Published December 11, 2014

A Net Carbohydrate and Protein System for Evaluating Cattle Diets: I. Ruminal Fermentation J. B. RusselI*J, J. D. O’ConnortJ, 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 Protein System (CNCPSI has a kinetic submodel that predicts ruminal fermentation. The ruminal microbial population is divided into bacteria that ferment structural carbohydrate (SC) and those that ferment nonstructural carbohydrate (NSC). Protozoa are accommodated by a decrease in the theoretical maximum growth yield (.50vs .40 g of cells per gram of carbohydrate fermented), and the yields are adjusted for maintenance requirements LO5 vs .150 g of cell dry weight per gram of carbohydrate fermented per hour for SC and NSC bacteria, respectively). Bacterial yield is decreased when forage NDF is < 20% (2.5% for every 1 YO decrease in NDF). The SC bacteria utilize only ammonia as a N source, but the NSC bacteria can utilize either ammonia or peptides. The yield of

NSC bacteria is enhanced by as much as 18.7% when proteins or peptides are available. The NSC bacteria produce less ammonia when the carbohydrate fermentation (growth) rate is rapid, but 34% of the ammonia production is insensitive to the rate of carbohydrate fermentation. Ammonia production rates are moderated by the rate of peptide and amino acid uptake (.07 g of peptide per gram of cells per hour), and peptides and amino acids can pass out of the rumen if the rate of proteolysis is faster than the rate of peptide utilization. The protein-sparing effect of ionophores is accommodated by decreasing the rate of peptide uptake by 34%. Validation with published data of microbial flow from the rumen gave a 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 In ruminants, feedstuffs are fermented in the rumen before gastric and intestinal digestion, and this fermentation has confounded the prediction of animal performance from dietary ingredients. Over the years, there has been considerable improvement in the feeding of ruminants, but this progress has been based on an empirical approach that has largely treated the rumen as a “black box.” The experience of other scientific disciplines has shown that a mechanistic understanding is usually needed for sustained develop

ment. If ruminant nutrition is to progress past the point at which diets are continually tested in virtually infinite combinations, the details of the fermentation must be considered. The Cornell Net Carbohydrate and Protein System (CNCPS) has a mechanistic submodel that provides quantitative estimates of fermentation end products (the ME from VFA production, microbial protein and ammonia) and materials that escape ruminal degradation (carbohydrates, protein, and undegraded peptides). The CNCPS can serve as a research tool or a guide for practical ration balancing.

Ruminal Ecosystem ‘Present address: P. 0. Box 2077, Corvallis, OR 97339. 2Present address: W. H. Miner Agric. Res. Inst., Chazy, NY 12921.

Received May 24, 1991. Accepted July 13, 1992.

The quality and quantity of ruminal fermentation products are dependent on the types and activities of microorganisms in the rumen, and the ruminal microbial ecosystem is very diverse (Hun-




gate, 1966; Bauchop, 1979; Coleman, 1980; Citron et al., 1987). Our understanding of ruminal microbial ecology is also complicated by the fact that there are numerous interrelationships among the various ruminal microbes (Russell and Hespell, 1981). The complexity of the ruminal ecosystem has led many nutritionists to conclude that the ruminal ecosystem is so complex that it cannot be understood or described in quantitative terms. More optimistic workers have attempted to model certain aspects of ruminal fermentation (Reichel and Baldwin, 1976; Baldwin et al., 19771, but the degree of “aggregation” has differed greatly. The persistent question has always been, What level of aggregation and detail will allow a n adequate representation? Reichel and Baldwin (19761 presented a linear programming model for evaluating ruminal microbial function that used eight groups of ruminal microorganisms. They found that “during several solutions of the model, considerable simplification of the rumen microflora occurred. This implies that current data and concepts, and the hypothesis regarding relative metabolic rate, as represented in the model, do not accommodate adequately competition among several rumen microbial species and, thus, that additional data and concepts regarding rumen microbial interactions are required.” In subsequent years, Baldwin and his colleagues discontinued the multimicrobe approach and adopted a one-“bug”model in which microbial diversity was not accommodated Baldwin et al., 1977). The CNCPS divides the ruminal microbial ecosystem into two microbial groups, microbes that ferment nonstructural carbohydrate (NSC) and those that ferment structural carbohydrate (SC). This segregation reflects differences in N utilization and growth efficiency as well as an almost exclusive partition of energy source utilization. The SC bacteria ferment only cell wall carbohydrate, use only ammonia as a N source, and do not ferment peptides or amino acids. The NSC bacteria ferment nonstructural carbohy drates (starch pectin, sugars, etc.), use either ammonia or peptides and amino acids as a N source, and can produce ammonia. Certain strains of Butyrivibrio fibrisohens can ferment both starch and cellulose and produce ammonia, but they degrade cellulose a t a much slower rate than do other cellulolytic bacteria (Bryant, 1973). In the CNCPS, B. fibrisolvem would be classified as an NSC fermenter.

An Ideal Ruminal Fermentation Any economic approach to ration balancing should seek to maximize the beneficial aspects of ruminal fermentation while minimizing fermenta-

tion losses. Because mammals do not produce enzymes that can hydrolyze cellulose or hemicellulose, rapid rates of ruminal fiber digestion are desirable. In many dietary situations, much of the amino acid N reaching the intestines is of microbial origin, and this dependency on microbial protein means that the efficiency of microbial growth can have a pronounced effect on the ruminant. Ruminal microorganisms can utilize ammonia, but in many cases the ammonia production rate exceeds the utilization rate. Excessive ammonia production and absorption increases N excretion and the energy cost of urea synthesis. The 15N studies of Nolan (1975) indicated that > 25% of the protein N may be lost in this manner. Volatile fatty acids arising from ruminal fermentation are the primary energy source for the animal. Acetate and butyrate are used efficiently by fattening animals, but they cannot make a net contribution to the glucose supply. Propionate can be used for gluconeogenesis, but a high ratio of propionate to acetate in the rumen is associated with a reduction in milk fat. If the fermentation rate in the rumen is rapid, lactic acid sometimes accumulates. Lactate can be converted to blood glucose, but it is a much stronger acid than the VFA. Lactate accumulation leads to ruminal acidosis, a decline in fiber digestion, decreased feed intake, founder, and, in extreme cases, even death (Slyter, 1976). In vitro studies indicated that the efficiency of microbial protein synthesis can decline significantly at pH values < 6.0 (Strobel and Russell, 1986). Methanogenesis provides a n alternative means of reducing equivalent disposal, which allows the microflora to increase ATP production, but methane can represent a significant loss of feed energy. The CNCPS provides estimates of VFA energy and ammonia and microbial protein production.

Rates vs Amounts Fiations for ruminants traditionally have been balanced according to the amounts of specific feed components (crude fiber, ether extract, nitrogenfree extract, and crude protein). Recent work, however, has indicated that the rate of feedstuff degradation in the rumen can have a profound effect on fermentation end products and on animal performance (Nocek and Russell, 1988): 1) if the rate of protein degradation exceeds the rate of carbohydrate fermentation, large quantities of N can be lost as ammonia; 21 if the rate of carbohydrate fermentation exceeds the protein degradation rate, microbial protein production can decrease; 3) if feedstuffs are degraded very slowly, rumen fill will decrease intake; and 4) if the


degradation rate is slow, some of the feed may escape ruminal fermentation and pass directly to the lower gut. The procedures of Goering and Van Soest (1970) provided a basis for estimating amounts of available fiber, and in vitro and in situ studies have provided data on the rates of fiber digestion. Protein degradation rates can be estimated by enzymatic procedures (Krishnamoorthy et al., 19821, but starch degradation is still difficult to estimate. The fermentation rate of starch can vary greatly, and this rate is influenced by feed treatment (e.g., pelleting), the method of storage (e.g., dried shell corn vs high-moisture corn), and the type of cereal grain that is fed (e.g.,barley vs corn; 0rskov, 19761. Until recently, the best estimates of starch degradation have come from in situ studies in which cereal grain was placed in a nylon bag and suspended in the rumen (Nocek, 1988). Better methods are needed to estimate the rate at which starch will be degraded in vivo. A companion paper (Sniffen et al., 1992) describes the composition and fermentation rates of various feedstuffs.

Passage vs Fermentation Much of the feed that enters the rumen is fermented, but some feed escapes ruminal degradation. The fate of the feed is ultimately determined by the relative rates of fermentation and passage (Waldo and Smith, 1972). Fermentation rate is an inherent property of the feed, and in the CNCPS fermentation rates are described by firstorder kinetics (substrate-limited, enzyme excess) and specific rate constants Passage rates are regulated by feed intake, processing (chopping, grinding, and other means of particle size reduction), and the type of feed that is consumed (e.g., forage vs cereal grain). In the CNCPS, passage rates can be altered by the user; the guidelines for selecting passage rates are given in a companion paper (Sniffen et al., 1092). For many years it was assumed that proteolysis was the rate-limiting step in ruminal protein degradation and that proteolysis was synonymous with utilization. This assumption was supported by the observation that amino acids were usually present a t low concentrations in ruminal fluid (Wright and Hungate, 10671, but peptides were not measured. Winters et al. (1964) noted that ruminal fluid contained large amounts of nonammonia N that could not be precipitated by trichloroacetic acid, and in vitro experiments showed that p e p tides sometimes accumulated (Mangan, 1972; Flussell et al., 1983). These peptide accumulations indicated that certain types of peptides were not readily utilized by ruminal bacteria; the CNCPS uses a rate constant of .07 g of peptide per gram of


microorganism per hour to reflect this limitation (Hino and Russell, 1985). Peptide flow from the rumen usually accounts for a small percentage of the dietary N (Chen et al., 1987a,b), but the peptide uptake rate can have a significant effect on the rate of ammonia production (see Protein Fermentation and Ammonia Accumulation below). The passage of undegraded feed from the rumen can affect nutrient availability. If the passed feed can be digested in the small intestine (e.g., protein and starch), there may be a decrease in fermentation losses (ammonia and methane) and an increase in nutrient retention. However, if the feed is digested poorly postruminally, digestibility will decline. A reduction in digestibility is not always detrimental. If an increase in feed intake counteracts the decrease in digestibility, the rate of nutrient absorption may increase. The optimal feeding strategy and degree of passage is dependent on the cost of the feed and the value of animal production. Passage can have a profound effect on the balance of ruminal fermentation products. If carbohydrates are not digested in the rumen, there will be a decrease in microbial growth and ammonia utilization. Although the passage of protein from the rumen may be advantageous from the standpoint of ammonia accumulation, a reduction in ruminally degraded protein may cause a decrease in the efficiency of microbial protein synthesis. The concept that ruminal microbes can only utilize ruminally available feedstuffs is obvious; the use of TDN or total digestibility to predict ruminal microbial yield in many instances will cause errors of prediction.

NRC Requirements The National Research Council (NRC) of the National Academy of Sciences publishes estimates of the nutrient requirements of domestic animals, including ruminants. These guidelines are used by researchers, extension agents, nutritional consultants, and, ultimately, by animal producers. The 1985 NRC publication about N usage by ruminants discusses various aspects of microbial growth in the rumen, and it provides equations that attempt to relate various aspects of ruminal function to animal performance. These empirical recommendations have a variety of limitations: 1) microbial growth in the rumen is driven by TDN or total digestibility rather than by an estimate of ruminally available carbohydrate; 2) the dairy cattle equation relating TDN to microbial yield has a negative intercept, which may underestimate microbial protein production at low TDN intakes; 31 microbial growth efficiency is constant; 4) the relationship between microbial yield and



23) Y






CARBOHYDRATE FERMENTATION or GROWTH RATE (% per h) Figure 1. The effect of carbohydrate fermentation rate (growth rate) on the yield of ruminal bacteria that ferment structural carbohydrate (SC) and nonstructural carbohydrate (NSC). The theoretical maximum growth yield is .4 g of cells per gram of carbohydrate, and the maintenance energy requirements for SC and NSC bacteria are .05 and .150 g of carbohydrate per gram of bacteria per hour, respectively. Growth rates in the rumen usually range from .05 to .2 h-l.

microbial maintenance energy requirement is ignored; 5) the microbial population is not partitioned according to metabolite activity and N requirements; 6) rates of carbohydrate fermentation are not integrated with rates of protein degradation; and 7) feed degradations are fixed and thus not sensitive to variations in feed intake and passage. The CNCPS rumen submodel has allowances for each of these factors and provides a more quantitative analysis of ruminal fermentation and nutrient availability.

Microbial Growth Ruminal microorganisms derive most of their energy from the fermentation of carbohydrates, and ruminal bacteria may be categorized in a general way according to the type of carbohydrate that they ferment (Russell, 1984). In the CNCPS, the ruminal microorganisms are partitioned into those that ferment SC and those that ferment NSC. Butyrivibrio fibrisolvens has the potential to occupy both of these niches, but most species (e.g., cellulolytics vs amylolytics) can be separated by this arbitrary classification. Microorganisms that ferment cellulose and hemicellulose (SC) grow slowly and utilize ammonia as a N source for microbial protein synthesis.

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 rumen have often assumed that ruminal microbial growth yields are a constant function of DMI or OM digestion (Nocek and Russell, 19881, and the NRC (1985, 1989) used a static efficiency of 26.12 g of microbial N per kilogram of TDN. The use of TDN to determine microbial yield ignores the fact that most ruminal bacteria are unable to utilize protein, fat, lipid, or ash as an energy source, and that carbohydrate is the primary energy source for growth (Nocek and Russell, 1988).A newly isolated group of ammonia-producing bacteria can utilize peptides and amino acids as a n energy source (Russell et al., 1988; Chen and Russell, 1989).These bacteria have a significant effect on ammonia production (see Protein Fermentation and Ammonia Accumulation below), but they are present at low numbers in vivo and only account for a small percentage of the microbial protein production. The use of a static efficiency by the NRC also ignores the fact that ruminal microorganisms have maintenance energy requirements. When bacteria grow slowly, a large proportion of the energy is used to maintain the cells, and in this regard maintenance energy is analogous to the fixed overhead of a business. One can only make a profit (growth) after the overhead is met [maintenance). If cash flow is large (rapid rates of energy utilization), overhead (maintenance) will make up a small proportion of the total budget. Because the growth rate of microorganisms in the rumen is sometimes very slow, maintenance energy can have a significant effect on microbial growth efficiency (Russell and Wallace, 1988). In the derivation of Pirt (19851, bacterial maintenance is defined as a time-dependent function that is directly proportional to cell mass (m = grams of carbohydrate per gram of bacteria per hour), and the theoretical maximum yield (YG= grams of bacteria per gram of carbohydrate) is defined as the yield that would be obtained if there were no maintenance. The CNCPS uses the Pirt derivation to adjust microbial yield as a function of growth rate, but there are few data concerning the maintenance energy or theoretical growth yields of ruminal microorganisms. Isaacson et al. (1975) reported that mixed ruminal microorganisms had a theoretical maximum

AND PROTEIN SYSTEM FOR CATTLE 3555 yield .5 g of cell dry weight per gram of carbohy20 drate. These in vitro experiments were, however, conducted in the absence of protozoa. Protozoal predation leads to a turnover of bacteria in the 15 rumen (Coleman, 1980). The CNCPS makes a n 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 0 .150 and .05 g of carbohydrate per gram of bacteria 0 2 4 6 8 10 12 14 per hour, respectively. The effect of bacterial maintenance on the efficiency of microbial growth Ratio of Amino Acids to is depicted in Figure 1. Over the range of specific Total Organic Mattet% growth rates possible in the rumen, yield can vary by more than threefold. Figure 2 . Effect of amino acid nitrogen on the yield of In vitro studies showed that ruminal bacteria ruminal microbial protein in vitro. Redrawn from respond in a positive fashion to the provision of Russell and Sniffen (1984). peptides and amino acids [Russell and Sniffen, 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 growth. In vitro studies (Russell et al., 1983) much as 18.7%as the ratio of peptides to NSC plus 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 acids and 34% of their N from ammonia, and that ment in yield (Figure 2). If ammonia becomes this proportion was not influenced by the growth limiting, yield should theoretically decline, but the of the microorganisms. When peptides and rate CNCPS does not make any provision for this type amino acids are no longer available, all the N of limitation. Because ammonia is one of the must be derived from ammonia. Because the SC outputs of the model, a n ammonia deficiency can bacteria do not utilize peptides or amino acids, all be remedied by adding NPN to the ruminant diet. Ruminal cellulolytic bacteria require branched- their N must come from ammonia (Bryant, 1973). The rumen is well buffered by salivary secrechain VFA for growth (Bryant, 1973). The current rumen submodel has a provision for branched- tions, but if the amount of dietary fiber is reschain VFA, but we are developing a modification tricted and the rate of carbohydrate fermentation is rapid, pH can decline. The NRC (1985)indicated to accommodate this aspect of ruminal fermentathat diets containing c 40% forage (20% NDF) tion. Because branched-chain VFA are derived have lower microbial growth yields; mixed ruminal from the fermentation of branched-chain amino bacteria that were incubated in vitro produced acids (Russell and Sniffen, 19841, it should be 50% less protein at pH 5.7 than at 6.7 (Strobel and possible to estimate branched-chain VFA from the Russell, 1986). The model uses this latter relationfermentation of peptide and amino acids. The ship to adjust yield, and pH is predicted from the submodel calculates total ammonia production from true protein and NPN separately, and there- NDF content of the ration. When forage NDF is 2 20% of the DM, microbial yield is reduced 2.5% for fore this parameter should be easy to estimate. every 1% decrease in NDF. This adjustment will Branched-chain VFA requirements, in turn, could probably be most acceptable when the forage is be estimated from the amount of branched-chain 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. CARBOHYDRATE



Protein Fermentation and Ammonia Accumulation Most ruminal bacteria are able to use ammonia as a N source for microbial protein synthesis, but ruminal protein fermentation often creates more ammonia than the microorganisms can utilize. In many cases, 2 25% of the protein can be lost as ammonia (Nolan, 1975). Because protein is the most expensive ingredient in many ruminant rations, there has been considerable interest in reducing ruminal protein fermentation. Proteins are degraded by extracellular enzymes, and these proteinases must come in contact with proteins through a n interaction involving water. If the protein goes into solution quickly, the rate of enzymatic degradation is often increased (Tamminga, 1979). Many of the proteins found in forages and soybeans are very soluble and are degraded rapidly by ruminal bacteria. Heat treatments that can denature the protein can decrease solubility and the rate of protein degradation. Some feeds contain proteins that are naturally insoluble brewers grains, distiller byproducts, fish meal, etc.). The submodel uses enzymatic data to predict the rate at which feedstuff proteins will be degraded (Krishnamoorthy et al., 1982). Carbohydrate has little effect on the rate of protein degradation by extracellular proteinases, but it can greatly affect the end product of amino acid metabolism (Russell et al., 1983). In the CNCPS, NSC microorganisms take up peptides at a rate of .07 g of peptide per gram of microorganism per hour, and this N is used for microbial protein synthesis or ammonia production (Russell and Martin, 1984; Hino and Russell, 1985). The diversion of peptides to microbial protein or ammonia is regulated by the availability of carbohydrate. When carbohydrate availability allows growth, 6 8 % of the NSC microbial protein comes from peptides and 34% comes from ammonia (Russell et al., 1983). In the absence of carbohydrate, all the peptide N is converted to ammonia. Bladen et al. (1961) examined the capacity of various ruminal bacteria to ferment protein hydrolyzate and produce ammonia. They c o n cluded that Bacteroides ruminicola was the most important amino acid fermenting bacterium in the rumen of cattle, but subsequent experiments indicated that this species could not account for ammonia production in vivo. B. ruminicola B14, one of the most active strains, had a specific activity of 13.5 nmol of ammonia per milligram of protein per minute (Russell, 19831, but mixed ruminal bacteria produced ammonia at a rate of 31 nmol per milligram of protein per minute (Hino and Russell, 1985). This comparison introduced a paradox. How could the best strains have an activity that was less than the average?

When mixed ruminal bacteria were incubated with an excess of casein and with amounts of mixed carbohydrates that were inadequate to support maximum growth rates, there was a linear decline in ammonia production a s the fermentation rate (growth rate1 increased, but 14N labeling studies indicated that 34% of the ammonia was not influenced by carbohydrate availability (Russell et al., 19831. Because B. ruminicola produces little ammonia when carbohydrate is available (Russell, 19831, this carbohydrate-insensitive ammonia production could not be explained. Recent ruminal enrichments, however, revealed three new species of ruminal bacteria (Russell et al., 1988; Chen and Russell, 1989). These strains that do not utilize carbohydrate grew on peptides and amino acids and produced ammonia 20-fold faster than other ruminal bacteria. Because their rates of protein synthesis are 10- to 25-fold lower than their rates of ammonia production and their numbers in vivo are not high, these bacteria contribute little to microbial protein production and are in most cases detrimental. Ionophores decreased ammonia production in vivo (Dinius et al., 1976) and in vitro (Van Neve1 and Demeyer, 1977; Russell and Martin, 19841, but they have little effect on proteolysis. Whetstone et al. (1981) noted that monensin caused an increase in nonammonia, nonprotein nitrogen in vitro. Previously isolated, ammonia-producing bacteria were resistant to monensin (Chen and Wolin, 1979; Dennis et al., 19811, but the newly isolated ammonia-producing bacteria are sensitive to ionophores (Russell et al., 1988; Chen and Russell, 1989). The new isolates are included in the NSC bacteria even though they are unable to utilize carbohydrate. Their contribution arises from the relationship between peptide transport and ammonia production. If only 64% of the peptides that are taken up can ever be incorporated into microbial protein, the remainder must end up as ammonia. The influence of ionophores on ruminal ammonia production can be accommodated by a 34% reduction in the peptide uptake rate constant.

Recycled Nitrogen Recycled nitrogen may be a significant ruminal input when ammonia is deficient. The NRC (1985, 1989) assumed that recycled nitrogen can be as much a s 70% of the protein intake if the dietary protein intake is low (5% CP). When the protein intake is high (20% CPI the contribution of recycled nitrogen decreases ( 1 1% of intake CP). The CNCPS uses the NRC (1985) equation for recycled nitrogen: Y = 121.7 - 12.01X + .3235X2, where Y = urea N recycled (percentage of N



intake) and X = intake of crude protein, as a percentage of diet DM. 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 intake increases to support maximal rates of milk production or gain, more amino acids may be used for gluconeogenesis (for lactose and fetal growth), the efficiency of amino acid N utilization may decrease, and there can be a n increase in recycled N. Further work (possibly a n additional submodell is needed to assess the value of recycled N as a N source for ruminal microbial growth. Recycled N


can supply ruminal ammonia, but not ruminal peptides.

Composition of Ruminal Bacteria Bacterial composition can be influenced by factors such as changes in growth rate, growth phase, and growth media. In this model, bacteria are assumed to be 62.5% CP, 21.1% carbohydrate, 12% fat, and 4 . 4 % ash on a DM basis (Hespell and Bryant, 19791. Only 50 to 70% of microbial N is available protein, and the remainder is bound in

Table 1. Glossary of terms Y

urea nitrogen recycled, 96 of nitrogen intake intake crude protein, % of dry matter percentage of NDF dry matter that is forage NDF yield efficiency of SC bacteria from the available fiber fraction of the jth feedstuff, g of SC bacterialg of SC digested yield efficiency of NSC bacteria from the sugar fraction of the jth feedstuff, g of NSC bacteria/g of NSC digested yield efficiency of NSC bacteria from the starch fraction of the jth feedstuff, g of NSC bacteria/g of NSC digested maintenance rate of the structural carbohydrate bacteria, g of SC.g of bacteria-'.h-I maintenance rate of the nonstructural carbohydrate bacteria, g of NSC/g theoretical maximum yield of the structural carbohydrate bacteria, g of bacterialg theoretical maximum yield of the nonstructural carbohydrate bacteria, g ratio of peptides to peptide plus NSC in the jth feedstuff peptides in the j* feedstuff g of NSC in the A (sugar)fraction of the jth feedstuff g of NSC in the B (starch and pectins) fraction of the feedstuff g of SC in the B2 (available cell wall) fraction in the jt feedstuff g of ruminally degraded true protein g of ruminally degraded true protein nitrogen


YZl y31




Kd4j Kd5j Kd6j


= = = =





growth rate of the sugar-fermenting bacteria, h-' growth rate of the starch-fermenting bacteria, h-' growth rate of the SC-fermenting bacteria, h-' percentage improvement in bacterial yield, 46, due to the ratio of peptides to peptides plus NSC in jth feedstuff, maximum = 18% (see Figure 21 yield of bacteria fermenting SC, from the jth feedstuff, g/d yield of bacteria fermenting NSC from the jth feedstuff, g/d yield of bacteria, from the jth feedstuff, g/d bacterial nitrogen, g/d bacterial nitrogen of bacteria fermenting SC, g/d bacterial nitrogen of bacteria fermenting NSC, g/d liquid passage rate, % / h bacterial peptide from the jth feedstuff, g/d bacterial peptide nitrogen from the jth feedstuff, g/d bacterial peptide nitrogen retained from the jth feedstuff, g/d nonprotein nitrogen intake, g/d NSC bacteria ammonia nitrogen retained from the jth feedstuff g/d SC bacteria ammonia nitrogen retained from the jth feedstuff, g/d peptide uptake rate by the NSC bacteria when ionophores are fed, .07 g peptide uptake rate by the NSC bacteria, % / g of NSC bacteria/h Ruminal ammonia nitrogen, g bacterial true protein escaping the rumen by the jth feedstuff, g/d 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 bacterial fat escaping the m e n by the jth feedstuff, g/d bacterial ash escaping the rumen by the jth feedstuff, g/d



cell wall structures and nucleic acids (Ling and Buttery, 1978; Van Soest, 1982). The CNCPS assumes that nucleic acid N is 15% of the total microbial N (Purser and Buechler, 1966). Cell wall protein is assumed to be 25% of the microbial protein (Bergen et al., 1967). The remainder is available true protein. Ruminal organisms also contain significant quantities of lipid, storage carbohydrate, and minerals (Van Soest, 1987).

Ruminal Fermentation Model The CNCPS uses the inputs of feed intake, feed composition, and the inherent rate of carbohy drate and protein degradation to calculate the lIU"lina1 ammonia and peptide production, the amounts of carbohydrate, protein, and peptide escaping ruminal degradation, and the amount of metabolizable energy that will be available to the animal; these calculations are summarized in a companion paper (Sniffen et al., 1992). See Table 1 for a glossary of terms used in the CNCPS model. In the-CNCPS, bacterial yield is dependent on the rate of carbohydrate fermentation a d , hours-'), an input, theoretical maximum growth yield CYG, grams of bacteria per gram of carbohydrate fermented), the maintenance energy coefficient (KM, grams of carbohydrate per gram of bacteria per hour), and the input, forage NDF (RFNDF, grams): if RFNDF e 20 then YG1 = YG1-1 - ((20 - RFNDF)..025)) if RFNDFe20then YG2 = YG2.1 - ((20 - RFNDF)*.O25)) I/Ylj = (KMl/Kdsj) + (I/YG1) 1/Y2j = (KM2/Kd4jl + (1/YG21 I/Y3j = (KM2/Kd5jl + (I/YG2)

Then the yield of NSC bacteria is adjusted according to the availability of ruminal peptides PEP) and the input of ruminally degraded carbohydrate (RDC): RATIOj






EXP(.4O4* LnRATIOj 100)



SCBACTj = Y'j'RDCB2j Y2j = Yzj'(1 + IMPj*.O1) Y3j = Y3j.(l + IMPj*.O11 NSCBACTj = Yzj'RDCAj + Y3j'RDCBlj The total flow of bacteria (BACT) is the s u m of NSC and SC bacteria: BACTj






1 .O * SCBACTj


In the CNCPS, the input RDPA (ruminally degradable protein) is converted to peptides, RDPEPj, a t a rate that is the inherent property of the protein (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 (see Sniffen et al., 1992): PEPUPj



If ionophores are fed the peptide uptake rate can be decreased by 34olO: KUPI = KUPq.66 peptides taken up are 160,,o PEPUPN




The CNCPS then calculates ruminal N balance. Because peptides can only provide 66% of the N to NSC bacteria, then PEPUPNR = .66 * NSCBACT when PEPUPN is =eater (.66 NSCBACTN); otherwise, PEPUPNR = PEPUPN





(RAN) is

RAN = (Y + RDPA + NPN) - (PEPUPNR + NSCAMMNR + SCAMMNR) Bacteria (both SC and NSC) escaping the rumen (REB) are 62.5% CP (of which 25% is cell wall nitrogen [CWI and 15% is nucleic acid nitrogen [NAD,2 1% carbohydrate (CHOI, 12% fat (FAT) and 4.4% ash CASH): REBTPj = REBCWj = REBNAj = REBCHOj REBFATj = REBASHj =

.6O. .625 BACTj .25 * .625 * BACTj

.15 .625 BACTj .21*BACTj

.12*BACTj .044 BACTj


The bacteria (BACTI are 10% N:

Undegraded carbohydrate and protein, peptides, and ruminal bacteria for each individual feedstuff


ply needed by growing calves. Similar results were 4 0 observed in field studies with beef and dairy cattle 0


200 100

0 p!








OBSERVED BACTERIAL N (g/dey) Figure 3. The relationship between observed flows of microbial nitrogen from the rumen and those predicted by the Cornell Net Carbohydrate and Protein System. Data were taken from Robinson and Sniffen (1985), Garret et al. (19871, Glenn et al. (1989),McCarthy et al. (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 observed in lactating dairy cows, whereas the low N flows were from trials with steers.


pass out of the rumen. Their absorption is discussed in companion papers (Fox et al., 1992; Sniffen et al., 1992).

Evaluation The literature contains an abundance of performance data, but few studies have 11 described the diets so that the rates of carbohydrate fermentation and protein degradation can be estimated with accuracy and 2) reported the flow of microbial protein and undegraded feed to the lower gut. Thus, only certain studies could be used to validate the model (Robinson and Sniffen, 1985; Garrett et al., 1987; Glenn et al., 1989; McCarthy et al., 1989; Song and Kennelly, 1989). These studies used Holstein cows and steers that were fed corn, barley, and silage-based diets at various intakes. The regression coefficient of the observed and predicted BACTN was .94 k2 = .881 and the slope was .94. The slightly negative intercept (-12 g of N/ d) indicates that the model is biased toward a n underprediction of BACTN, but this bias is minor (Figure 3). We have used this submodel to design feeding experiments and evaluate the results (Fox et al., 1990b). The model correctly predicted metabolizable protein (microbial plus undegraded feed) sup-

(Fox et al., 1990a). We are encouraged by these results, and feel that the CNCPS can account for the effect of variations in feed carbohydrate and protein fractions on microbial yield, feed protein escaping ruminal degradation, and carbohydrate utilization in the rumen. Any scheme of ration formulation depends on an accurate and meaningful description of dietary ingredients. Representative feeds are listed in a companion paper (Sniffen et al., 18921, but the user may encounter feeding situations in which an appropriate comparison is not readily available. 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, indicates that the model can still be valuable. The user can use present performance to estimate and validate the inputs. After the inputs have been validated, the user then evaluates the proposed changes to see whether the potential allocation of diet is beneficial or detrimental.

Implications Traditional schemes of ration balancing for ruminants have been based on equations that have attempted to predict nutrient availability and animal productivity. In many cases these empirical equations have not provided a n accurate representation of ruminal fermentation and nutrient availability. The Cornel1 Net Carbohydrate and Protein System has a fermentation submodel model that compares the rate of carbohydrate fermentation with the rate of protein and predicts the ruminally digestible organic matter, microbial protein synthesis, ammonia production, and the flow of undigested feed to the lower gut. Based on the evaluations, it seems that this submodel gives a n accurate assessment of microbial growth and fermentation end products.

Literature Cited Baldwin, R. L., J. J. Koong, and M. J. Ulyatt. 1977. A dynamic model of ruminant digestion for evaluation of factors affecting nutritive value. Agric. Systems 2:255. Bauchop, T. 1979. Rumen anaerobic b g i of cattle and sheep. Appl. Environ. Microbiol. 38: 148. Bergen, W. G., D. B. Purser, and J. H. Cline. 1967. Enzymatic determination of the protein quality of individual rumen bacteria. J. Nutr. 92:357. Bird, S.H., and R. A. Leng. 1978. The effects of defaunation of the rumen on the growth of cattle on low-protein high energy diets. Br. J. Nutr. 40:163. Bladen, H. A., M. P. Bryant, and R. N. Doetsch. 1961. A study of bacterial species from the rumen which produce ammonia from protein hydrolyzate. Appl. Microbiol. 9:175.



Bryant, M. P. 1973. Nutritional requirements of the predominant rumen cellulolytic bacteria. Fed. Proc. 32:1809. Chen, G.. and J. B. Russell. 1988. Fermentation of peptides and amino acids by a monensin-sensitive ruminal peptostrep tococcus. Appl. Environ. Microbiol. 54:2742. Chen, G., and J. B. Russell. 1989. More monensin-sensitive, ammonia producing bacteria from the rumen. Appl. Environ. Microbiol. 55:1052. Chen, G., J. B. Russell, and C. J. Sniffen. 1987a. A procedure for measuring peptides in rumen fluid and evidence that p e p tide uptake can be a rate-limiting step ruminal protein degradation. J. Dairy Sci. 70:1211. Chen, G., C. J. Sniffen, and J. B. Russell. 1987b. Concentration and estimated flow of peptides from the rumen of dairy cattle Effects of protein quantity, protein solubility and feeding frequency. J. Dairy Sci. 70:983. Chen, M., and M. J. Wolin. 1979. Effect of monensin and lasalocid-sodium on the growth of methanogenic and Nmen saccharolytic bacteria. Appl. Environ. Microbiol. 38~72. Citron, A., A. Breton, and G. Fonty. 1987. Rumen anaerobic fungi. Bull. Inst. Pasteur 85:329. Coleman, G. S.1980. Rumen ciliate protozoa. Adv. Parisitol. 18: 121.

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A net carbohydrate and protein system for evaluating cattle diets: I. Ruminal fermentation.

The Cornell Net Carbohydrate and Protein System (CNCPS) has a kinetic submodel that predicts ruminal fermentation. The ruminal microbial population is...
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