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Ruminant Nitrogen Usage (1985)

Chapter: 4 Feed Evaluation

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Suggested Citation:"4 Feed Evaluation." National Research Council. 1985. Ruminant Nitrogen Usage. Washington, DC: The National Academies Press. doi: 10.17226/615.
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Suggested Citation:"4 Feed Evaluation." National Research Council. 1985. Ruminant Nitrogen Usage. Washington, DC: The National Academies Press. doi: 10.17226/615.
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Suggested Citation:"4 Feed Evaluation." National Research Council. 1985. Ruminant Nitrogen Usage. Washington, DC: The National Academies Press. doi: 10.17226/615.
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Page 25
Suggested Citation:"4 Feed Evaluation." National Research Council. 1985. Ruminant Nitrogen Usage. Washington, DC: The National Academies Press. doi: 10.17226/615.
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Page 26
Suggested Citation:"4 Feed Evaluation." National Research Council. 1985. Ruminant Nitrogen Usage. Washington, DC: The National Academies Press. doi: 10.17226/615.
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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

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

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

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

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~.

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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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