involving water. If al., 1988; Chen and Russell, 1989). These strains the protein goes into solution quickly, the rate of that do not utilize carbohydrate grew on peptides enzymatic degradation is often increased (Tam- and amino acids and produced ammonia minga, 1979). Many of the proteins found in 20-fold faster than other ruminal bacteria. Because forages and soybeans are very soluble and are their rates of protein synthesis are 10- to degraded rapidly by ruminal bacteria. Heat treat- 25-fold lower than their rates of ammonia produc- ments that can denature the protein can decrease tion and their numbers in vivo are not high, these solubility and the rate of protein degradation. bacteria contribute little to microbial protein Some feeds contain proteins that are naturally production and are in most cases detrimental. insoluble brewers grains, distiller byproducts, fish Ionophores decreased ammonia production in meal, etc.). The submodel uses enzymatic data to vivo (Dinius et al., 1976) and in vitro (Van Neve1 predict the rate at which feedstuff proteins will be and Demeyer, 1977; Russell and Martin, 19841, but degraded (Krishnamoorthy et al., 1982). they have little effect on proteolysis. Whetstone et Carbohydrate has little effect on the rate of al. (1981) noted that monensin caused an increase protein degradation by extracellular proteinases, in nonammonia, nonprotein nitrogen in vitro. but it can greatly affect the end product of amino Previously isolated, ammonia-producing bacteria acid metabolism (Russell et al., 1983). In the were resistant to monensin (Chen and Wolin, 1979; CNCPS, NSC microorganisms take up peptides at Dennis et al., 19811, but the newly isolated ammo- a rate of .07 g of peptide per gram of microorgan- nia-producing bacteria are sensitive to ionophores ism per hour, and this N is used for microbial (Russell et al., 1988; Chen and Russell, 1989). protein synthesis or ammonia production (Russell The new isolates are included in the NSC and Martin, 1984; Hino and Russell, 1985). The bacteria even though they are unable to utilize diversion of peptides to microbial protein or carbohydrate. Their contribution arises from the ammonia is regulated by the availability of carbo- relationship between peptide transport and ammo- hydrate. When carbohydrate availability allows nia production. If only 64% of the peptides that are growth, 6 8 % of the NSC microbial protein comes from peptides and 34% comes from ammonia taken up can ever be incorporated into microbial (Russell et al., 1983). In the absence of carbohy- protein, the remainder must end up as ammonia. drate, all the peptide N is converted to ammonia. The influence of ionophores on ruminal ammonia Bladen et al. (1961) examined the capacity of production can be accommodated by a 34% various ruminal bacteria to ferment protein reduction in the peptide uptake rate constant. hydrolyzate and produce ammonia. They c o n cluded that Bacteroides ruminicola was the most important amino acid fermenting bacterium in the Recycled Nitrogen rumen of cattle, but subsequent experiments indi- cated that this species could not account for Recycled nitrogen may be a significant ruminal ammonia production in vivo. B. ruminicola B14, input when ammonia is deficient. The NRC (1985, one of the most active strains, had a specific 1989) assumed that recycled nitrogen can be as activity of 13.5 nmol of ammonia per milligram of much a s 70% of the protein intake if the dietary protein per minute (Russell, 19831, but mixed protein intake is low (5% CP). When the protein ruminal bacteria produced ammonia at a rate of intake is high (20% CPI the contribution of 31 nmol per milligram of protein per minute (Hino recycled nitrogen decreases ( 1 1% of intake CP). and Russell, 1985). This comparison introduced a The CNCPS uses the NRC (1985) equation for paradox. How could the best strains have an recycled nitrogen: Y = 121.7 - 12.01X + .3235X2, activity that was less than the average? where Y = urea N recycled (percentage of N

7 CARBOHYDRATE AND PROTEIN SYSTEM FOR CATTLE 3557 intake) and X = intake of crude protein, as a can supply ruminal ammonia, but not ruminal percentage of diet DM. peptides. If recycled N makes up a large proportion of the total ruminal N, the long-term protein needs of the animal may be underestimated. When protein Composition of Ruminal Bacteria intake increases to support maximal rates of milk production or gain, more amino acids may be used Bacterial composition can be influenced by for gluconeogenesis (for lactose and fetal growth), factors such as changes in growth rate, growth the efficiency of amino acid N utilization may phase, and growth media. In this model, bacteria decrease, and there can be a n increase in recycled are assumed to be 62.5% CP, 21.1% carbohydrate, N. Further work (possibly a n additional submodell 12% fat, and 4 . 4 % ash on a DM basis (Hespell and is needed to assess the value of recycled N as a N Bryant, 19791. Only 50 to 70% of microbial N is source for ruminal microbial growth. Recycled N available protein, and the remainder is bound in Table 1. Glossary of terms Y urea nitrogen recycled, 96 of nitrogen intake X intake crude protein, % of dry matter RFNDF percentage of NDF dry matter that is forage NDF y11 yield efficiency of SC bacteria from the available fiber fraction of the jth feedstuff, g of SC bacterialg of SC digested YZl yield efficiency of NSC bacteria from the sugar fraction of the jth feedstuff, g of NSC bacteria/g of NSC digested y31 yield efficiency of NSC bacteria from the starch fraction of the jth feedstuff, g of NSC bacteria/g of NSC digested KM1 maintenance rate of the structural carbohydrate bacteria, g of SC.g of bacteria-'.h-I KM2 maintenance rate of the nonstructural carbohydrate bacteria, g of NSC/g YGl theoretical maximum yield of the structural carbohydrate bacteria, g of bacterialg YGZ theoretical maximum yield of the nonstructural carbohydrate bacteria, g Ratioj ratio of peptides to peptide plus NSC in the jth feedstuff RDPEPj peptides in the j* feedstuff RDCAj RDCB1j RDCB2j g of NSC in the A (sugar)fraction of the jth feedstuff g of NSC in the B (starch and pectins) fraction of the th feedstuff g of SC in the B2 (available cell wall) fraction in the jt feedstuff RDPA g of ruminally degraded true protein RDPAN g of ruminally degraded true protein nitrogen Kd4j growth rate of the sugar-fermenting bacteria, h-' Kd5j growth rate of the starch-fermenting bacteria, h-' Kd6j growth rate of the SC-fermenting bacteria, h-' IMPj percentage improvement in bacterial yield, 46, due to the ratio of peptides to peptides plus NSC in jth feed- stuff, maximum = 18% (see Figure 21 SCBACTj yield of bacteria fermenting SC, from the jth feedstuff, g/d NSCBACTj yield of bacteria fermenting NSC from the jth feedstuff, g/d BACTj yield of bacteria, from the jth feedstuff, g/d BACTNj bacterial nitrogen, g/d SCBACTNj bacterial nitrogen of bacteria fermenting SC, g/d NSCBACTNj bacterial nitrogen of bacteria fermenting NSC, g/d K1 liquid passage rate, % / h PEPUPj bacterial peptide from the jth feedstuff, g/d PEPUPNj bacterial peptide nitrogen from the jth feedstuff, g/d PEPUN& bacterial peptide nitrogen retained from the jth feedstuff, g/d NPN nonprotein nitrogen intake, g/d NSCAMMNR = NSC bacteria ammonia nitrogen retained from the jth feedstuff g/d SCAMMNR = SC bacteria ammonia nitrogen retained from the jth feedstuff, g/d KUPI = peptide uptake rate by the NSC bacteria when ionophores are fed, .07 g KUP = peptide uptake rate by the NSC bacteria, % / g of NSC bacteria/h RAN REBTPj -- I Ruminal ammonia nitrogen, g bacterial true protein escaping the rumen by the jth feedstuff, g/d REBCWj REBNAj REBCHOj = - bacterial cell wall protein escaping the rumen by the jth feedstuff, g/d bacterial nucleic acid nitrogen escaping the rumen by the jth feedstuff, g/d bacterial carbohydrate escaping the rumen by the jth feedstuff, g/d REBFATi REBASHj I - bacterial fat escaping the m e n by the jth feedstuff, g/d bacterial ash escaping the rumen by the jth feedstuff, g/d

8 3558 RUSSELL ET AL. cell wall structures and nucleic acids (Ling and NSCBACTNj = .LO.NSCBACTj Buttery, 1978; Van Soest, 1982). The CNCPS as- sumes that nucleic acid N is 15% of the total microbial N (Purser and Buechler, 1966). Cell wall SCBACTNj .O * SCBACTj 1 protein is assumed to be 25% of the microbial protein (Bergen et al., 1967). The remainder is In the CNCPS, the input RDPA (ruminally degrad- available true protein. Ruminal organisms also able protein) is converted to peptides, RDPEPj, a t a contain significant quantities of lipid, storage rate that is the inherent property of the protein carbohydrate, and minerals (Van Soest, 1987). (see Sniffen et al., 1992). RDPEPj can be taken up (KUP)by NSC bacteria or passed out of the rumen a t the fluid dilution rate (Kl), an input of the model Ruminal Fermentation Model (see Sniffen et al., 1992): The CNCPS uses the inputs of feed intake, feed PEPUPj = ((KUP NSCBACT)/ composition, and the inherent rate of carbohy ((KUP NSCBACT) + K1)) RDPEPj) 4 drate and protein degradation to calculate the lIU"lina1 ammonia and peptide production, the If ionophores are fed the peptide uptake rate can amounts of carbohydrate, protein, and peptide be decreased by 34olO: escaping ruminal degradation, and the amount of metabolizable energy that will be available to the KUPI = KUPq.66 animal; these calculations are summarized in a companion paper (Sniffen et al., 1992). See Table 1 peptides taken up are 160,,o N: for a glossary of terms used in the CNCPS model. In the-CNCPS, bacterial yield is dependent on the PEPUPN = PEPUPj/6.25 rate of carbohydrate fermentation a d , hours-'), an input, theoretical maximum growth yield CYG, grams of bacteria per gram of carbohydrate The CNCPS then calculates ruminal N balance. fermented), the maintenance energy coefficient Because peptides can only provide 66% of the N to (KM, grams of carbohydrate per gram of bacteria NSC bacteria, then per hour), and the input, forage NDF (RFNDF, grams): PEPUPNR = .66 * NSCBACT when PEPUPN is =eater than if RFNDF e 20 then YG1 = YG1-1 (.66 NSCBACTN); otherwise, - ((20 - RFNDF)..025)) PEPUPNR = PEPUPN if RFNDFe20then YG2 = YG2.1 - ((20 - RFNDF)*.O25)) and I/Ylj = (KMl/Kdsj) + (I/YG1) 1/Y2j = (KM2/Kd4jl + (1/YG21 NSCAMMNR = NSCBACTN - PEPUPNR I/Y3j = (KM2/Kd5jl + (I/YG2) SCAMMNR = SCBACTN Then the yield of NSC bacteria is adjusted and ammonia (RAN) is according to the availability of ruminal peptides PEP) and the input of ruminally degraded carbo- RAN = (Y + RDPA + NPN) - (PEPUPNR + hydrate (RDC): NSCAMMNR + SCAMMNR) RATIOj = RDPEPj/(RDCAj + RDCBIj Bacteria (both SC and NSC) escaping the rumen + RDPEPj) (REB) are 62.5% CP (of which 25% is cell wall IMPj = EXP(.4O4* LnRATIOj 100) nitrogen [CWI and 15% is nucleic acid nitrogen + 1.942) [NAD,2 1% carbohydrate (CHOI, 12% fat (FAT) and SCBACTj = Y'j'RDCB2j 4.4% ash CASH): Y2j = Yzj'(1 + IMPj*.O1) Y3j = Y3j.(l + IMPj*.O11 REBTPj = .6O. .625 BACTj NSCBACTj = Yzj'RDCAj + Y3j'RDCBlj REBCWj = .25 * .625 * BACTj REBNAj = .15 .625 BACTj The total flow of bacteria (BACT) is the s u m of REBCHOj .21*BACTj NSC and SC bacteria: REBFATj = .12*BACTj REBASHj = .044 BACTj BACTj NSCBACTj + SCBACTj Undegraded carbohydrate and protein, peptides, The bacteria (BACTI are 10% N: and ruminal bacteria for each individual feedstuff

9 CARBOHYDRATE AND PROTEIN SYSTEM FOR CATTLE 3559 n V \ 4 0 in field studies with beef and dairy cattle 0 ply needed by growing calves. Similar results were observed (Fox et al., 1990a). We are encouraged by these 300 results, and feel that the CNCPS can account for the effect of variations in feed carbohydrate and protein fractions on microbial yield, feed protein 200 escaping ruminal degradation, and carbohydrate utilization in the rumen. Any scheme of ration formulation depends on 100 an accurate and meaningful description of dietary ingredients. Representative feeds are listed in a 0 companion paper (Sniffen et al., 18921, but the user may encounter feeding situations in which an p! n 0 100 200 300 400 appropriate comparison is not readily available. OBSERVED BACTERIAL N (g/dey) Because rate values are not yet an aspect of most commercial analyses, the user may think that the model is not applicable. Our experience, however, Figure 3. The relationship between observed flows of indicates that the model can still be valuable. The microbial nitrogen from the rumen and those predicted user can use present performance to estimate and by the Cornell Net Carbohydrate and Protein System. validate the inputs. After the inputs have been Data were taken from Robinson and Sniffen (1985), validated, the user then evaluates the proposed Garret et al. (19871, Glenn et al. (1989),McCarthy et al. changes to see whether the potential allocation of - (1989), and Song and Kennelly (1989). The regression line (not shown) had an r2 .88, a slope of .94, and an intercept of -12 g of N/d. The high N flows were diet is beneficial or detrimental. observed in lactating dairy cows, whereas the low N Implications flows were from trials with steers. Traditional schemes of ration balancing for ruminants have been based on equations that pass out of the rumen. Their absorption is dis- have attempted to predict nutrient availability cussed in companion papers (Fox et al., 1992; and animal productivity. In many cases these Sniffen et al., 1992). empirical equations have not provided a n ac- curate representation of ruminal fermentation and nutrient availability. The Cornel1 Net Carbohy- Evaluation drate and Protein System has a fermentation submodel model that compares the rate of carbo- The literature contains an abundance of perfor- hydrate fermentation with the rate of protein and mance data, but few studies have 11 described the predicts the ruminally digestible organic matter, diets so that the rates of carbohydrate fermenta- microbial protein synthesis, ammonia production, tion and protein degradation can be estimated and the flow of undigested feed to the lower gut. with accuracy and 2) reported the flow of microbial Based on the evaluations, it seems that this protein and undegraded feed to the lower gut. submodel gives a n accurate assessment of Thus, only certain studies could be used to microbial growth and fermentation end products. validate the model (Robinson and Sniffen, 1985; Garrett et al., 1987; Glenn et al., 1989; McCarthy et Literature Cited al., 1989; Song and Kennelly, 1989). These studies used Holstein cows and steers that were fed corn, Baldwin, R. L., J. J. Koong, and M. J. Ulyatt. 1977. A dynamic barley, and silage-based diets at various intakes. model of ruminant digestion for evaluation of factors af- The regression coefficient of the observed and fecting nutritive value. Agric. Systems 2:255. predicted BACTN was .94 k2 = .881 and the slope Bauchop, T. 1979. Rumen anaerobic b g i of cattle and sheep. was .94. The slightly negative intercept (-12 g of N/ Appl. Environ. Microbiol. 38: 148. Bergen, W. G., D. B. Purser, and J. H. Cline. 1967. Enzymatic d) indicates that the model is biased toward a n determination of the protein quality of individual rumen underprediction of BACTN, but this bias is minor bacteria. J. Nutr. 92:357. (Figure 3). Bird, S.H., and R. A. Leng. 1978. The effects of defaunation of We have used this submodel to design feeding the rumen on the growth of cattle on low-protein high energy diets. Br. J. Nutr. 40:163. experiments and evaluate the results (Fox et al., Bladen, H. A., M. P. Bryant, and R. N. Doetsch. 1961. A study of 1990b). The model correctly predicted metaboliza- bacterial species from the rumen which produce ammonia ble protein (microbial plus undegraded feed) sup- from protein hydrolyzate. Appl. Microbiol. 9:175.

10 3560 RUSSELL ET AL. Bryant, M. P. 1973. Nutritional requirements of the predominant Krishnamoorthy, U., T. V. Muscato, C. J. Sniffen, and P. J. Van rumen cellulolytic bacteria. Fed. Proc. 32:1809. Soest. 1982. Nitrogenous fractions in feedstuffs. J. Dairy Chen, G.. and J. B. Russell. 1988. Fermentation of peptides and Sci. 65:217. amino acids by a monensin-sensitive ruminal peptostrep Ling, J. R.,and P. J. Buttery. 1978. The simultaneous use of tococcus. Appl. Environ. Microbiol. 54:2742. ribonucleic acid, 358, 2,6 diaminopimelic acid and 2 Chen, G., and J. B. Russell. 1989. More monensin-sensitive, aminoethylphosphoric acid as markers of microbial nitro- ammonia producing bacteria from the rumen. Appl. Envi- gen at the duodenum of sheep. Br. J. Nutr. 39:165. ron. Microbiol. 55:1052. Mangan, J. L. 1972. Quantitative studies on nitrogen Chen, G., J. B. Russell, and C. J. Sniffen. 1987a. A procedure for metabolism in the bovine rumen. Br. J. Nutr. 27~281. measuring peptides in rumen fluid and evidence that p e p McCarthy, R.D., Jr., T. H. Klusmeyer, J. 3. Vicini, J. H. Clark, tide uptake can be a rate-limiting step ruminal protein and D. R. Nelson. 1989. Effects of source of protein and degradation. J. Dairy Sci. 70:1211. carbohydrate on ruminal fermentation and passage of Chen, G., C. J. Sniffen, and J. B. Russell. 1987b. Concentration nutrients to the small intestine of lactating cows. J. Dairy and estimated flow of peptides from the rumen of dairy Sci. 72~2002. cattle Effects of protein quantity, protein solubility and Nocek, J., and J. B. Russell. 1988. Protein and carbohydrate as feeding frequency. J. Dairy Sci. 70:983. an integrated system. Relationship of ruminal availability Chen, M., and M. J. Wolin. 1979. Effect of monensin and to microbial contribution and milk production. J. Dairy Sci. lasalocid-sodium on the growth of methanogenic and N- 71:2070. men saccharolytic bacteria. Appl. Environ. Microbiol. 38~72. Nocek, J. E. 1988. In situ and dther methods to estimate ruminal Citron, A., A. Breton, and G. Fonty. 1987. Rumen anaerobic protein and energy digestibility: A review. J. Dairy Sci. 71: fungi. Bull. Inst. Pasteur 85:329. 2051. Coleman, G. S.1980. Rumen ciliate protozoa. Adv. Parisitol. 18: Nolan, J. V. 1975. Quantitative models of nitrogen metabolism 121. in sheep. In: I. W. MacDonald and A.C.I. Warner (Ed.) Dennis, S. M., T. G. Nagaraja, and E. E. Bartley. 1981. Effects of Digestion and Metabolism in the Ruminant. p 416. Univ. of lasalocid or monensin on lactate-producing or using bac- New England Publishing Unit, Armidale, Australia. teria. J. Anim. Sci. 52:418. NRC. 1985. Ruminant Nitrogen Usage. National Academy Dinius, D. A., M. E. Simpson, and P. B. Marsh. 1976. Effect of Press, Washington, DC. monensin fed with forage on digestion and the ruminal NRC. 1989. Nutrient Requirements of Dairy Cattle. National ecosystem of steers. J. h i m . Sci. 42:229. Academy Press, Washington, DC. Fox, D. G., C. J. Sniffen, J. D. OConnor, J. B. Russell, and P. J. 0rskov, E. R. 1976. The effect of processing on digestion and Van Soest. 1992. A net carbohydrate and protein system for utilization of cereals by ruminants. Proc. Nutr. SOC.35:245. evaluating cattle diets: 111. Cattle requirements and diet Pirt, S.J. 1965. The maintenance energy of bacteria in growing adequacy. J. Anim. Sci. 703578. cultures. Proc. R. SOC.Lond. Ser. B Biol. Sci. 163:224. Fox, D. G., C. J. Sniffen, J. D. OConnor, J. B. Russell, P. J. Van Purser, D. B., and S.M. Buechler. 1966. Amino acid composition Soest, and W. Chalupa. 199Oa. Formulating cattle rations of rumen organisms. J. Dairy Sci. 49:81. for optimum carbohydrate, ammonia, peptides and amino Reichel, J. R.,and R.L. Baldwin. 1976. A rumen linear program acid concentrations, using The Cornell Net Carbohydrate ming model for evaluation of concepts of rumen microbial and Protein System. Roc. le90 Annu. Mtg. of the Califor- function. J. Dairy Sci. 59:439. nia-Nevada Chapter of the Am. Registry of Prof. Anim. Robinson, P. H., and C. J. Sniffen. 1985. Forestomach and whole Scientists. tract digestibility for lactating dairy cows as influenced by Fox, D. G., C. J. Sniffen, J. D. OConnor, J. B. Russell, P. J. Van feeding frequency. J. Dairy Sci. 68:857. Soest, and W. Chalupa. 199Ob. Using the Cornell Net Car- Russell, J. B. 1983. Fermentation of peptides by Bacteroides bohydrate and Protein System to predict the effects of rurninicola B14. Appl. Environ. Microbiol. 45:1586. metabolic modifiers on the metabolizable energy and pro- Russell, J. B. 1984. Factors influencing competition and compo- tein requirement of growing cattle. Proc. Cornell Nutr. sitions of rumen bacterial flora. Proc. Symp. Herbivore Cod. p 28. Nutrition in Sub-tropics and Tropics. p 313. The Science Garrett, J. E., R. D. Goodrich, J. C. Meiske, and M. D. Stem. Press, Craighall, South Africa. 1987. Influence of supplemental nitrogen source on diges- Russell, J. B., and R.L. Baldwin. 1981. Microbial rumen fermen- tion of nitrogen, dry matter, and organic matter and on in tation. J. Dairy Sci. 64:1153. vivo rate of ruminal protein degradation. J. Anim. Sci. 64: Russell, J. B., and R. B. Hespell. 1985. Regulation of lactate 1801. production in Streptococcus bovis A spiraling effect that Glenn, B. P., G. A. Varga, G. B. Huntington, and D. R. Waldo. contributes to rumen acidosis. J. Dairy Sci. 68:1712. 1989. Duodenal nutrient flow and digestibility in Holstein Russell, J. B., and S.A. Martin. 1984. Effects of various methane steers fed formaldehyde- and formic acid-treated alfalfa or inhibitors on the fermentation of amino acids by mixed orchardgrass silage at two intakes. J. Anim. Sci. 67613. rumen microorganisms in vitro. J. Anim. Sci. 59:1329. Goering, H. K., and P. J. Van Soest. 1970. Forage fiber analyses Russell, J. B., and C. J. Sniffen. 1984. Effect of carbon-4 and (apparatus, reagents, procedures, and some applications). carbon-5 volatile fatty acids on growth of mixed rumen Agric. Handbook 379. AFlS, USDA, Washington, DC. bacteria in vitro. J. Dairy Sci. 67987. Hespell, R. B., and M. P. Bryant. 1979. Emciency of rumen Russell, J. B., C. J. Sniffen, and P. J. Van Soest. 1983. Effect of microbial growth: Influence of some theoretical and e x carbohydrate limitation on degradation and utilization of perimental factors on YAP. J. Anim. Sci. 49:1640. casein by mixed rumen bacteria. J. Dairy Sci. 66:763. Hino, T., and J. B. Russell. 1985. The effect of reducing equiva- Russell, 3.B., H. J. Strobel, and G. Chen. 1988. The enrichment lent disposal and NADH/NAD on the determination of and isolation of a ruminal bacterium with a very high amino acids by intact and cell-free extracts of rumen specific activity of ammonia production. Appl. Environ. microorganisms. Appl. Environ. Microbiol. 50:1368. Microbiol. 54:872. Hungate, R. E. 1966. The Rumen and its Microbes. Academic Russell, J. B., and R. J. Wallace. 1988. Energy yielding and Press, New York. consuming reactions, In: P. N. Hobson (Ed.)pp 185-216. The Isaacson, H. R.,F. C. Hinds, M. P. Bryant, and F. N. Owens. Rumen Microbial Ecosystem. Elsevier Applied Science, 1975. Efficiency of energy utilization by mixed rumen bac- London. teria in continuous culture. J. Dairy Sci. 58:1645. Slyter, L. L. 1976. Influence of acidosis on rumen function. J.

11 CARBOHYDRATE AND PROTEIN SYSTEM FOR CATTLE 3561 Anim. Sci. 43910. Van Nevel, C. J., and D. I. Demeyer. 1977. Effect of monensin on Sniffen, C. J., J. D. OConnor, P. J. Van Soest, D. G. Fox, and J. rumen metabolism in vitro. Appl. Environ. Microbiol. 34: B. Russell. 1992. A net carbohydrate and protein system for 251. evaluating cattle diets: 11. Carbohydrate and protein avail- Van Soest, P. J. 1987. Nutritional Ecology of the Ruminant. ability. J. Anim. Sci. 70:3562. Cornell University Press, Ithaca, NY. Song, M. K., and J. J. Kennelly. 1989. Effect of ammoniated Waldo, D. R., and L. W. Smith. 1872. Model of cellulose disap- barley silage on ruminal fermentation, nitrogen supply to pearance from the rumen. J. Dairy Sci. 55:472. small intestine, ruminal, and whole tract digestion, and Whetstone, H. D., C. L. Davis, and M. P. Bryant. 1981. Effect of milk production of Holstein cows. J. Dairy Sci. 72:2981. monensin on the breakdown of protein by ruminal microor- Strobel, H. J., and J. B. Russell. 1886. Effect of pH and energy ganisms in vitro. J. Dairy Sci. 53:803. spilling on bacterial protein synthesis by carbohydrate Winters, K. A., R. R. Johnson, and B. A. Dehority. 1964. limited cultures of mixed rumen bacteria. J. Dairy Sci. 69: Metabolism of urea nitrogen by mixed cultures of rumen 2941. bacteria grown on cellulose. J. Dairy Sci. 47:793. Tamminga, S.1979. Protein degradation in the forestomachs of Wright, D. E., and R.E. Hungate. 1967. Amino acid concentra- ruminants. J. Anim. Sci. 49:1615. tions in rumen fluid. Appl. Microbiol. 15:148.

Load More