|
Items in cart [0] |
Questions? Call 888-624-8373 |
| ||||||||||||
| Copyright © 2009. National Academy of Sciences. All rights reserved. Terms of Use and Privacy Statement |
Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 23
Feed Evaluation
The metabolizable or absorbable (AP) protein con-
cept is an attempt to improve protein feeding of rumi-
nants. It requires a descriptive separation of dietary in-
take protein (IP) into (1J a ruminally degraded fraction
(DIP) and (2) an undegraded fraction (UIP). Ideally,
this separation requires experiments with duodenally
cannulated animals, techniques to separate bacterial
and protozoa! crude protein (BCP) and undegraded in-
take protein (UIP), and measurements of total protein
flow. Although this remains the reference technique, its
complexity has stimulated research to define simpler
techniques for routine feed analysis. Tables of degrada-
tion data for various protein sources from cannulated
animals are listed as Appendix Table 4.7 by ARC (1980),
by Chalupa (1975a), and in Table 6 of this publication.
RUMINAL DEGRADATION ESTIMATION
Laboratory Procedures
The process of selecting simple techniques for routine
feed analysis is continuing. Presently, tabulated data or
several promising, but yet not generally accepted, pre-
dictive techniques are employed for various feeds. Ex-
pected degradability (DIPIP) estimates of variable so-
phistication and accuracy are presented in the Iowa and
French protein evaluation systems. Burroughs et al.
(1975b) present data on 90 common U. S. feeds. The tab-
ular data in the French system were calculated from ei-
ther solubility in salt solution or in Vitro ammonia accu-
mulation. Verite et al. (1979) present data on 32 feeds,
and Demarquilly et al. (1978) and Verite and Demar-
quilly (1978) present data on 50 feeds. Other tabulations
of solubility (Crooker et al., 1978; Waldo and Goering,
1979) and solubility and in situ rumen DIPIP (Crawford
et al., 1978) are available.
The validity of using a single tabular value for a feed
23
class is reduced as the variation with-in a feed class in-
creases. Variation in the solubility of feeds within a class
was implicit in the ranges suggested by Leng et al.
(1977~. The French system (Demarquilly et al., 1978;
Verite and Demarquilly, 1979) also uses ranges within
feed classes based on solubility. WaIdo and Goering
(1979) observed ranges in insolubility of proteins in 15
feeds assayed with four methods. Ranges also are ob-
served with dynamic techniques where four commercial
samples of solvent-extracted cottonseed meal had 37.5
+ 6.6 percent (as a standard deviation) UIP and two
samples of screw-press cottonseed meal had 62.6 + 3.7
percent UIP (Broderick and Craig, 1980~. In 16 samples
of fish meal prepared in the laboratory (Mehrez et al.,
1980), the in situ disappearance of nitrogen was 39.2 +
8.6 percent. Such large variation within a feed class sug-
gests that simple tabular values, even though estimated
with systems having high predictive value, may differ
greatly from the value of a specific feed.
The difficulty of obtaining UIPIP data with cannu-
lated animals and the variation within feed classes cause
the search for accurate predictive assays for individual
feeds to continue. A summary of the correlations among
UIPIP assays, insolubility assays, and in vivo responses is
given in Table 5. This table includes production re-
sponses in addition to UIP passage at the duodenum.
High correlations of predictive assays with production
responses should encourage further consideration, but
low correlations do not necessarily imply low predictive
value because the animals may not have had the poten-
tial to use additional absorbed amino acids. The assays
with consistently higher correlations are in situ bags
(Gonzales et al., 1979; Stern et al., 1980), in vitro am-
monia accumulation (Hagemeister et al., 1976; Siddons
and Beever, 1977), autoclaved rumen fluid (Waldo,
1977b; Waldo and Tyrrell, 1980), and certain proteo-
lytic enzymes (Poos et al., 1980a). Combinations of sev
OCR for page 24
24 Ruminant Nitrogen Usage
TABLE 5 Interrelationship Among In Vivo TABLE 5 Continued
Responses, Undegradability Assays, and
Insolubility Assays
First covariate
Second covariate n r2 Reference
Milk production
In situ bags
Growth
Fungal protease
Bromelain
Ficin
Papain
Bacterial protease
Burroughs
Sodium chloride, .15 M
Sodium hydroxide
Water, hot
Tissue nitrogen deposition
Autoclaved rumen
fluid
Nitrogen retention,
balance
Autoclaved rumen
fluid
Autoclaved rumen
fluid
Burroughs
Burroughs (forage
component)
Burroughs (concentrate
component)
Burroughs (total diet)
In viva protein
degradation
In sate bags
In Vitro nitrogen
digestion
In Vitro ammonia
accumulation
In Vitro ammonia
accumulation
Pronase
Pepsin
Pepsin
Duodenal N flow/feed N
Pepsin
In site bags
Autoclaved rumen
fluid
Burroughs
Sodium chloride
In vitro ammonia
accumulation
Autoclaved rumen
fluid
Burroughs, modified
Burroughs
Burroughs
McDougall's
Sodium chloride
Sodium hydroxide
Water
Autoclaved rumen fluid
Burroughs, modified
Burroughs
Burroughs
Burroughs
3
3
3
3
11
11
10
11
11
21
is
28
28
28
6
6
6
25
6
6
7
7
7
28
350
7 .79
9 .71-.76
9 .25-.49
9 .49-.S5
9 .34-.44
9 .46-.58
9 .32
9 .17
9 .49
9 .55
.95
4 .91
6 .99
4 .28
.04
.85
.59
.61
.44
.61
.63
.10
.56
.27
.96
.29
.44
.22
.01-.34
.01-.61
.01-.49
.98
.02-.64
.04-.45
.27
.14
.01
.04
.61
.53
First covariate
Second covariate
McDougall s
Sodium chloride, .15 M
Sodium chloride, .15 M
Sodium chloride, .15 M
Gonzalez et al., 1979
Poos et al., 1980a
Poos et al., 1980a
Poos et al., 1980a
Poos et al., 1980a
Poos et al., 1980b
Poos et al., 1980b
Poos et al., 1980b
Poos et al., 1980b
Poos et al., 1980b
Waldo and Tyrrell, 1980
Waldo and Tyrrell, 1980
Waldo, 1977b
Wohlt et al., 1976
Sniffen, 1974
Sniffen, 1974
Sniffen, 1974
Stern et al., 1980
Siddons and Beever, 1977
Siddons and Beever, 1977
Hagemeister et al., 1976
Siddons and Beever, 1977
Siddons and Beever, 1977
Siddons et al., 1976
Beever et al., 1976
Crawford et al., 1978
Crawford et al., 1978
Crawford et al., 1978
Crooker et al., 1978
Crooker et al., 1978
Crooker et al., 1978
Henderickx and Martin,
1963
Crooker et al., 1978
Crooker et al., 1978
Little et al., 1963
Little et al., 1963
Crooker et al., 1978
Crooker et al., 1978 Animal Procedures
Crawford et al., 1978
Waldo and Goering,
1979
n r2 Reference
_ .
7 <.01
7 .65
27 .69
3SO .37
7 .07
350 .59
7
7
46
27
350
350
7
350
Sodium hydroxide
Water, hot
Water
Burroughs, modified
Burroughs
McDougall's
Sodium chloride, .15 M
Burroughs
McDougall s
Sodium chloride, .15 M
Sodium chloride, .15 M
Sodium chloride, .15 M
Water, hot
McDougall's
Sodium chloride, .15 M
Water, hot
Sodium hydroxide
Water
_.
.04
.82
.97
.02
.82
.86
.82
.66
.41
< .01
.24
7 .12
Crooker et al., 1978
Crooker et al., 1978
Crawford et al., 1978
Waldo and Goering,
1979
Little et al., 1963
Waldo and Goering,
1979
Little et al., 1963
Crooker et al., 1978
Crooker et al., 1978
Crooker et al., 1978
Crooker et al., 1978
Crooker et al., 1978
Crawford et al., 1978
Waldo and Goering,
1979
Waldo and Goering,
1979
Crooker et al., 1978
Waldo and Goering,
1979
Little et al., 1963
oral procedures such as solubility and in situ (Zinn and
Owens, 1983) may be helpful.
Some special problems have been observed in the
analysis of certain feeds by some techniques. The
French system is generally based on solubility in salt so-
lution for dry feeds and pressed juice from wet fer-
mented feeds (Verite and Demarquilly, 1978~. Verite et
al. (1979) observed that such solubility data were gener-
ally well correlates! with in vitro DIPIP as measured by
ammonia accumulation as earlier observed by Hen-
clerickx and Martin (1963~. But solubility was lower
than expected for cereals, soybean meal, and beet pulp
and higher than expected for horse beans and peas based
on the general relationship to degradability. Entrapped
liquids may cause some protein solubility estimates to be
misleading. The DIPIP for corn gluten meal by in situ
bag technique was 14 percent, but by duodenally can-
nulated animals it was 45 percent (Stern et al., 1980~.
This difference occurred] because it formed a viscous
mass in the bags. In situ results will vary due to pore size
and thoroughness of washing.
Two assay procedures are being used that use animals
for more than in vitro or in situ fermentations. Klopfen
OCR for page 25
Feed Evaluation 25
stein et al. (1982) use a cattle growth assay to determine
the value of supplemental proteins relative to soybean
meal. A basal or negative control diet contains supple-
mental nitrogen as 100 percent urea while the reference
or positive control diet contains supplemental nitrogen
as 40 percent from soybean meal and 60 percent from
urea. Test proteins are substituted for soybean meal,
and a protein efficiency is calculated as the incremental
gain from the protein supplement divided by the incre-
mental protein intake from that protein supplement.
Relative values are calculated by dividing the protein
efficiencies of test proteins by the protein efficiency of
soybean meal. This is a useful transitional methocl, but
its general use as an assay would tend to ignore the in-
creasing evidence for the large variation among lots
within a feed class and effects of dietary energy level and
food intake on ruminal degradation of protein.
Danish researchers (Moller and Thomsen, 1977) use
cluodenally cannulated animals and regression tech-
niques to estimate UIP and BCP production relative to
the DM ingested. A protein source is fed at different ni-
trogen percentages in the feed dry matter, X, and the
ratio of duodenal nitrogen to feed nitrogen, Y. is related
to X by the hyperbolic regression equation, Y = a +
b/X. The constant, a, is interpreted as the fraction of
feed protein escaping degradation in the rumen. The
constant, b, is interpreted as microbial nitrogen fixation
into protein per 100 g of dry matter ingested. Variation
within feed classes and the complexity of experiments
with cannulated animals make the use of this method
unlikely as a general assay.
Since milk production responds rapidly to changes in
protein status, direct use of lactation response to assay
the need for additional UIP appears feasible anti di-
rectly applicable. Calderon Cortes et al. (1977)
abruptly changed the IF protein fed to ewes at the start
of the third week of lactation to 77 percent and again at
the start of the fourth week to 106 percent of that fed in
the second week. The corresponding changes in milk
production were 83 percent and 101 percent. The corre-
sponding changes in milk protein output were 76 per-
cent and 101 percent. Note that milk production and
milk protein output responded rapidly to both the
square wave decrease and increase in crude protein fed.
The milk production responses to short-term changes in
UIPIP observed by Gonzales et al. (1979) also support
this hypothesis.
PROTEIN INDIGESTIBILITY
At least five common feeds may contain sizable por-
tions of their protein in bound or indigestible form.
These feeds are hay-crop silages (Goering et al., 1974),
dehydrated alfalfa (Goering, 1976), citrus pulp (Am
merman, 1973), and corn distillers drier! grains and
brewers dried grains (Waldo and Goering, 1979~. The
Cornell system considers that acid detergent insolubl
nitrogen (Goering and Van Soest, 1972) is bound and
indigestible (Van Soest et al., 1982~. Pepsin insoluble
nitrogen is another possible method for determining this
fraction. Heat and chemicals that decrease the ruminal
degradation of proteins can increase the amount of
bound protein. The bound and indigestible fraction
must be subtracted from the undegradable fraction
since it does not contribute absorbable amino acids.
PROTEIN FRACTIONS AND
DEGRADATION
Protein degradation has been described as a function
of time when using in vitro and in situ fermentations or
proteolytic enzymes. Most of these data fit a general
model with three pools or fractions:
A- NPN or protein that is degraded very rapidly;
B protein that is degraded at a rate similar to the
rate of passage (0.02 to 0.07 h- i; and
C-bound or unavailable protein that is degraded
very slowly.
Theoretically, each pool or fraction has a degradation
rate that is assumed to be fractional, that is, a constant
proportion of the residue is degraded per unit of time.
The fractional degradation rates are:
k,dA-fractional degradation rate for A that may be in
the order of 10 times greater than the rate of
passage;
kdB fractional degradation rate for B that may be
between 10 times and one-tenth the rate of pas-
sage; and
kin fractional degradation rate for C that may be in
the order of one-tenth the rate of passage.
Practically, kdA is usually considered infinite and A is
considered to be entirely degraded; kdC is usually consid-
ered zero and C is considered to be entirely passed. Only
B is usually considered to be affected by the relative
rates of passage, kpB, and LAB at any time (see p. 215 of
Bray and White, 1966~. The fraction of B that is de-
graded will be kaB/ PUB + kpB) and the fraction of B that
is passed will be kpB/(kdB + kpB). The fraction of total
protein that is degraded,
D = A + kaBB/ (kaB + kpB),
and the fraction of total protein that is passed,
P = kpBB/ PUB + kpB) + C.
As fraction C is often an asymptotic residue, it may or
may not relate to bound and unavailable fraction dis
OCR for page 26
26 Ruminant Nitrogen Usage
cussed above. A time lag for bacterial attachment and
penetration by ruminal fluid may precede degradation.
Pichard and Van Soest (1977) used proteolytic en-
zymes to describe subfractions Be and B2 plus their frac-
tional rate constants. Soluble fraction A and unavail-
able fraction C were estimated independently by
chemical assay. The implicit fractional rate constant for
A was infinity and no time lags were implied. Van Soest
et al. (1982) extended the system to include subfraction
B3, for some proteins. Broderick and Craig (1980) used
in vitro rumen fermentations to describe a biexponential
system of A and B plus their fractional rate constants.
Unavailable fraction C was implicitly zero and no time
lag was implied. Broderick (1982) suggested that the
biexponential might be simplified by using a Michaelis-
Menten approach where degradation rate = Vmax/
Km. The estimated proportions escaping the rumen
based on Michaelis-Menten degradation rates were sim-
ilar to proportions based on biexponential degradation
rates for casein and unheated cottonseed meal.
Schoeman et al. (1972) used in situ bags to measure
protein degradation at 12 or 24 h, and later Mehrez and
0rskov (1977) used synthetic fiber (normally dacron or
nylon) bags in situ for determining the degradation of
protein in the rumen at several times. 0rskov et al.
(1980) presented a detailed description of this in situ
technique and its application. Mohamed and Smith
(1977) used the in situ technique to describe a fraction A
that was washed out of tile bag and a fraction B plus its
fractional degradation rate. Fraction A was 1 minus the
antilog of the intercept value, and its fractional rate was
assumed to be infinity. Neither fraction C nor a time lag
were considered. Nocek et al. (1979) calculated frac-
tional degradation rates from O to 2 h and from 2 to 12 h
for concentrates or 2 to 48 h for forages. Their first rate
applies to fraction A, and the second rate applies to frac-
tion B of the general model. Pool sizes for fraction A and
fraction B are not explicitly defined. No fraction C was
considered and a time lag of 2 h for the 2 to 12 h or 2 to 48
h degradation rates was implied by default. Grummer
ant] Clark (1982) calculated fractional degradation
rates from O to 1 h, 1 to 4 h, and 4 to 16 h. The first rate
applies to fraction A, and the third rate applies to frac-
tion B of the general model. Pool sizes for fraction A and
fraction B were not explicitly defined. No fraction C was
considered, and a time lag of 4 h for the 4 to 16 h degra-
dation rate is implied. Zinn et al. (1981) described frac-
tion A as that lost at 4 h in situ arid calculated fractional
degradation rates from 4 to 12 h and from 12 to 24 h.
The first rate thus applies to fraction Be, and the second
rate applies to fraction B2 of the general model. Pool
sizes for fraction Be and B2 were not explicitly calcu-
lated, and no fraction C was considerecl. A time lag of 4
h is implied for fraction Be and a time lag of 12 h is im
plied for fraction B2. A termination time of 12 h is im-
pliecl for fraction Be. Owens and Zinn (1982) described
fraction A independently by solubility due to washout of
small particles through the pores of the dacron bags and
calculated fractional degradation rates for the residue,
so explicit and implicit assumptions are the same as de-
scribed for Zinn et al. (1981~.
The definition of rates without the simultaneous ex-
plicit definition of pools is not the proper way to apply
differential equations to biological systems. Certain
treatments of feeds may change the pool size, while oth-
ers change the fractional rate. Examples of the former
are reduction of lignin by either chemical or genetic
methods that affect the pool of potentially digestible fi-
ber more than it affects its fractional rate of digestion
(Waldo and Jorgensen, 1981~ . The pool sizes of the pro-
tein fractions varied among feecistuffs (Krishnamoorthy
et al., 1982~. Knowledge of pool sizes, degradation
rates, and passage rates are needed to quantitate protein
degradation in the rumen. Secondly, time lags are an
occasional component of descriptive biology using dif-
ferential equations and are consistent with the assump-
tion of fractional rate constants. But termination times
are inconsistent with the concept of fractional rate con-
slants. Conceptually, fractional degradation continues
for infinite time. Choice of a termination time similar to
mean retention time of particles in the rumen may leave
a protein residue in dacron bags similar to amounts of
protein escaping in viva ruminal degradation (Zinn and
Owens, 1983) but do not provide values for modeling to
other passage rates.
0rskov and McDonald (1979) combined data from in
situ degradation rate measurements with independent
data on rate of passage using chromium labeled protein.
They calculates] an effective percentage degradation,
D = A + [kdBB/(kdB ~ kpB)] t1 - e~(k3B+kPB)~], where t
is time after feeding. This effective percentage degrada-
tion is the amount of protein degraded at any time, t,
when both passage and degradation are possible such as
in the rumen. They calculated A as the intercept and
considered the possibilities of a fraction C and a lag time
but did not use them. McDonald (1981) included a lag
time and relaxed the constraint that A ~ B - 1. Stern et
al. (1983a) combined rates of degradation and passage
using either the procedure of 0rskov and McDonald
(1979) for the final value or the procedure of Miller
(1980), where degradation, D = A + kdBB/(kdB + kpB)~
Neither fraction C nor a lag time is considered. Erd-
man (19823 combined rates of degradation and passage
to calculate the protein degraclation, D = A + kdBB/
MOB + kpB) or protein passing, P = kpBB/(k~ + kpB) +
C. The implicit fractional rate for A is instantaneous,
and fraction C is included but no lag time is considered.
Krishnamoorthy et al. (1983) described art in vitro
OCR for page 27
Feed Evaluation 27
technique for estimating rumen proteolysis using prote-
ase from Streptomyces griseus. Krishnamoorthy et al.
(1983) compared estimates of ruminal escape protein us-
ing in vitro proteolysis and the in situ bag technique for
12 concentrate mixtures when assuming the rate of pas-
sage to be 0.04 h- i. The in vitro proteolysis estimates of
escape protein were more highly correlated (r2 = 0.61)
with in vivo escape protein than in situ estimates of es-
cape protein were correlated (r2 = 0.41) with in vivo
escape protein.
All of the models described above imply two simulta-
neous first-order processes operating on each pool and
are subject to the same criticisms about the validity of
these assumptions as used by Baldwin et al. (1977a)
about a similar model of fiber degradation and passage.
The concept of pools and rates is avoided by the use of
summative incremental models (Kristensen et al., 1982;
Stern et al., 1983a, Stern and Satter, 1983~. Such models
do not provide detailed analytic insight since they do not
consider pool sizes, rates, or lag times.
An interim proposal for a system of describing the
degradation of feed proteins seems to require three frac-
tions. Fraction A is assumed to be instantaneously de-
graded. An intermediate clegraclable fraction, B. is as-
sumed to degrade at a fractional degradation rate that
makes the extent of degradation a function of residence
time. The relative proportions of B degraded and passed
depend on the relative rates of degradation and passage.
Fraction C is assumed to have a zero rate of degradation
and must pass undegraded. A time lag may be consid-
ered for B by conventional techniques of differential
equations where t minus the time lag rather than t, per
se, is used. Conceptually, these three fractions and the
rate of degradation of B could be estimated from time
series data obtained from in vitro and in situ fermenta-
tions or proteolytic enzymes. Proper selection of the ini-
tial time should allow a mathematical definition of frac-
tion A as 1 minus the intercept value using theory of
differential equations and r~onlinear estimation much as
done by Mohamed and Smith (1977) or 0rskov et al.
(1980~. Proper selection of the termination time should
allow a mathematical definition of fraction C as an
asymptotic value using theory of differential equation
and nonlinear estimation. This leaves fraction B = 1 -
(A ~ C). Simplification by use of a single B fraction and
single rate constant should be thoroughly considered.
The Michaelis-Menten approach of Broderick (1982)
and the stochastic modeling approach of Matis and Tol-
ley (1980) are possibilities. Such a system seems a reason-
able compromise because I. H. Matis as cited by
Broderick (1982) has suggested that about 10 time points
are required to define each fraction and its fractional
degradation rate.
The theoretical enzymatic and chemical arguments
for a larger number of subfractions are valid but the cost
and difficulty of quantitation rapidly increase. Matis
and Tolley (1980) point out the statistical difficulty of
estimating more than two fractions and suggest that a
deterministic model evaluated at the average rate will
always underestimate the mean of the corresponding
stochastic model. When one considers that a dairy ra-
tion will generally contain at least three feed compo-
nents-forage, grain, and protein supplement-it may
be well to heed the comments of Matis and Tolley
(1980), that ". . . a small, simple stochastic model can
often be substituted for a large, complex deterministic
model.. . ."
Protein degradation estimates from in situ bags are
very sensitive to the conditions of measurement (Moha-
med and Smith, 1977~. Degradation of cottonseed meal
was reduced when in situ bags were incubated in a host
animal that received a concentrate diet compared to a
forage diet (Owens and Zinn, 1982~. Mohamed and
Smith (1977) found steers fed an 85 percent corn diet
had no difference in the soluble fraction but a reduced
rate of degradation to one-third as compared with sheep
fed an alfalfa hay diet. 0rskov et al. (1983) found degra-
dation greater in sheep fed grass than in sheep fed barley
and no consistent difference between cattle and sheep
fed grass. Mohamed and Smith (1977) also found a
threefold increase in rate of degradation when the host
animal was adapted to the protein being tested. Zinn et
al. (1981) and Owens and Zinn (1982) used a reference
protein of soybean meal in an attempt to control varia-
tion and observed a high correlation between predicted
and observed protein bypass. Evidence is accumulating
that rates of degradation decline as feed intake increases
and that this reduction is greater for proteins that have
higher rates of degradation at low feeding levels (Erd-
man, 1982~. This finding implies that the fractional
rates are not constant and that protein degradability dif-
ferences wil1 be reduced at high intakes or low pH. De-
creasing particle size increased both the soluble fraction
and the fractional rate of degradation (Mohamed and
Smith, 1977), but Ehle et al. (1982) have not observed
any increase in degradation rate with decreasing parti-
cle size. Fine soybean meal increased ruminal bypass of
nitrogen compared to coarse soybean meal (Netemeyer
et al., 1980) but did not alter rumen ammonia, blood
urea, total tract digestibility, and milk production.
Heating of soybean meal decreased both the soluble
fraction and the fractional rate of degradation to about
one-third that of an unheated control (Mohamed and
Smith, 1977~. Mixed total diets showed large deviation
from that expected based on the ingredients (Nocek et
al., 1979~.
Representative terms from entire chapter:
fractional degradation
