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OCR for page 37
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.
OCR for page 38
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.
OCR for page 39
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,
OCR for page 40
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
OCR for page 41
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
OCR for page 42
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
OCR for page 43
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.
OCR for page 44
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OCR for page 45
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.
Representative terms from entire chapter:
microbial growth
