Microbial Growth and Flow as Influenced by Dietary Manipulations

SYMPOSIUM: PROTEIN AND FIBER DIGESTION, PASSAGE, AND UTILIZATION IN LACTATING COWS Microbial Growth and Flow as Influenced by Dietary Manipulations

C. J. SNIFFEN Department of Animal Science Cornell University Ithaca, NY 14853 P. H. ROBINSON Department of Animal Science University of Alberta Edmonton, Alberta T6G 2P5 CANADA

ABSTRACT

The accuracy of prediction of microbial growth in the rumen and flow of microbial protein to the small intestine is important in predicting protein and carbohydrate utilization in dairy cattle as well as the development of a protein and carbohydrate feeding system that will be an improvement over present systems. Empirical multiple and simple regression equations are presented that demonstrate the impact of body size, proportion of forage in the diet, and dry matter intake on flow of microbial protein into the small intestine from the rumen. Concepts are developed and validated for a mechanistic, dynamic approach for prediction of microbial growth and flow of microbial protein based on Michaelis-Menton equations, microbial substrate affinities, and rumen liquid flow kinetics. Emphasis is placed on the importance of quantifying dynamics of rumen function, the need for experimentation to develop a carbohydrate system that will include methods for analysis, and a factorial approach to digestion and utilization. INTROOUCTION

The protein synthesizing capability of the rumen microbial population was highlighted by the research of Virtanen (96), who demonstrated that the protein requirement of an average producing cow could be met by the microbial mass with urea as the only dietary N source. Research in ruminant nutrition since these studies has corroborated the importance

Received August 30, 1985. Accepted December 8, 1986. 1987 J Dairy 8ci 70:425--441

425

of the rumen microbial population both in carbohydrate digestion and the contribution of microbial protein to the ruminant's protein requirement. The ruminant receives 40 to 80% of its daily amino acid requirements from microbial protein flowing to the small intestine. It is important to be able to quantitate factors affecting flow of microbial protein and to study how these factors might be manipulated so that a more sensitive protein feeding system can be developed. Current protein feeding systems in use or proposed (56) are factorial in nature. The goal of these systems is to predict better the utilization of protein by the animal. Factors affecting protein utilization can be broadly divided into animal, dietary, and among and within day events. Each of the protein systems defines, to varying degrees in a factorial manner, animal and dietary components. Little consideration, with exception of the Cornell model (92), is given to among and within day events; thus, these systems are all, with the exception of the Cornell system, static in nature; that is, they do not include time. It becomes imperative to understand the nature and consequences of these various systems. The Cornell model (92) includes dynamic components for passage and microbial growth only. Prediction of protein flow in these new systems is primarily empirical in nature and results from regression analysis of various data sets. This approach, although valid, has significant restrictions in application on a field basis. It assumes that the animal is in steady state and that nutrient flow is constant throughout the day. This is not true, as among day, and particularly, within day changes in feeding behavior contribute to nonsteady state conditions. Unfortunately, few experiments have been designed to examine this or used the

426

SNIFFEN AND ROBINSON

proper mathematics required to quantitate changes that are the consequence of nonsteady state conditions. Dynamic, mechanistic models (2, 3, 4) will help solve this problem. Unfortunately, these models cannot yet be used in application models. We will examine microbial growth first and emphasize the factors that are suggested to influence it primarily from a theoretical view and as suggested from in vitro and chemostat studies. In the second section, we will examine microbial flow from the rumen as it is influenced by several dietary manipulations emphasizing in vivo results. These findings will be contrasted to results that might have been predicted from our knowledge of microbial growth and the apparent inconsistencies discussed. MICROBIAL GROWTH

The biochemistry of microbial growth has been reviewed (30, 31, 32, 56, 70). This discussion will be devoted to ecological and physical factors affecting microbial growth, flow, and yield. Microbial protein appearing at the small intestine is a function of microbial efficiency resulting from generation of microbial mass and its washout from the rumen. Microbial efficiency needs to be considered for the whole rumen, which reflects the changes in microbial efficiency of various bacterial types and protozoa predation. Net rumen microbial yield (efficiency) is a function of costs of maintenance and death. The relationship of maintenance, growth, and substrate availability are presented in Figures 1 and 2 (30, 60, 83). This represents the reciprocal relationship of: 1/Y = M/K + 1/Y G where Y = yield, grams of bacteria per gram carbohydrate (CHO) fermented; M = grams of CHO per gram bacteria per hour; K = growth rate per hour; and YG = maximum growth yield, grams of bacteria per gram glucose. Small changes in nutrient availability for cellulolytics can result in large changes in yield per substrate. Examples of this are isoacids, mainly isovalerate (71), or NHa limitation (35, 78). Net microbial growth for noncellulolytics, which have a high maintenance requirement, is

Journal of Dairy Science Vol. 70, No. 2, 1987

more directly influenced by carbohydrate availability. Availability of carbohydrates is variable among and within feedstuffs and is significantly influenced by processing (40). Microbial yield is appropriate for chemostat studies and describes kinetic relationships of maintenance costs, uncoupled growth, and net yield (30). Microbial yield is defined from animal experiments as measured microbial flow to the small intestine in relation to organic matter (OM) digested in the rumen. There are problems of interpretation of microbial yield data presented without correction for microbial OM flowing with undigested dietary OM. This error can be significant where undigested dietary OM residue is small and microbial OM flow is large. Theoretically it is best to make the correction, b u t in order for it to be done accurately, N concentration in bacteriai OM flow must be determined. Estimation of bacterial N:OM is not worthwhile, as it is more misleading to make an inaccurate correction

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Figure I. Effect of microbial growth rate on the yield of rumen bacteria from various feed components. These plots assume a .33 g bacteria per gram organic matter fermented theoretical maximum yield for rumen bacteria. Three different maintenance energy coefficients (gram organic matter fermented per gram bacteria per hour) indicated as "m" are shown. The range of observed fermentation rates for various carbohydrates is shown. Although the maintenance cost of cellulolytics is low, the added advantage of this efficiency may be offset by poor quality slow digesting sources. Quality of forage fiber is required to take advantage of the potential efficiency of cellulolytic bacteria. The same problems of quality also apply to sources of starch. Further limitations of certain substrates are shown in Figure 3. [Sniffen et al., (78).]

SYMPOSIUM: PROTEIN AND FIBER DIGESTION 100

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Figure 2~ Effect of different rates of digestion upon cummulative extent and time. Curve A, soluble sugar 300%/h; curve B, pectin, vegetable celluloses and branched starches, 30 to 50%/h; curve C, more crystalline starch, 10 to 20%/h (processing can have major effects on starch digestion); curve D, crystalline cellulose 3 to 5%/h with a 12 h lag; curve E, timothy cell wall 8%/h; curve F, alfalfa cell wall 12%/h. Curves E and F asymptote at limited digestibilities due to lignification. Even though alfalfa is more lignified than grass its cell wall digests at a faster rate and to a greater extent at early times than grass, crystalline starch or cellulose. These differences are partly due to the very short lag in alfalfa digestion. [Sniffen et al. (78).1

than not to make the correction at all. In most experiments in the literature it is not possible to make this correction without making unsupported assumptions about the microbial N:OM ratio. Thus, microbial yield is defined as grams microbial N/100 g OM apparently digested in the rumen (ADOM) for comparative

greatest numbers (35, 70) with a wide range of substrate affinities. Protozoa are also quite diverse, fewer in number (1 × 10s), and of about equal mass in the rumen (13, 35). Fungi are not diverse, are about equal to the protozoa numbers (1 × 105), and might possibly, under certain conditions, compose about 25% of the microbial mass (56, 82). Stewart et al. (82) presents a model (Figure 3) that elegantly provides an understanding of the interaction of fungi with bacteria and protozoa. Appreciation of the important role that fungi may play has just recently come into focus. Protozoa predation on bacteria can reduce significantly microbial flow to the small intestine. This reduces animal performance (45, 82). Predation may be beneficial in a high producing cow by reducing rapidly fermented substrate and reducing bacteria fermenting that substrate. Fungi might compete for substrate with fiber bacteria and yet provide synergistic action by fracturing fibrous material (1). Understanding these ecological interrelationships improves our ability to predict better and to control microbial growth and efficiency. Another important function other than competition and predation is in crossfeeding and H2 transfer (70, 78, 82). One example of crossfeeding is the production of lactic acid by Streptococcus boris and Magaspberia elsdennii (70). Hydrogen transfer has an important impact on microbial metabolism. Russell (68)

I

Microbial yield expressed in this way can be difficult to interpret. There can be significant recycling of microbial OM in the rumen via protozoal predation and death, which leads to underestimation of true microbial efficiency. The potential for inaccuracies increases with lower rumen turnover rate and also with conditions encouraging high protozoal concentrations. Microbial outflow efficiency, as determined in animal studies, is only a partial reflection of changes in true growth efficiency, which are the result of nutrient availability, optimum conditions for crossfeeding, and optimum washout of slowly growing bacteria. The microbial population is complex and diverse (70, 91). Bacteria are diverse and in

427

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C~[~,~Hu2c~J~.OL/N~ERS PRO- H2 ~ W LINELLA ~ ~ oou Figure 3. Principal links between fungi, ciliate protozoa, and bacteria. H = H2 transfer, P = predation, c = competition. Nutrient crossfeedingis also assumed to occur [Stewart etal. (82).] Journal of Dairy Science Vol. 70, No. 2, 1987

428

S N I F F E N AND ROBINSON

recently demonstrated this impact on cellulolytic bacterial growth and potential fiber digestion. It is appropriate to discuss microbial flow to the small intestine in terms of various dietary and feeding management factors that might be expected to impact it. This can be better appreciated if considered in terms of the rumen regions and microbial types presented. Figure 4 provides a general scheme for growth and escape from the rumen. Microbial mass can be considered in two demensions: substrate preference (15, 30, 35, 70) and region or zone/location within the rumen (Czerkawski, unpublished data). The latter concept defines four zones: 1) rumen border region, 2) free suspension, 3) shuttle compartment, and 4) closely associated with solids. The zones describe regions where bacteria and protozoa are located. Each ecosystem has a mixture of organisms, but certain organisms predominate, depending on the most prevalent substrate in the region. For example, bacteria closely associated with substrate can be cellulolytic; the bacteria in the shuttle compartment will be loosely associated and could be cross feeders. Bacteria can generally be divided into three major subclasses based mainly on substrate preference: 1) fiber, 2) starch and sugar, and 3) secondary. Protozoa do not exhibit this substrate preference and generally have a high preference for starches, sugars, and bacteria (13). If the concepts of regions and substrate preference, as well as relationships shown in Figures 1 to 4 are combined, the complexity of prediction of microbial protein contribution to the animal can be appreciated. To complicate matters further, passage of solids will be primarily from the particle pool less than 2 mm. The microbial mass, therefore, is a heterogenous pool that will be differentially washed out of the rumen depending on growth rate and regions of activity. For example, protozoa (largely in mat), as well as bacteria associated with large particles and the rumen wall, will be retained. Kinetics of microbial growth also emphasize the importance of nutrients other than protein and carbohydrates. It is often assumed that nutrients such as minerals and vitamins are always in adequate supply. Minerals, for example, sulfur, can be deficient or in excess (56) Journal of Dairy Science Vol. 70, No. 2, 1987

and change microbial efficiency. Greater research emphasis needs to be placed in this area. Increasing dry matter intake results in greater substrate flow to the rumen, which will result in greater microbial growth. As the proportion of forage increases in feed dry matter there is greater saliva flow, maintained pH, improved cation exchange capacity, improved hydration (reducing lag time), improved microbial attachment, and improved mat formation, leading to improved retention times and greater microbial growth as microbial generation time is reduced (58, 82, 91). Saliva flow also increases liquid outflow, which has been suggested to increase microbial outflow from the rumen, at least partly by decreasing the proportion of bacteria growing at slow growth rates. Carbohydrate source can also impact microbial growth. Different sources of forage fiber have been shown (91) to have various cation exchange capacities (CEC). It has been suggested that increased CEC would improve bacterial attachment rate, reducing lag and improving overall rate and extent of digestion. Alfalfa fiber has a high CEC and corn silage has a low CEC. Nonstructural carbohydrates (NSC) have different proportions of sugars, starches, and pectins. Each of these carbohydrates ferment at different rates. Starch varies widely in rate of fermentation (Figures 1 and 2) and results in a wide range of bacterial growth rates. If sugars and starches are a high proportion of the ration, NSC fermenters grow rapidly. This can result in increased microbial yield if pH is maintained

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SYMPOSIUM: PROTEIN AND FIBER DIGESTION but can have negative impacts if there is an accumulation of lactic acid, driving down rumen pH and resulting in a change in microbial ecology and dry matter intake (70, 78). Rumen acidosis is a c o m m o n result of this imbalance. Protein amount and its degradability can also impact microbial growth. The microbial population requires ammonia and peptides (14; Chen et al., unpublished data) as well as amino acids for growth. If there is a deficiency of total protein intake (11), decreased digestibility of OM can result. It is suggested that protein ingested can also be too low in degradability to provide for adequate microbial growth (78). The type of protein being degraded may also be important. For example, isoleucine is a precurser of isovalerate, which is required by some cellulolytic bacteria. It is suggested that inadequate degradable isoleucine in the diet could have a negative impact on microbial growth. Too much degradable protein can also be detrimental. If there is excess degradable protein for the carbohydrate available, bacteria will waste protein by producing excess ammonia. Also, if there is excess available NSC for available soluble protein an ecological imbalance can occur (78). Feeding management can be involved in optimal microbial growth and true microbial yield (64, 77, Robinson et al., unpublished data). Chemostat data suggests that continuous input of balanced nutrients will result in o p t i m u m yield. However, under normal management conditions the ruminant does not consume feed continuously. Controlling the order in which substrates are offered, as well as their frequency of feeding may improve microbial growth and efficiency by maintaining pH, through increased saliva flow, and stabilizing fermentation rate to optimize rumen digestion time. Washout of microbial and undigested feed OM is a partial function of liquid outflow rate. Particulate outflow rate is a partial function of the size of the small particle pool (< 2 mm) and the hydrated specific gravity of those particles. Liquid outflow rate is a function of rumen liquid volume, liquid inflow from water consumption, saliva flow, and rumen motility. Saliva flow is a function of dry matter intake and particle size, which affect rumination. Oldham (58) suggests that prediction of microbial flow is the sum of microbes attached to

429

particulate matter washed out of the rumen and unassociated microbes in liquid leaving the rumen. He suggests that at least 50% of microbial matter leaving the rumen is attached to particulate material. Data presented later in this paper could support this contention. Prediction of microbial flow is difficult. Rumen microbial efficiency needs to be known to calculate microbial pool size. This could be predicted (30, 60, 83) if rumen pool size of available carbohydrate and protein were known. To predict washout of microbial OM it may be necessary to predict rumen liquid volume and the small particle washout pool size and to relate these to such factors as intake, diet, particle size, and animal size. If research is directed to separating and quantitating these factors, it should be possible to improve prediction of microbial flow to the small intestine. MICROBIAL YIELD

Flow of microbial matter from the rumen is the result of a number of associated processes. Perhaps the most important single factor is microbial growth per se (discussed in preceding section), as it is clear that without growth there can be no escape. However, escape also incorporates parameters such as microbial recycling within the rumen, kinetics of liquid and particulate passage from the rumen, potential rate and extent of digestion of feedstuffs consumed, extent of microbial association with rumen ingesta, and interaction among and within microbial groups. A dynamic mechanistic approach should deal with each of these parameters as they are influenced by changes in conditions of intake and integrate them to describe the process in one model. This is not difficult in theory if all parameters are measured in studies that examine changes in microbial flow in response to changes in conditions of intake. However, in practice, only a few of the parameters that drive microbial, generally only bacteria, flow are commonly measured in experiments. This limits discussion of microbial yield to a dynamic, mechanistic discussion of empirical relationships observed within and among laboratories. Feed Intake

Feed intake is one of the more important parameters that affects microbial yield, because Journal of Dairy Science Vol. 70, No. 2, 1987

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1 From National Research Council (56) and oth er relationships from the same data sets.

Sheep, g N/kg ADOM

Beef cattle, g N/kg ADOM

Regression 2

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TABLE 1. Regressions relating microbial yield with intake and percentage of forage in the diet for dairy cattle, beef cattle, and sheep. 1

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SYMPOSIUM: PROTEIN AND FIBER DIGESTION it varies up to fivefold or more in dairy cattle during the course of lactation• Feed intake is generally completely confounded with stage of lactation, or age in beef cattle, and so discussion of change in intake often implies change in metabolic status. It is clear that increased feed intake results in higher flow of microbial N from the rumen. However, several studies (10, 20, 33, 85, 88) indicated no relationship between intake of a fixed diet and microbial yield. A limitation of these studies is a narrow range of intakes, generally only from 1 to 2.5% of b o d y weight (BW), and only two or three feed intakes. Other studies (23, 52, 65) have shown higher bacterial yield at higher feed intakes. Analysis of separate beef, sheep, and dairy data sets, which include most available microbial yield data (Tables 1 to 3 and Figure 5), clearly indicates higher microbial yield at higher feed intakes for dairy cattle (56). Relationships for beef cattle and sheep are not as strong and probably reflect the relatively narrow range of intakes in the observations that make up these data sets. If data of Tamminga (84), one of the larger dairy data sets available (n = 43), are recalculated and expressed as bacterial yield vs. dry matter intake for a constant diet of approximately 47% long hay, a curvilinear function is defined with a minimum microbial yield at about 10 kg of DM intake (roughly 1.8% of b o d y weight) with increased yield at higher and lower intakes. This relationship is similar in shape to that reported by Robinson (63) for high forage diets, and both are shown in Figure 6. The shape of the relationship may explain why groups that have looked at the effect of feed intakes on microbial yield in the range of intake from 1 to 2.5% of BW (cited previously) have not consistently detected an effect. Increased microbial yield at high intake may reflect increased bacterial N flow from the rumen. Robinson et al. (unpublished data) have shown that as intake increases on high fiber diets NDF turnover rate increases concomitant with a reduced ratio bacterial OM/NDF in rumen ingesta (Figure 7). This supports the suggeston of Van Soest (91) that as intake increases there is greater flow of particles from the rumen that are at an earlier stage of digestion with more attached microbes. Thus, microbial recycling is reduced concomitant with increased proportional flow of feed OM leading to increased microbial yield.

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Journal of Dairy Science Vol. 70, No. 2, 1987

432

SNIFFEN AND ROBINSON

TABLE 3. Equations used for predicting microbial yield or efficiency. Item

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.

Independent variables Intercept TDNI, kg Forage intake, kg (Forage intake, kg) 2 Concentrate intake r2 Independent variable Intercept NE1,4 Mcal/d r2

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Microbial yield, grams of N/day = total digestible nutrient intake (TDNI) X efficiency (EFFN). To be used for cattle receiving less than 40% of their DMI as forage. 2 Standard error. 3 The use of this equation improves the predication @2.58) of microbial flow compared with the use of TDN intake alone. 4 NE 1 = Net energy for lactation.

W h e t h e r such a rationale h o l d s for low f i b e r diets w i t h f e w e r large p a r t i c u l a t e s is n o t clear, and m o r e w o r k is r e q u i r e d in this area. Increased microbial yield at l o w e r intakes m a y simply reflect t h e g r e a t e r q u a n t i t a t i v e imp o r t a n c e o f e n d o g e n o u s OM s e c r e t i o n s f r o m saliva and sloughed cells, w h i c h t e n d t o depress a p p a r e n t digestion o f OM in t h e t u r e e n due to an u n d e r e s t i m a t e o f t r u e OM e n t r y t o t h e rumen.

R u m e n liquid and p a r t i c u l a t e t u r n o v e r rates are positively c o r r e l a t e d to increased intake over a w i d e range o f intakes (17, 18). T h e t u r n o v e r rate o f t h e r u m e n liquid f r a c t i o n has been considered a potential moderator of bacterial yield in vivo, particularly since Isaacson et al. (37) and Van Nevel a n d D e m e y e r (90) d e m o n s t r a t e d M i c h a e l i s - M e n t o n kinetics bet w e e n m i c r o b i a l yield and d i l u t i o n rate in vitro. K e n n e d y et al. (42) r e p o r t e d a positive Michaelis-

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Menton t y p e relationship b e t w e e n liquid t u r n o v e r rate and r u m e n microbial yield in cold stressed sheep, and Harrison et al. (28) d e m o n strated a positive correlation b e t w e e n liquid t u r n o v e r rate and r u m e n microbial yield in animals ruminally infused with artificial saliva. As a result of these findings, Van Soest et al. (92) p r o p o s e d a Michaelis-Menton relationship b e t w e e n r u m e n liquid t u r n o v e r rate and microbial yield in vivo as a p o r t i o n o f the nitrogen s u b m o d e l o f the Cornell Net Protein Model. However, subsequent a t t e m p t s to stimulate microbial yield in vivo by increased fractional t u r n o v e r rate o f r u m e n liquids have been largely unsuccessful (8, 22, 23, 25). In s o m e cases there was even a t e n d e n c y to a l o w e r yield at higher liquid t u r n o v e r rates (Figure 8). The apparent inconsistency b e t w e e n the in vitro and in vivo situation may reflect f u n d a m e n t a l differences b e t w e e n t h e m . F o r example, t u r n o v e r rate in vitro refers to t u r n o v e r of the entire contents, whereas liquid t u r n o v e r rate in vivo refers to t u r n o v e r of only the liquid fraction, that associated with the less active and smaller microbial mass. There is little evidence to suggest that stimulation of liquid t u r n o v e r rate by feeding o f buffers or infusion of artificial saliva stimulates particulate t u r n o v e r rate to a similar extent. Liquid t u r n o v e r rate m a y m o d e r a t e microbial yield in vivo by washing o u t suspension bacteria and p r o t o z o a (45). However, it is m o r e likely t h a t its apparent relationship to microbial yield reflects its close association to particulate t u r n o v e r rate (17, 18), which is p r o b a b l y a m o r e i m p o r t a n t

43 3

m o d i f i e r of m i c r o b i a l yield, as it is the particulate fraction with which the larger m o r e active bacteria f r a c t i o n is associated, as well as o t h e r factors. It seems c o m m o n l y o v e r l o o k e d that in the study of K e n n e d y et al. (42) particulate t u r n o v e r rate was stimulated in almost exactly the same m a n n e r as liquid t u r n o v e r rate. Evan's (17, 18) reviews indicate that over a wide range, t u r n o v e r of liquid and particulate fractions are closely related, but o t h e r w o r k indicates that r u m e n liquid and particulate t u r n o v e r rates m a y n o t always respond in the same m a n n e r to diet challenges under specific conditions [e.g., divergence at high f e e d intakes (Figure 9)] ( R o b i n s o n et al., unpublished data). Thus, the occasionally observed relationship b e t w e e n liquid t u r n o v e r rate and microbial yield over a wide range (42, 65, R o b i n s o n et al., unpublished data) p r o b a b l y reflects the correlation b e t w e e n factors that stimulate microbial yield and those that stimulate liquid t u r n o v e r rate rather than a cause and effect relationship. A l t h o u g h t u r n o v e r rate of b o t h the liquid and particulate p o r t i o n s of r u m e n ingesta are u n d o u b t e d l y factors that m o d i f y microbial yield, it is n o t surprising that the relationships with microbial yield are not as strong as o t h e r factors, such as the ratio of microbial to dietary OM in r u m e n ingesta, which m a y be influenced by dietary factors such as intake (Figure 7) and m a y m o d e r a t e microbial yield. There is very little evidence to suggest that microbial yield can be increased in vivo if only liquid t u r n o v e r rate is stimulated. 40-

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.03 .06 09 RUt~NLIQUIDDILUTIONRATE( I/h]

.12

Figure 8. Relationship between rumen liquid dilution rate and bacterial N yield. O- - -o Chamberlain and Thomas (8) with urea, ,- - - . Chamberlain and Thomas (8) without urea, =- - -m Hadjipanayiotou et al. (25), o- - -u Greife et al. (23), A- - -, Goetsch and Owens (22). ADOM = Apparent digestibility of organic matter. Journal of Dairy Science Vol. 70, No. 2, 1987

434

SNIFFEN AND ROBINSON 14"

2

LIQUID

10 "

~ER PATE (~h)

8•

/

/

/

/

.

NOFIBER RATE

~n._.._.~-~-

,4

6

4

5

(~t,)

i3

i 7

i q n 10 ~3 ~6 [~y MATTER INTAKE(kg/d)

; 19

22

Figure 9. Relationship between feed intake and turnover rate of liquid and NDF fractions of digesta (from Robinson et al., unpublished data.) u- - -o NDF turnover rate (based on complete evacuation of rumen contents), m-- -m liquid (Co-EDTA) turnover rate.

Forage to Concentrate Ratio

Variation in the forage to concentrate ratio is one of the most c o m m o n l y modified characteristics of practical diets. Although few studies have examined this factor, results by Chamberlain and Thomas (9) and Mathers and Miller (49) suggest that m a x i m u m bacterial yield is achieved at a b o u t 70% forage with r e d u c t i o n s at lower, but particularly at higher, inclusions. Although these observations were made with sheep consuming only 1.2 to 2.3% of b o d y weight, respectively, observations by Cole et al. (12) with steers and Oldham et al. (59) with cows fed at m u c h higher intakes s u p p o r t the relationship (Figure 10). In all these studies, " c o n c e n t r a t e " refers to a mixture of corn and barley. Analysis of beef, dairy, and sheep data sets (56), which include much of the available microbial yield data (Tables 1 to 3), generally support this pattern. A d d i t i o n of "percentage fiber in the diet" as a factor in the regression m o d e l with OM intake, or OM intake adjusted for ether extract and lignin, improves the fit, particularly for dairy and beef cattle. The decline in yield at lower a m o u n t s o f forage inclusion on high energy diets p r o b a b l y reflects higher true digestion of OM in the t u r e e n as well as reduced flow of bacterial N per se. It m a y be that diets high in concentrates do n o t allow efficient microbial growth, due to uncoupled f e r m e n t a t i o n , and inclusion of more slowly degraded forage OM allows more energy to be trapped by microorganisms. Reviews by Evans (17, 18) suggest a positive relationship Journal of Dairy Science Vol. 70, No. 2, 1987

b e t w e e n p r o p o r t i o n of forage in the diet and t u r e e n liquid and that particulate t u r n o v e r rates occurs over a wide range of forage inclusions. This w o u l d also appear to support the h y p o t h e sis of Van Soest (91) that more rapid t u r n o v e r of particulates stimulates microbial flow by washing out more heavily colonized particulates at an earlier stage of digestion. Depression in microbial yield at very high rates of roughage inclusion p r o b a b l y reflects higher bacterial recycling in the r u m e n a n d slower bacterial growth, resulting in a greater p r o p o r t i o n of energy diverted for m a i n t e n a n c e , although slower growth might also be expected to select for more efficient bacterial species. These relationships generally c o n f o u n d intake with p r o p o r t i o n of forage in the diet (i.e., low intake-high forage; high forage-low intake), and it m a y be that a c o m b i n a t i o n of high forage diets fed at high intakes m a y n o t have the same effect if increased rate of passage of forage reduces bacterial recycling and causes passage of particulates at an earlier stage of digestion with more attached bacteria. However, such a c o m b i n a t i o n is p r o b a b l y of little practical importance. T a m m i n g a (84), in a series of experiments with cows fed variable forage to c o n c e n t r a t e ratios at various intakes, observed no relationship b e t w e e n forage to concentrate ratio and bacterial yield. However, in contrast to the studies cited in which the concentrate was grain, concentates in these studies were corn-

45-

MIOL~IAL YIELD

25 (g N/ kg/0011) 15

5

1

i i 50 75 PERCENTAGEOFDIETDM AS F~RAOE

I 100

Figure 10. Relationship between proportion of the dietary dry matter (DM) intake as forage and microbial yield. #- - -# Cole et al. (12), zx-- _A Oldham et al. (59) with barley as concentrate, A- - -A Oldham et al. (59) with corn as concentrate, o- - -D Chamberlain and Thomas (9), m- - -m Mathers and Miller (49). ADOM = Apparent digestibility of organic matter.

SYMPOSIUM: PROTEIN AND FIBER DIGESTION posed of by-product ingredients with high fiber contents. In addition, the range in forage content of diets was 29 to 82%, and this is the range in which other studies observed minimal effect of forage to concentrate ratio. Source of Carbohydrate in the Concentrate

Johnson (39) suggested that a combination of rapidly and slowly degraded carbohydrate sources should support maximum microbial yield. Offer et al. (56) demonstrated this effect when he showed that increased intake of paper and wheat starch together stimulated microbial yield. Offer et al. (57) demonstrated this effect when he showed that increased intake of paper in vivo studies that have examined the influence of source of carbohydrate in the concentrate on microbiaI yield. Oldham et al. (59) found that barley grain supported a much higher bacterial yield than corn grain when fed to dairy cows at moderate intakes in 10 or 40% hay diets (shown in Figure 10). Although hay analysis was not listed, it apparently was of rather poor quality (about 6.4% crude protein), and the more rapid degradation of barley starch, vs. corn starch, may have better supported microbial growth. Voight et al. (97) found that pelleted ryegrass hay supplemented with barley supported substantially higher microbial yield than corn, but if the hay was chopped there was little difference in microbial yield. Pelleting hay increases rate of hay degradation by exposing more sites for bacterial attachment, and barley starch may have improved microbial yield by supplying a rapidly degraded source of energy to support microbial growth. In contrast to these studies, Spicer et al. (79) found no influence of sorghum vs. corn vs. barley on microbial yield in steers fed 20% forage diets. Interactions with feed intake, production, type (e.g., legume vs. grass), form (e.g., hay vs. silage), and amount of forage inclusion all modify the relative abilities of grains to support microbial growth in vivo.

435

Nitrogen in the Diet

Infusion of urea in the rumen (8) or feeding urea (43) increases microbial yield when the basal diet is deficient in N. Work reported by Hume et al. (34), Leibholz (43), and KangMeznarich and Broderick (41) indicates the response to progressive increases in urea to a low, or no, N diet is quadratic with either a plateau or decline in rumen microbial N flow, and yield, at high urea additions (Figure 11). 1 However, where the basal diet is higher in rumen degradable N, the positive response in yield and bacterial N flow is to a lower threshold of urea (15) or there may be no response at all (53). Where reported in these studies rumen ammonia N below about 100 to 150 mg/L are not associated with the highest bacterial N flow or yield. Results reported by Pisulewski et al. (61) support this conclusion but suggest that the amount of rumen ammonia associated with maximal bacterial N yield may vary among diets if urea is the primary source of dietary N. Where graded increases of soluble proteins other than urea have been examined, a similar pattern (47, 94, 95) of a tendency to lower microbial yield at higher dietary N has been observed (Figure 12). However, maximal bacterial flow from the rumen is associated with a somewhat lower rumen ammonia (about 80 mg/L) than those observed for diets where urea is the primary source of dietary N. The

El

20"

MICR~IAL YIELD

kg ADOM)

i

i

i

10

20

30

DI£T TOTAL N (g/kO DM tnUIke)

1This may reflect the need for BCVFA, peptides, or amino acids to maximize microbial growth.

Figure 11. Relationship between increasing total diet N, due to increased addition of urea, and microbial yield, z~---a Hume et al. (34), u - - - i Leibholz (43), D---~ Kang-Meznarich and Broderick (41). [Increase lowest amount due to addition of urea for Leibholz (43) and Kang-Meznarich and Broderick (41). All N from urea in Hume et al. (34).] DM = Dry matter, ADOM = apparent digestibility of organic matter. Journal of Dairy Science Vol. 70, No. 2, 1987

436

SNIFFEN AND ROBINSON 50

BAC'f~RIAL YIELD (9 N/ kg ADOrl) 15-

,0

,0

;, DIET TOTALN (g/kg DM)

Figure 12. Relationship between the dietary amount of a very rapidly degraded source of nitrogen and bacterial yield. ~- - -~ Lu et al. (47), =- - -= Veira et al. (95), D- - -D Veira et al. (94).

f i n d i n g t h a t excessive a d d i t i o n o f N f r o m u r e a or p r o t e i n is associated w i t h d e p r e s s e d b a c t e r i a l N escape a n d yield is of i n t e r e s t , p a r t i c u l a r l y b e c a u s e t h e d e p r e s s i o n o c c u r s at o n l y a b o u t 25 g N/kg DM i n t a k e , a n d this is c o n c e n t r a t i o n c o m m o n l y e x c e e d e d in l a c t a t i o n rations. In c o n t r a s t , T a m m i n g a et al, (85) f o u n d n o effect of N up to 39 g N/kg DM in l a c t a t i o n r a t i o n s o n m i c r o b i a l yield, b u t t h e p r o t e i n sources used were o f m o d e r a t e s o l u b i l i t y ( o n l y a b o u t 2 5 0 g soluble N/kg t o t a l N). If N addit i o n is f r o m less d e g r a d a b l e p r o t e i n s , m u c h higher N a d d i t i o n m a y be possible w i t h o u t d e l e t e r i o u s effects o n m i c r o b i a l g r o w t h since it is o n l y t h e f r a c t i o n of t h e d i e t a r y N released in the rumen that influences rumen ammonia.

t h e s e studies a m i n o acids l i m i t e d b a c t e r i a l g r o w t h . It a p p e a r s t o s u p p o r t in vitro e v i d e n c e to this e f f e c t (14) b u t n o t suggestions t h a t b a c t e r i a l g r o w t h is m o r e rapid in t h e p r e s e n c e o f a m i n o acids a n d / o r p e p t i d e s (52, 87). However, m u c h o f t h e w o r k cited was c o m p l e t e d w i t h a n i m a l s at relatively low i n t a k e s a n d results at h i g h e r i n t a k e s m a y be s u b s t a n tially d i f f e r e n t w h e r e p r o t e i n d e m a n d f o r milk p r o d u c t i o n or tissue s y n t h e s i s is high. It is difficult t o use t h e s e data to c o m m e n t o n effects o f i n d i v i d u a l i n g r e d i e n t s d u e to t h e variability a m o n g e x p e r i m e n t s in t h e basal diet a n d o t h e r factors. It seems likely t h a t t h o s e p r o t e i n s w i t h slow rates of d e g r a d a t i o n stimulate m i c r o b i a l yield m u c h m o r e if s u b s t i t u t e d for o t h e r s w i t h high rates o f d e g r a d a t i o n t h a n if s u b s t i t u t e d f o r p r o t e i n s w i t h l o w rates o f d e g r a d a t i o n , w h i c h are likely u n d e r c o n d i t i o n s w h e r e low a m m o n i a limits m i c r o b i a l g r o w t h . However, e x p e r i m e n t a l e v i d e n c e t o s u p p o r t this view does n o t exist, a n d m o r e e f f o r t n e e d s to b e d i r e c t e d t o t h i s area, p a r t i c u l a r l y f o r a n i m a l s at higher feed intakes. However, it w o u l d seem that definition of the probable degradability of N c o n t a i n i n g n u t r i e n t s is r e q u i r e d b e f o r e a p r e d i c t i o n of t h e value o f a p r o t e i n s u b s t i t u t e can b e m a d e .

t30-

$ 110

Protein Sources, Several studies (20, 21, 24, 26, 27, 4 3 , 45, 46, 50, 51, 72, 75, 76, 78, 89) have e x a m i n e d t h e i n f l u e n c e of e q u a l N s u b s t i t u t i o n o f p r o t e i n sources o n r u m e n m i c r o b i a l f l o w a n d m i c r o b i a l yield. In m o s t cases u r e a was i n c l u d e d as a t r e a t m e n t , a n d so it is p o s s i b l e to c o m p a r e yields o b t a i n e d w i t h various p r o t e i n s to t h a t o b t a i n e d w i t h urea (Figure 13). In spite of t h e wide variety o f diets a n d N a m o u n t s utilized, it seems clear t h a t in m o s t cases s u b s t i t u t i o n o f urea b y a p r o t e i n s o u r c e will n o t c o n s i s t e n t l y increase m i c r o b i a l yield. This reflects b o t h a t e n d e n c y to increased r u m e n OM d i g e s t i o n w i t h p r o t e i n vs. urea as t h e N source, a l t h o u g h this is b y no m e a n s always t h e case, as well as little change in r u m e n escape of m i c r o b i a l N per se. This does n o t suggest t h a t u n d e r c o n d i t i o n s o f Journal of Dairy Science Vol. 70, No. 2, 1987

m,

MICR(]6IAL YIELO (g N/ 90 kg AO~) WiTH PROTEIN AS A 70 PERCENTA6~ GF "n.IAT Wffl'l UREA 50

30

=

P

I T MTt'I ALE CAS SUBSTII1J'~DPROTEIN

( FSH

I PHT

Figure 13. Relationship between protein meal substituted for urea and microbial yield as a percentage of that measured with urea. <) - - -> Hagemeister and Pfeffer (27), • - - -> Hagemeister and Kaufmann (26), D - - ->Siddons et al. (76),1, - - -> Mercer et al. (51), v - - -> Leibholz (43),* - - -> Moller and Hvelplund (53), o - - - > Ha and Kennelly (24), .---> L i n g e t al. (46), • - - -> Firkins et al. (20), • - - -> McAllan and Smith (50). SBM = Soybean meat; RPS = rapeseed meal; ALF = dehydrated alfalfa; CAS = casein, MTM = meat meal; FSH = fish meal; PNT = peanut meal.

SYMPOSIUM: PROTEIN AND FIBER DIGESTION

437

Feeding Frequency

Ensiling

Frequency of feeding is one of the more easily modified feeding parameters, but it has been little studied as a moderator of microbial yield in vivo, although it has been reviewed recently (66, 77). It has generally been assumed that more frequent feeding should result in more efficient microbial growth by reducing diurnal fluctuation in rumen metabolite concentrations and thereby reducing uncoupled fermentation (39). Although experimental evidence to support the former is abundant (66), evidence to support the latter is limited. Beever et al. (5), Brandt et al. (7), and Robinson and Sniffen (64) reported no stimulation of bacterial N flow from the rumen with more frequent feeding. Tamminga (84) reported a large increase in bacterial N flow and yield for six vs. twice daily feeding of the concentrate portion of the diet and these increases were associated with large increases in ruminal true digestibility of OM. In contrast, Robinson (63) found increased bacterial yield efficiency with less frequent feeding of a mixed diet for cows at low feed intake and John and Ulyatt (38) reported that protozoal yield increased with less frequent feeding. It seems possible that infrequent feeding may stimulate bacterial yield if a rapid intake of feed over a short period causes rapid rumen rates of passage after feeding (Chen et al., unpublished observation) and rumen escape of particles still being actively digested and so heavily colonized by bacteria. Protozoa that enter the liquid phase immediately after feeding, in response to increased soluble substrate, may also be washed out of the tureen, resulting in a higher growth efficiency due to reduced recycling. In addition, less frequent feeding is usually associated with reduced rumen digestibility of OM (both true and apparent), and so microbial yield tends to be increased even if bacterial N escape per se is not changed. Because feeding frequency is one of the more easily modified factors in practical feeding and experimental results as to its effect on microbial yield are conflicting, it would seem that more research of its effect on microbial yield is justified. However, in practice, most measurement procedures assume steady state conditions and results could reflect procedural artifacts.

There are no studies in which the effect of silage vs. hay preservation on microbial yield has been determined where treatments are not confounded with N of the diet (72). However, Van Soest (91) noted that over a number of studies microbial yield for silage diets was generally lower than those for hay or mixed diets. It is commonly accepted that fermentation of soluble carbohydrates and protein in the silo depresses potential growth of rumen bacteria by reducing sources of rapidly fermentable substrate leaving rumen bacteria the more slowly degradable substrate. Bacteria cannot grow faster than the maximum rate of degradation of the available substrate (91). Low pH in well-preserved silages may contribute to low pH in the rumen, which has been shown to reduce rumen fermentation of fiber (55, 81). Studies that directly compare the influence of ensiling of different forages on microbial yield, particularly at different amounts of dietary inclusion, would be useful to elucidate mechanisms by which microbial yield is reduced.

Silage Preservatives

The effect of the most widely used silage preservatives (i.e., formic acid and formaldehyde) on rumen microbial yield have been little studied. Hvelplund and Moller (36) and Veira and Ivan (93) reported reduced microbial yield with addition of formic acid to clover and alfalfa silages, respectively, whereas Rooke et al. (67) found no effect for ryegrass. Part of the difference may be due to the addition rate of formic acid, as Hvelplund and Moiler (36) and Veira and Ivan (93) added formic acid at somewhat higher amounts than did Rooke et al. (67) (Figure 14). The apparent decline in efficiency with formic acid treatment may partly be an artifact of DM determination since insufficient correction of DM intake for volatiles lost in drying will underestimate OM intake. If formic acid treatment causes less production of volatiles (29), then this would have the effect of underestimating OM intake to a greater extent for the formic acid treated silage. Beever et al. (44) reported reduced microbial yield for formaldehyde-treated ryegrass silage where formaldehyde was added at 38 g/100 g total N. They concluded that this rate of

Journal of Dairy Science Vol. 70, No. 2, 1987

438

SNIFFEN AND ROBINSON of r u m e n bacteria, due to potential changes in bacterial species, as a result o f m o n e n s i n supplementation. CONCLUSIONS

YIELD

Itg A ~ I

O

5

10

15

20

FORflIC ACID ADOEO(g/kg 0fl)

Figure 14. Relationship between formic acid addition rate to silage and microbial yield. ~- - .A Hvelplund and Moiler (36) high intake, A- - -A Hvelplund and Moiler (36) low intake, o- - -D Veira and Ivan (93), =- - -= Rooke et al. (67). DM = Dry matter, ADOM = Apparent digestibility of organic matter.

addition was m u c h t o o high, as postruminal digestibility of r u m e n escape N was significantly reduced. However, it is possible that at least part of the reduced microbial yield was due to inhibition of r u m e n bacteria by free f o r m a l d e h y d e (73). Additives

Influence of i o n o p h o r e s on microbial yield in vivo is relatively unstudied. A d d i t i o n of ionophores to r u m i n a n t diets is k n o w n to influence the p r o p o r t i o n o f volatile f a t t y acids in r u m e n fluid and m a y suppress protein degradation in the r u m e n (54, 99) either by changing the bacterial species p r o p o r t i o n s (11, 16) a n d / o r the biochemical p a t h w a y s (80); J. B. Russell, personal c o m m u n i c a t i o n ) favored for ATP production. However Faulkner et al. (19) reported no influence of monensin, in the range of 0 to 36.6 p p m , on microbial yield in steers fed an 85% ground cornstalk diet. M o o r e et al. (54) f o u n d no influence of m o n e n s i n infused to the r u m e n at a b o u t 13 ppm of dietary dry m a t t e r on microbial yield of bermudagrass fed sheep. Similarly Thivend and J o u a n y (86) r e p o r t e d no influence o f lasolocid, in the range of 0 to 64 ppm, on microbial yield in sheep fed a beet pulp based diet. In contrast to these findings Poos et al (62) have r e p o r t e d a substantial decline in bacterial N f l o w in steers fed monensin. However, their use of a constant assumed bacterial marker c o n c e n t r a t i o n ignored possible differences in bacterial marker c o n t e n t Journal of Dairy Science Vol. 70, No. 2, 1987

Use of statistical empirical models to predict r u m e n microbial yield seems unavoidable for the forseeable future. However, it is i m p o r t a n t to recognize that this approach can at best yield broadly accurate predictions o f microbial escape over a wide range of c o n d i t i o n s and that predictions for any specific set of conditions m a y often be inaccurate. Clearly this is a disadvantage for feed f o r m u l a t i o n purposes where prediction for a defined set of conditions, usually for one dairy producer, is the objective. A d y n a m i c mechanistic approach to r u m e n f u n c t i o n offers t h e o n l y way to predict microbial yield zccurately for specific conditions. However, the c o m p l e x i t y of such models, and the shortage of data to drive t h e m , will severely curtail their use. Perhaps the best t h a t can be reasonably e x p e c t e d in the near future is m o d e l s that predict microbial escape based on multiple empirical e q u a t i o n s that recognize the mechanisms of r u m e n f u n c t i o n by their multiplicity.

REFERENCES

1 Akin, E. 1986. Chemical and biological structure in plants as related to microbial degradation of forage cell walls. Control of digestion and metabolism in ruminants. Page 139 in Proc. 6th Int. Symp. Ruminant Physiol., Prentice Hall, NJ. 2 Baldwin, R. L., and S. C. Denham. 1979. Quantitative and dynamic aspects of nitrogen metabolism in the rumen: a modeling analyses. J. Anita. Sci. 49:1631. 3 Baldwin, R. L., L. J. Koong, and M. L. Ulyatt. 1977. A dynamic model of ruminant digestion for evaluation of factors affecting nutritive value. Agric. Syst. 2:255. 4 Beever, D. E., J. France, and M. K. Theodorov. 1986. Modelling of rumen function, in Agriculture: new developments and future perspective in research on rumen function. A. Newman-Spencer, ed. Comm. Eur. Commun., Luxenborg. 5 Beever, D. E., D. G. Harrison, and D. J. Thomson. 1972. Determination of the quantities of food and microbial nitrogen in dodenal digesta. Proc. Nutr. Soc. 31:61A. 6 Beever, D. E., D. J. Thomson, S. B. Cammell, and D. G. Harrison. 1977. The digestion by sheep of silages made with and without the addition of formaldehyde. J. Agric. Sci., Camb. 88:61. 7 Brandt, M., K. Rohr, and P. Lebzien, 1981. Quan-

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tification of N-metabolism in the forestomachs of dairy cows. 2. The effect of nitrogen supply and feeding frequency on r u m e n N metabolism. Z. Tierphysiol. Tierernahr. F u t t e r m i t t e l k d . 46:49. Chamberlain, D. G., and P. C. T h o m a s . 1980. The effects of urea and artificial saliva on r u m e n bacterial protein synthesis in sheep receiving a high-cereal diet. J. Sci. F o o d Agric. 31:432. Chamberlain, D. G., and P. C. T h o m a s . 1979. Ruminal nitrogen metabolism and t h e passage of a m i n o acids to the d u o d e n u m in sheep receiving diets containing hay and concentrates in various proportions. J. Sci. Food Agric. 30:677. Chamberlain, D. G. , P. C. T h o m a s , and A. G. Wilson. 1976. Efficiency of bacterial protein synthesis in the r u m e n of sheep receiving a diet of sugar beet pulp and barley. J. Sci. Food Agric. 27:231. Chen. M., and M. J. Wolin. 1979. Effect of m o n e n sin and lasalocid-sodium on the growth of m e t h a n ogenic and r u m e n saccharolytic bacteria. Appl. Environ. Microbiol. 38:72. Cole, N. A., R. R. Johnson, F. N. Owens, and J. R. Males. 1976. Influence of roughage level and corn processing m e t h o d on microbial protein synthesis by beef steers. J. Anim. Sci. 43:497. Coleman, S. 1975. The interrelationship between r u m e n ciliate protozoa and bacteria. Page 149 in Digestion and metabolism in t h e r u m i n a n t . I. W. McDonald and A.C.I. Warner, ed. Univ. New England Publ. Unit, Armidale, New South Wales, Aust. Cotta, M. A., a n d J. B. Russell, 1982. Effect o f peptides and a m i n o acids on efficiency o f r u m e n bacterial protein synthesis in c o n t i n u o u s culture. J. Dairy Sci. 65:226. CottrilI, B. R., D. E. Beever, A. R. Austin, and D. F. Osbourn. 1982. The effect o f protein- and non-protein-nitrogen s u p p l e m e n t s to maize silage on total a m i n o acid supply in y o u n g cattle. Br. J. Nutr. 48:527. Dennis, S. M., T. T. Nagaraja, and E. E. Bartley. 1981. Effects of lasalocid or m o n e n s i n on lactateproducing or -using r u m e n bacteria. J. Anita. Sci. 52:418. Evans, E. 1981. An evaluation of t h e relationships between dietary parameters and tureen liquid turnover rate. Can. J. Anita. Sci. 61:91. Evans, E. 1981. An evaluation of the relationships between dietary parameters and r u m e n solid turnover rate. Can. J. Anita. Sci. 61:97. Faulkner, D. B., T. J. Klopfenstein, T. N. Trotter, and R. A. Britten. 1985. Monensin effects on digestibility, ruminal protein escape and microbial protein synthesis on high-fiber diets. J. Anim. Sci. 61:654. Firkins, J. L., L. L. Berger, G. C. Fahey, and N. R. Merchen. 1984. Ruminal nitrogen degradability and escape of wet and dry distillers grains and wet and dry corn gluten feeds. J. Dairy Sci. 67:1936. Firkins, J. L., L. L. Berger, N. R. Merchen, and G. C. Fahey. 1986. Effects o f forage particle size, level of feed intake and supplemental protein degradability on microbial protein synthesis and

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