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Ruminant Nitrogen Usage (1985)

Chapter: 6 Microbial Growth

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Suggested Citation:"6 Microbial Growth." National Research Council. 1985. Ruminant Nitrogen Usage. Washington, DC: The National Academies Press. doi: 10.17226/615.
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Suggested Citation:"6 Microbial Growth." National Research Council. 1985. Ruminant Nitrogen Usage. Washington, DC: The National Academies Press. doi: 10.17226/615.
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Suggested Citation:"6 Microbial Growth." National Research Council. 1985. Ruminant Nitrogen Usage. Washington, DC: The National Academies Press. doi: 10.17226/615.
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Suggested Citation:"6 Microbial Growth." National Research Council. 1985. Ruminant Nitrogen Usage. Washington, DC: The National Academies Press. doi: 10.17226/615.
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Page 40
Suggested Citation:"6 Microbial Growth." National Research Council. 1985. Ruminant Nitrogen Usage. Washington, DC: The National Academies Press. doi: 10.17226/615.
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Page 41
Suggested Citation:"6 Microbial Growth." National Research Council. 1985. Ruminant Nitrogen Usage. Washington, DC: The National Academies Press. doi: 10.17226/615.
×
Page 42
Suggested Citation:"6 Microbial Growth." National Research Council. 1985. Ruminant Nitrogen Usage. Washington, DC: The National Academies Press. doi: 10.17226/615.
×
Page 43
Suggested Citation:"6 Microbial Growth." National Research Council. 1985. Ruminant Nitrogen Usage. Washington, DC: The National Academies Press. doi: 10.17226/615.
×
Page 44
Suggested Citation:"6 Microbial Growth." National Research Council. 1985. Ruminant Nitrogen Usage. Washington, DC: The National Academies Press. doi: 10.17226/615.
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Page 45

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Microbial Growth INTRODUCTI ON Microbial flow from the rumen can meet 50 percent or more of the amino acid requirements of ruminants in various states of production (0rskov, 1982~. Therefore, it is important to understand the total rumen microbial ecology and factors affecting it. Microbial growth is a pivotal point in any ruminant protein system. There is an optimum balance between requirements for microbial growth and substrate avail- ability. The optimum is dictated, in part, by utilization of degraded protein and carbohydrate from any of the foodstuffs or ingredients used in diets. Protein degrada- tion in the rumen, in some cases, exceeds carbohydrate availability, and protein wastage occurs. In others, the reverse is true, and digestion of carbohydrate in the ru- men is reduced. Generalized schemes of carbohydrate and protein degradation have been presented (Russell and Hespell, 1981), and Figure 1 contains an overall protein scheme. The focus for defining microbial growth is to under- stand the substrate being fermented, the organisms fer- menting this substrate, and microbial requirements. MAJOR CLASSES OF ORGANISMS IN THE RUMEN Hungate (1966) and Russell and Hespell (1981) have described the rumen microbial genera. The rumen eco- logical system is complex and not entirely understood. The population is diverse with interdependence of vari- ous types of organisms (Meers, 1973~. Rumen bacteria can be divided into three major classes based on substrate affinity: cell wall digesters, general (those that can digest both cell wall and cell con- tents) digesters, and cell contents digesters. The last two 37 categories may include those bacteria that can be classed as secondary fermenters, i.e., those that utilize substrate from primary fermenters. Russell and Hespell (1981) recently outlined the dis- tribution of fermentation niches and major fermenta- tion products of some major rumen bacteria. Few bacte- rial species have proteolytic capability, and a few species are responsible for most of the digestion of cellu- lose. As a result, the composition of the diet can alter the rumen ecology and influence microbial growth, total microbial mass, and extent of dry matter digestion. In general, an increase in any one component of the sub- strate, particularly nonstructural carbohydrate, could result in proliferation of the digesting organism, usually at the expense of other species. Protozoa are divided physiologically into two major subclasses: flagellates and ciliates (Hungate, 1966; Rus- sell and Hespell, 1981~. Flagellates occur in young calves shortly after feeding but decrease as animals age. The major protozoa! population ire adults is ciliate, which is subdivided into two major groups: holotrichs and oligotrichs. Holotrichs are relatively simple, similar to paramecia, (Hungate, 1966; Russell and Hespell, 1981) and usually belong to the Dasytricha or Isotricha genera. Oligotrichs are more complex with surface pro- jections, cilia, and skeletal plates. Example species are Entodinia and Diplodia (Hungate, 1966~. Their role in the rumen is poorly understood. They engulf bacteria and feed particles and may influence proteolysis and re- cycling of bacterial nitrogen (Leng and Nolan, 1984) and delay starch metabolism (Hungate, 1966~. Bacterial populations are usually in the range of 107- 109/ml of rumen fluid and protozoa are 102-106/mI. Since individual protozoa mass may be 103 times that of bacteria, the total ruminal mass of protozoa in the ru- men may equal that of bacteria.

38 Ruminant Nitrogen Usage BACTERIAL NUTRIENT REQUIREMENT Bacterial growth can be rapid (doubling times range from 14 minutes to 14 hours), and the rate is a partial function of the availability of substrate at any given time interval (Bergen et al., 1982~. Their nutrient re- quirements are complex and dynamic and are a function of the microbial maintenance requirement as well as the requirement for growth (Russell and Hespell, 1981~. Growth for animals is normally described as change in mass per unit of time. At steady-state conditions in the rumen, bacteria grow or multiply at a rate only suffi- cient to replace those passing out of the rumen or lysing, since at steady state the population of cells remains con- stant. Growth rate, as measured by turnover of isotope labels, is an index of the rate at which cells are replaced, and gross yield from the rumen is the multiple of the replacement rate and the population in the rumen. Net yield, in contrast, is the multiple of dilution rate of mi- crobes and population in the rumen. The difference is cell lysis. Yield also is commonly calculated as the multi- ple of substrates use and Ys - Ys is yield per unit of sub- strate fermented. This can be further fractioned into a YS aX times substrate minus a maintenance coefficient times the population. If yield equals Ys and also equals population times dilution rate, then population equals Ys dilution rate. If dilution rate and Ys are constant, as under steady-state conditions, then population becomes a function of substrate available per unit of time. The amount of ATE per unit of substrate fermented may dif- fer with different types of bacteria. ATE yield and the maintenance coefficient and turnover rate determine the efficiency of microbial growth. Microbial cell composition has been demonstrated to vary considerably (Hungate, 1966; Hespell and Bryant, 1979), depending on many factors, including microbial type, growth phase, and rate of nutrient availability. Table 7 illustrates some of the variation in cell composi TABLE 7 Composition of Microbial Cellsa High Polysaccharide Maintenance/ General Low Turnover High Protein Lipid 4 x Maintenance/ High Turnover 65.0 8.0 1.0 12.0 7.6 2.0 4.4 Protein RNA DNA Lipid Polysaccharide Peptidogylcan Ash 47.5 11.4 3.4 7.0 12.3 14.0 4.4 47.5 8.0 1.0 7.0 30.1 2.0 4.4 aHespell and Bryant (1979). lion of bacteria in different metabolic states. The high- polysaccharide-content group represents those species that grow at slow rates and may be inefficient due to energetic uncoupling (Hespell and Bryant, 1979~. Un- coupling may occur in the rumen of animals fed at or below maintenance, fed low-nitrogen diets high in non- cell wall material, or with diurnal fluctuations of limit- ing nutrients, especially protein Respell and Bryant, 1979; Cotta and Russell, 1982~. The Table 7 high- protein composition represents bacteria in the rapid growth phase, specifically bacteria that have adequate substrate and nutrient supply. These data are in vitro and caution should be used in their application. Nutrient requirements could be expressed in terms of rate of microbial growth and microbial type. It is diffi- cult to consider bacterial type because of the range in maintenance requirement and variation in nutrient re- quirements. Carbohydrate Microbes can be classified according to substrate spe- cificity (Russell and Hespell, 1981~. The microbial mass can be divicled into two major categories: primary and secondary fermenters (Van Soest, 1982~. The primary fermenters degrade the complex cell wall, starch, and sugars. The secondary fermenters utilize the products produced by the primary group. Cell yield may not par- allel the amount of carbohydrate fermented (Hespell and Bryant, 1979; Russell et al., 1983) when factors nec- essary for growth are absent or when some factor in- creases the maintenance cost. The major available carbohydrate fractions of plant cell wall are cellulose, hemicellulose, and pectin. Non- cell wall carbohydrates are primarily starch, fructosans, and sucrose. Insoluble and partially unavailable cellu- lose and hemicellulose constitute from 15 to 66 percent of most diets of ruminant animals. Although it is a part of the cell wall, pectin along with the soluble carbohy- crate is rapidly and completely fermented, while starch is the primary insoluble storage carbohydrate that is sus- ceptible to rumen escape. The objective in feeding ruminants is to obtain a rate of digestion of the complex carbohydrate substrate to maximize nutrient intake and availability of nutrients from the rumen and the lower tract. Digestion is maxi- mized in an ecosystem balanced in acidity, nutrient availability, and fermentation products both within and among microcolony niches. Due to methane and heat losses that accompany fermentation in the rumen, energetic efficiency may favor small intestinal over ru- minal digestion of nutrients, but certain nutrients are poorly or not digested in the small intestine.

Microbial Growth 39 Protein or Nitrogen Microbial nitrogen requirements vary qualitatively. Many fiber digesters require ammonia and may require branched chain C4 and Cs acids for protein synthesis and growth (Hungate, 1966; Johnson and Bergen, 1982; Russell and Sniffen, 1984~. Amino acids appear mildly stimulatory to a few organisms such as Ruminococcus albus, R. fZavefaciens, and Megasphera elsdenii (Bryant and Robinson, 1963, Hungate, 1966; Maeng and Baldwin, 1975; Maeng et al., 1975; Leibholz and Kella- way, 1979; Russell et al., 1983~. The starch, sugar, and secondary fermenters also require ammonia. However, there are several species such as Streptococcus bovis for which amino acids and possibly short peptides are essen- tial (Cotta and Russell, 1982~. Amino acids and branched chain volatile fatty acids are required by cellulolytic bacteria in vitro, but crossfeeding can meet this need in the rumen under most circumstances (Hume, 1970; Stewart, 1975; Chalupa, 1976, Russell et al., 1979~. The amount of ammonia required for microbial growth has been researched, modeled, and reviewed ex- tensively (Nolan et al., 1972; Thomas, 1973; Satter and Roffler, 1975, Smith, 1975, 1979; Mehrez et al., 1977; Baldwin and Denham, 1979; Kennedy and Milligan, 1980; Schaefer et al., 1980; Beever et al., 1980, 1981; Black et al., 1980-1981; Kang-Meznarich and Brod- erick, 1981~. Mehrez et al. (1977) suggested that an am- monia concentration of 20 to 22 mg NH3-N/100 ml ru- men fluid was needed to maximize rate of barley dry matter fermentation. Lower values are suggested to be adequate by other workers based on in vitro data. (Sat- ter and Slyter, 1974) and by 0rskov (1982) for highly fibrous diets. Poos et al. (1979a) suggested that maxi- mum digestion and intake depend upon larger fluxes of ammonia because of greater quantities of fermentable organic matter in dairy cows fed total mixed rations. It is suggested that the requirement for ammonia is di- rectly related to substrate availability, fermentation rate, microbial mass, and yield (Hespell and Bryant, 1979; Russell et al., 1983~. Methyl amine may also play a role in ammonia uptake by microorganisms (Hill and Mangan, 1964~. Vitamins and Minerals Vitamin requirements have been outlined by Hungate (1966) and others (Scott and Dehority, 1965~. Generally, many of the organisms require biotin, PABA, thiamin, folic acid, and riboflavin. Recent results would suggest that nicotinic acid may, under cer- tain conditions, improve the efficiency of microbial growth (Bartley et al., 1979; Schaetzel and Johnson, 1981~. However, these studies neecl corroboration. Crossfeeding should supply the B vitamins necessary for bacterial growth in most feeding conditions. Mineral requirements have commonly been consid- ered for only the host animal with the exception of sulfur ancl cobalt. Bacterial requirements can be large (Am- merman and Miller, 1974; Spears et al., 1978), espe- cially when one considers the requirements in terms of the dynamic microbial growth (Thomson et al., 1977~. It is important that minerals like phosphorus (2 to 6 per- cent of cel1 dry matter) and sulfur for synthesis of sulfur amino acids (Hume and Bird, 1969) be available during rapid microbial growth. PROTOZOA Protozoa in the rumen ecosystem consume particulate cellulose, peptides, starch (which delays fermentation of non-cell wall constituents), and bacteria (Coleman, 1975; Delfosse-Debusscher et al., 1979; Demeyer and Van Nevel, 1979; Vogels et al., 1980~. Protozoa have a division time of about 15 h. If the environmental condition in the rumen is such that there is a high rumen turnover or the coarse particulate mat- ter of the upper layer in the rumen is reduced, such as through the feeding of fine particle substrate, the popu- lation will be reducec3 through washout (Whitelaw et al., 1972~. Although as much as 50 percent of the micro- bial protein in the rumen may be in protozoa, they only constitute 20 to 30 percent of the microbial nitrogen flowing to the small intestine, which may be mostly the small ciliate protozoa (Leng and Nolan, 1984~. Oldham (1984) suggests that at higher levels of intake in dairy or beef cattle where the particle size in the rumen is smaller, due to a smaller particle size in the diet, and solid and liquid turnover is greater, there could be in- creased washout of protozoa and subsequently smaller rumen populations under typical feeding situations. If, however, animals are fed continuously, this would not be the case. Protozoa are sensitive to pH, and if rumen pH is outside the range of 5.5 to 8.0 (optimum pH 6.S) for extended periods, these populations will be reduced (Hungate, 1966~. Holotrichs rapidly assimilate soluble sugars that are stored as starch. In contrast, entodiniomorphs ingest starch and particulate matter. There is evidence that en- todiniomorphs can digest cellulose, although this activ- ity may be the result of residual enzymes produced by consumed bacteria. Protein requirements are met variously by ingestion of peptides, preformed protein, amino acids, and, to a smal1 degree, ammonia or possibly urea (Hungate,

40 Ruminant Nitrogen Usage 1966~. Protozoa and some bacteria are actively pro- teolytic and will digest protein and release ammonia. The nutrient requirements of protozoa are poorly de- fined. It could be assumed that the requirements are proportional to composition. Research is needed in this area. SPIROCHETE S Spirochetes have recently been characterized in the rumen (Paster and Canale-Parola, 1982~. They have been found to vary from 105 to 4 x 106 cells/ml of rumen fluid. Thirteen strains were characterized. They were shown to utilize hydrolysis products of plant polymers. They do not ferment amino acids. It was concluded that these organisms do contribute to the breakdown of plant polysaccharide material. FUN GI Fungi have also been recently identified in the rumen (Bauchop, 1981; Akin et al., 1983) as having a signifi- cant role in fiber digestion. Bauchop (1981) suggests that the concentration of fiber in the ration is positively correlated with fungal concentration. It was clemon- strated that the fungi preferentially colonized the ligni- fied cells of blade sclerenchyma (Akin et al., 1983) . Further studies are needed with spirochetes and fungi to determine nutrient requirements (Akin et al. t1983], have shown a positive sulfur response by fungi), the in- teraction with the bacterial and protozoa! mass, the dietary and environmental conditions under which they thrive, and their significance in the extent of organic matter digestion in the rumen. MICROBIAL GROWTH AND FLOW Microbial growth will be discussed in three contexts: microbial efficiency, microbial mass, and microbial flow. Efficiency and mass are dependent on the specific substrate available for fermentation in the rumen, pat- tern, composition and rate of substrate availability, and environmental factors. Microbial flow is dependent on rumen volume/passage and particle size relationships. Most reviews of microbial efficiency have considered YATP (microbial celIs/mole ATP), protein or N/unit of fermentable organic matter, or mole of hexose fer- mented (Hespell and Bryant, 1979; Smith, 1979; Stern and Hoover, 1979; Steinhour and Clark, 1982~. These terms are most appropriate in chemostats and possibly studies conducted with small particle diets fed fre- quently (Hungate, 1966~. The factors affecting microbial efficiency are numer- ous and complex and are beyond the scope of this discus sion. Reviews and discussions of concepts and equations have been presented elsewhere (Bauchop and Elsden, 1960; Pittman and Bryant, 1964; Pirt, 1965; Forrest and Walker, 1971; Stouthamer, 1973, 1979; Stouthamer and Bettenhaussen, 1975; Hespell and Bryant, 1979; Roels, 1980; Bergen et al., 1982~. Growth and its limita- tions can first be defined in terms of the maintenance requirement (Pirt, 1965~. The maintenance require- ment varies (Hespell and Bryant, 1979) among various bacteria. The impact on net microbial growth can be significant. The maintenance requirement is the net di- version of energy anchor carbon from growth-limiting (or energy-generating) substrate to processes not result- ing in an increase of cell mass. The maintenance re- quirement is both growth dependent and independent. The term YATP describes the theoretical yield in bacte- rial dry cells per mole ATE produced. Bauchop and Els- den (1960) originally suggested that YATP was relatively constant and proposed a value of 10.5. Hespell and Bry- ant (1979) have suggested that YATP COULD approach a maximum of 26 for a mixed rumen microbial population at an infinite growth rate. Since some of the ATE is used for maintenance, observed yields have been substan- tially less than this maximum and quite variable (Hes- pell and Bryant, 1979; Stern and Hoover, 1979; Van Soest, 1982~. This can be attributed to the high cost of maintenance, especially at low growth rates. The high cost of maintenance may be due partly to energetic un- coupling that can be influenced by nutrient imbalances and by environmental factors such as ionic concentra- tion and H + concentration. In a dynamic ecosystem, nutrients, such as branched chain VFA, ammonia, and amino acids, might be limiting at certain times after feeding. Microbial efficiency as expressed in chemostat studies must be used in in vivo experiments with caution. Effi- ciency as reviewed by Bergen et al. (1982) is a rate of yield per unit of substrate in the rumen. The extent of substrate disappearance is coupled with the efficiency of yield and results in the microbial mass in the rumen at any time, which is yield per substrate. The rate of growth for any bacterial niche is a function of balanced substrate and nutrient availability per unit time. The pool size of microbial mass in the rumen is modified by liquid and solid outflow and protozoa predation. Ani- mal measurements provide estimates of the flow of mi- crobial matter to the abomasum or duodenum. The data are expressed as yield per substrate apparently or truly fermented. This is not a measurement of true effi- ciency, rather it is a measure of microbial wash-out and the amount of microbial matter recycled in the rumen. These interactions can be described by Michaelis- Menten kinetics (Bergen et al., 1982; Van Soest, 1982~. Oldham (1984) suggested that microbial efficiency can

Microbial Growth 41 be estimated by the equations originally derived by Pirt (1965) and summarized by Bergen et al. (1982~. He fur- ther suggested that the microbial outflow be divided be- tween that flowing with solids ant] that with liquid. Minato et al. (1966) presented and Oldham (1984) re- viewed evidence that a significant proportion of the mi- crobial population is associated with the particulate matter leaving the rumen. Oldham (1984) proposed the following equation: Km = Ps Ks + Pi Kit where Ps and Pi are the proportions of microbial popula- tion associated with the solid and liquid fractions, re- spectively, and Ks and Kit are the fractional outflow rates for solicis and liquids, respectively. This concept provides a biological and dynamic basis for predicting microbial flow. Unfortunately, the in viva studies mea- suring rumen microbial efficiency are minimal and the predictability of flow of liquid and solids is relatively low (Evans, 1981a,b). Forage intake has been shown to improve microbial flow (summarized by Johnson and Bergen, 1982; Van Soest, 1982~. This may be caused by the combination of increased saliva flow, increased liquid turnover (in- creased small particle washout with attached bacteria), and increased pH, which could improve the ruminal en- vironment, reduced total ruminal maintenance cost (older microbes being washed out), and a more juvenile population where the maintenance requirement is a small proportion of total requirement at high growth rates (Russell and Hespell, 1981~. Rumen dilution rate has been shown to have a signifi- cant impact on microbial flow (Ibrahim and Ingalls, 1971, Harrison et al., 1976; Kennedy et al., 1976; Ken- nedy and Milligan, 1978; Hartnell and Satter, 1979; Rogers et al., 1979; Bergen et al., 1982; Van Soest et al., 1982~. Data summarized by Van Soest et al. (1982) em- ploying the Michaelis-Menten relationship with in vivo and in vitro data give the average equation: 1/y = 0.14 ~ 0.015~1/x), (R2 = 0.76~. In this equation y = g ru- men microbial N/100 g organic matter fermented in the rumen adjusted for microbial incorporation of nutrient organic matter and x = fractional rumen liquid dilution rate. This equation was derived in part from steady- state data with animals at maintenance and resembles chemostat data (Van Nevel and Demeyer, 1976~. Ex- trapolation of this equation beyond these data is not ad- vised as these conditions need further verification. Leng and Nolan (1984) have suggester] that 30 to 50 percent of the total flux of ammonia was recycled through pathways within the rumen (ammonia other nitrogenous compounds ~ ammonia). The ni- trogen can come from lysed bacteria due to activity of bacteriophages and mycoplasmas and cell death. The latter can occur by starvation, especially under mainte- nance-fed or meal-fed conditions. A significant amount of recycling can occur through protozoa! predation of bacteria (Coleman, 1975~. Gen- erally, there is an inverse relationship between ruminal protozoa and bacteria concentrations. Coleman (1975), based on in vitro studies, suggests that more than 108 bacteria can be ingested per hour by the protozoa! mass. Leng and Nolan (1984) feel that this is probably exces- sive. It is suggested that recycling of bacterial N will be higher in conditions of lower intake where forage makes up a significant part of the diet. Also, recycling would be significant when animals are consuming diets multi- ple times per day or other conditions contributing to lower turnover rates, reduced washout of particles and microbial mass. More work is definitely needed in this area. Recycling of bacterial nitrogen must be taken into account in any estimate of microbial flow. In order to predict microbial flow on efficiency it is necessary to know the amount of organic matter fer- mented. Johnson and Bergen (1982) have reviewed some of the recent literature. Their summary would suggest that processing, feed type, intake level, amount of forage consumed, and animal type may affect the ex- tent of organic matter fermentation in the rumen. Microbial How has been determined in many experi- ments in sheep, beef cattle, and dairy cattle. These data are shown in Appendix Table 3 and are, in part, from the summary of Johnson and Bergen (1982~. Extensive measurements have been made with sheep at or near maintenance. Fewer measurements have been made with beef cattle. These measurements have been ob- served under a broad range of feeding conditions and processing methods Johnson and Bergen, 1982~. Dairy cattle data are limited in number and source but rela- tively high intakes have been achieved. Data are presented in Appendix Table 3 and regres- sion summaries in Table 8. Estimates of TDN were made based on chemical analyses and ingredient com- position (NRC, 1982~. Diet DE (Meal/kg DM) = 0.04409 TDN according to NRC (1982~. Flow of micro- bial protein for the combined data set is correlated with dry matter intake (r2 = 0.65~. Slopes are similar for sheep and dairy cattle, but not beef cattle. Dry matter intake will influence not only the quantity and possibly the type of substrate available for synthesis of microbial protein, but also various ruminal parame- ters such as pH and dilution rate and microbial determi- nants such as bacterial dilution rate, protozoa! presence and bacterial numbers, distribution, and lysis in the ru- men. These factors should be studied independently so that individual components can be used in predictive equations. Unfortunately, these factors cannot be eval

42 Ruminant Nitrogen Usage TABLE 8 Recressions for Dairy Cattle~ Sheep' and Beef Cattle Model Regressions R~ S.E. B. S.E. B ~S.E. . Dairy regressions BY (gN/d) DMI (kg/d) .735-33.84 12.00 17.62 .98 OMI (kg/d) .739- 28.49 11.60 18.56 1.02 OMI less EE (kg/d) .754- 34.14 14.00 19.66 1.19 OMI less EE & Lignin (kg/d) .737-51.09 18.87 24.24 1.82 DEI (Meal/d) .77431.86 10.74 5.92 .29 TDNI (kg/d) .774- 31.86 10.74 26.12 1.30 adj. DEI (Meal/d) .762- 47.14 14.04 6.68 .38 adj. TDNI (kg/d) .762-47.14 14.04 29.47 1.71 RDOM .627- 14.23 16.38 29.05 2.27 TDOM .726- 24.21 15.99 25.55 1.75 FI & CI .742- 34.57 11.91 16.11 1.29 19.54 1.47 BY/RDOM (gN/d/kg) DMI .21216.64 2.03 .812 .160 OMI .21116.87 1.99 .854 .168 DMI less EE .25015.86 2.22 .966 .186 OMI less EE & Lignin .29814.10 2.85 1.32 .269 DEI (Meal/d) .24216.18 1.95 28.82 5.22 TDNI (kg/d) .24216.18 1.95 1.27 .23 adj. DEI .26714.39 2.27 34.49 6.20 adj. TDNI .26714.39 2.27 1.52 .27 BY/adj. DEI (gN/d/Mcal) DMI (kg/d) .127361.52 42.56 11.94 3.26 OMI (kg/d) .134360.68 41.68 12.93 3.43 Sheep regressions BY (gN/d) DMI (kg/d) .6851.61 .84 11.81 .98 OMI (kg/d) .6931.27 .85 13.18 1.06 OMI 1ess EE (kg/d) .0825.46 2.32 6.10 3.49 OMI less EE & Lignin (kg/d) .1503.76 2.36 9.92 4.04 DEI (Meal/d) .729- 1.29 .96 5.22 .39 TDNI (kg/d) .729- 1.29 .96 23.04 1.11 adj. DEI (Meal/d) .767- 2.14 .94 5.50 .38 adj. TDNI (kg/d) .767- 2.14 .94 24.26 1.68 RDOM .548- 2.01 1.49 27.57 3.04 TDOM .644- 3.21 1.49 24.45 2.4E FI & CI .729-.37 .99 13.08 .99 17.32 1.9] BY/RDOM (gN/dlkg) DMI .14217.39 1.87 7.27 2.18 OMI .14217.23 1.91 8.06 2.40 DMI less EE .108 31.34 4.85- 14.80 7.29 OMI less EE & Lignin .082 30.58 5.19- 15.53 8.90 DEI (Meal/d) .114 16.61 2.352.79 .95 TDNI (kg/d) .114 16.61 2.3512.31 4.19 adj. DEI .131 15.82 2.292.28 .93 adj. TDNI .131 15.82 2.2912.72 4.12 BY/adj. DEI (gN/d/Mcal) DMI (kg/d) .023 418.13 31.4744.78 36.50 OMI (kg/d) .025 416.08 32.5450.89 40.38 ~eeJ regressions BY (gN/d) DM~ t~cg/a) .~`u ~. ~.~o.; ~. OMI (kg/d) .533 7.14 4.228.40 .87 OMI less EE (kg/d) .445 10.35 4.827.70 1.14 OMI less EE & Lignin (kg/d) .260 18.42 5.446.56 1.47 DEI (Meal/d) .322 16.79 5.061.81 .34 TDNI (kg/d) .332 16.79 5.067.97 1.49 adj. DEI (Meal/d) .226 17.74 6.101.76 .43 adj. TDNI (kg/d) .226 17.74 6.107.77 1.91 RDOM .238 26.29 4.217.42 1.49 TDOM .281 18.24 5.347.60 1.69 FI & CI .490 12.88 4.177.36 1.10 o. ~too

Microbial Growth 43 TABLE 8 Continued Model Regressions R2 Be S.E. Be S.E. Be S.E. BY/RDOM (gN/d/kg) DMI .027 22.12 2.70 - .80 ·57 OMI .011 20.65 2.44 -.48 .52 DMI fess EE .018 20.78 2.91 -.69 .686 OMI less EE & Lignin .078 23.79 2.75 - 1.63 .741 DEI (Meal/d) .114 25.52 2.88 - .54 .20 TDNI (kg/d) .114 25.52 2.88 -2.39 .884 adj. DEI .123 26.05 2.94 -.59 .21 adj. TDNI .123 26.05 2.94 - 2.60 .92 BY/adj. DEI (gN/d/Mcal) DMI (kg/d) .0064 341.80 48.85 - 6.33 10.44 OMI (kg/d) .0032 333.56 49.49 - 4.84 11.35 DMI = Dry matter intake (kg/d) 0MI = Organic matter intake (kg/d) OMI, less EE = Organic matter intake, corrected for ether extract (kg/d) OMI, less EE & Lignin = Organic matter intake, corrected for ether extract and lignin (kg/d) DE I Digestible energy intake (Meal/d) TDNI - Total digestible nutrients intake (kg/d) adj. DEI = Digestible energy intake, adjusted for maintenance (Meal/d) adj. TDNI - Total digestible nutrients intake, adjusted for maintenance (kg/d) RDOM = Rumen digested organic matter (kg/d) TDOM = Total tract digested organic matter (kg/d) FI = Forage intake (kg/d) CI = Concentrate intake (kg/d) BY = Bacterial yield (gN/d) uated independently using the currently available data base concerning microbial protein synthesis. Indeed, the addition of digestibility of the diet to the regression equations developed did not improve the ability to pre- dict microbial yield. Further, the accuracy of predicting microbial efficiency (BY/ROMD) was very poor. Fur- ther studies are needed to evaluate these factors inde- pendently so that diet digestibility, extent of digestion in the rumen and the effect of dilution rate, protozoa! pres- ence, and pH on microbial efficiency can be determined and employed to improve efficiency of synthesis of mi- crobial protein in the rumen. The amount of organic matter fermented in the ru- men is dependent on the rate of digestion and the rate of passage (Mertens and Ely, 1979; Van Soest et al., 1979; 1982~. This would follow the equation r`_ Kd Kd + Kp where D = organic matter digestion, Kp = rate of pas- sage, and Ks = rate of digestion. As organic matter passage increases, the amount of potentially fermentable organic matter actually fer- mentecl is reduced unless Ka is very high relative to Kp. External phenomena not associated with feed type and rate of passage may alter this and, along with such events as a delay in microbial attachment to substrate, might delay digestion. Van Soest et al. (1982) have sug- gested that a time lag in digestion (due to hydration or other phenomena) also may delay passage. Johnson and Bergen (1982) adjusted the estimates of ruminally fermented organic matter for microbial mass, thus giving an estimate of truly fermented organic mat- ter, and calculated percentage of total tract digestion occurring in the rumen. For cattle trials for which total tract digestibilities were available, percent of total tract digestion occurring in the rumen was 76 + 10. They expressed the true ruminally fermented organic matter as a percent of total tract organic matter digestion. The variability of digestion in the rumen was increased, and it was suggested that this was due in large part to differ- ences in microbial yields. The largest data base is with animals fed at maintenance, and thus a true evaluation of the impact of the variation is not possible. Measurement techniques for site of digestion studies are responsible for some of the variation. Solid phase digesta markers, which are not well attached, can mi- grate from the treated particle to other particles, or move with the liquid phase. Microbial markers are a source of variance. While RNA marks bacteria and pro- tozoa, diamino-pimelic acid (DAP) identifies only bac- teria. However, all of these systems suffer from various limitations. Nevertheless, these are the systems upon which most of the data are founded.

cD CD m U) ~} CD m V) C~ o C~ bC o s" ·~ Z ~ - I=, CO ._ ~0 C~ V) o ~ O 0 m ._ C) ·_4 =- ~ a, C) ._ ._ ~ C) ¢ ~ 00C~ C ~CO _0 C~C~C~O~ 00_ ~0 ..... 11 1 eo U) 00 ~C~00 C~COC~ C ~COCO ~_C~ .... __ C~ U: __ C~0 ~0~ C~C]O ... - C~ C~_ ~ oooo~rc, ~G: .... C] ~r~c~ __C~0 .... 0 0C7 oooO ~r ~CD C~ C~ ... . . - ~00 e ~oo oo 0 00 C~ CM C]_0 0 ~_ ... _ _ C~ - CO 00C~ C] . .. . CD ~ ~ ~. - - - 1 r ~c~ C~ C~ CO C~ . . U. ~ _ 0 C~ . . == 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 - _ I 00 ~_C~C~C ~ 00 0 0 C~t_CC)c ~C ~00 o0 _ O C~ ~ C~ O C~ O _ ~ ~ C~ . . . . . . . . . . . . . . . . . . . . . . . . . . oo ~_ CC 0 ~co ~ oo c~ 0 0 ~ r 0 0 ~ 0 ca co CO ~ ~ O i ~U) CD _ ~ ~ C] ~ r" CD - C~ C~ - - ~ CC I I r- oo _ _ _ I I I I I t I I I I I I I I I I I I I I ° ~O oo ~oo C ~o _ c ~c ~ ~ ~ 0 c~ ~ ~ ~ ~ 0 0 0 r~ _ _ 0 co C ~0 0 ~0 C ~C)CD0000 ~C ~C ~a,c~c~0C53r~ . . . . . . . . ................. . ~ ~ Z C: ~O Z ~ tg ~ ~ a ~ °= ~ aZ a aZ ° a a a ~ ~ ~ ~ ~ ~~ + ~ c' a + a + a + ~ + `~ ~ + ~ ~ ~ ~ ~ ~+ + ~ ~ `~ a ~ 3 ^o 0 ° 0 ~ z ~ z ~ ~ ~ 0 ~ 0 ~ ~ ~ ° ~ ~ + ~ ~ ~ ~ ~ + ~ ~ ~ ~ + + Z ~ ~ ~ ~ ~ ~ ~ ~ ~ O ~ ~ 3: ~ 3: a 3 Z 3: ~ m [~ ~ 3 3 ~ ~ 3 ~ ~ 3 ~ ~ L~ 3 -- m + m + m + ~ + m + m + m + m + ~ + m m :~ 2 a ~ ~ m ~ m m a a ~ ~ a ~ ~m 11 11 11 11 11 11 11 11 11 11 11 11 tl 11 11 1! 11 11 11 11 11 il 11 il 11 11 44 ~, to =: ~r ;^ c ~t X ~;~ ~ tC =- _ ~he ~ _ _ ~ ~ ~ ~ o ~ ,y ,C s }- ,= o ~o o o,l _ _ ,,, ~..... Z o ~ ~ ~ ~ ~ ~ ' ~ a ~ z ~ a a a a a a ~: c . C5 . c o o ~ _ _ ~ ce . = O ~ ~:5 =to u~ bC tC~ ~ ~ C ~ ~ i~'= ~ =` :~-li m ~ ~ ~ ~ ~ a O ~ ~

Microbial Growth 45 Validity of various microbial markers also has been questioned. Various workers have reviewed and com- pared these techniques (Smith, 1979; Stern and Hoover, 1979; Steinhour and Clark, 1982~. Several workers have compared microbial markers (Harmeyer et al., 1976; Ling and Buttery, 1977; Siddons et al., 1979; Wolstrup et al., 1979; McAllan and Smith, 1983~. McAllan and Smith (1983) recently demonstrated little difference be- tween RNA and DNA microbial concentrations in mi- crobes from defaunated sheep. Bergen et al. (1982) have shown that the RNA/protein ratio increases with in- creased microbial growth rate, suggesting that a careful definition of the physiological state of the microorgan- isms being sampled is needed in order to interpret the information. Work is still needed in this area, especially to identify a primary standard from which predictive methods can be developed. Mason (1969) provides evi- dence that it might be more appropriate to estimate mi- crobial mass by difference through a combination of de- tergents and centrifugation. This approach needs more study. Several multiple regression analyses were also per- formed using both linear and quadratic models (Table 9~. Rumen models (Nolan et al., 1972, Baldwin and Denham, 1979; Black et al., 1980-1981) have inte- grated the current knowledge of the biology of micro- bial growth in the rumen. Some of the models suggest more than one microbial pool based on either substrate affinities or niches. Unfortunately, these models, al- though providing us with an improver] understanding of the mechanisms of microbial growth and flow, are not advanced enough to use in a quantitative field applica- tion model. An alternative is the use of empirical models. These are presented in Table 9. The simplest model that may have field application is one in which the parameters) can be readily measured in the field such as dry matter or organic matter intake. The regressions for the combined data are presented in Table 9. The models demonstrate the importance of the interactions between forage, energy value of the diet, and intake. Further research is needed to develop quantitative dynamic prediction models that will incor- porate measurements of diet type and processing john- son and Bergen, 1982), rumen escape estimates, and po- tential substrate degradability on various microbial niches in the rumen and flow of microbes from the ru- men. Further, it is important that studies be conducted TABLE 10 Equations Used for Predicting Microbial Yield or Efficiency Item Dairy Cattle Sheep Beef Cattlea - (Microbial N. g/TDNI, kg) Dependent Microbial N. g/d variables Independent variables Intercept - 31.9 (10.7)b TDN intake, kg 26.13 (1.3) Forage intake, kg (Forage intake, - kg)2 Concentrate intake r Independent variables Intercept NEL, Meal/d 0.77 0.73 - 30.92 (10.69) - 11.45 (0.57) 0.77 - 1.29 (0.96) 8.63 (1.67) 23.0 (1.71) 14.6 (2.84) -5.18 (1.3) 0.595 (0.8) 0.36~ aMierobial yield, gN/day = TDNI x Microbial N. g/TDNI. To be used for cattle receiving less than 40 percent of their DMI as forage. Standard error. C The use of this equation improves the prediction (r2.0.58) of m erobial flow compared to the use of TDN intake alone. to determine the interactions of N recycling in the ru- men and its importance on microbial flow to the small intestine. The interactions of intake, diet type, and rumen vol- ume with microbial efficiency in the rumen and micro- bial flow to the small intestine are complex. The present data set does not adequately allow the development of one equation that will describe these interactions for dairy cattle, beef cattle, and sheep. Separate equations are therefore recommended for each species and are summarized in Table 10. The equations for dairy cattle and sheep adequately describe microbial yield based on TDN intake. The beef equation is for rations containing less than 40 percent of forage (see Table 10) and includes forage and concentrate components. The use of TDN is suggested because it represents the largest data base that is available on foodstuffs today, and the vast majority of feed analysis laboratories base the predicted energy con- tent of feeds on an equation driven at some point by TDN. TDN is a good estimate of whole tract DOM. The equations for NEL are derived directly from TDN (NRC, 1978) and are presented for convenience.

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This book brings together the latest research on protein absorption by ruminants and takes a look at the calculation of optimum nutrient requirements, including bacterial digestion, in the calculations. It also describes the parameters of nitrogen conversion in the ruminant and examines the different kinds of protein found in animal feedstuffs. "Animal Feed Science and Technology" calls it "essential for all scientists and teachers actively working in ruminant nutrition research and instruction."

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