A Net Carbohydrate and Protein System for Evaluating Cattle Diets: 111. Cattle Requirements and Diet Adequacy' v

D. G. Fox*, C. J. Sniffen*p2, J. D. O'Comor*~,J. B. Russell*~+, and P. J. Van Soest*

*Department of Animal Science, Cornell University, Ithaca, NY 14853 and W.S. Dairy Forage Research Center, ARS, USDA, Madison, WI 53706 and U.S.Plant, Soil, and Nutrition Laboratory, Ithaca, NY 14853

ABSTRACT: The Cornell Net Carbohydrate and Protein System (CNCPSI has equations for predicting nutrient requirements, feed intake, and feed utilization over wide variations in cattle (frame size, body condition, and stage of growth), feed carbohydrate and protein fractions and their digestion and passage rates, and environmental conditions. Independent data were used to validate the ability of the CNCPS to predict responses compared to National Research Council (NRC) systems. With DMI in steers, the CNCPS had a 12% lower standard error of the Y estimate (Sy.x) and three percentage units less bias than the NRC system. For DMI in heifers, both systems had a similar Sy.x.but the NRC had four percentage units less bias. With lactating dairy cows' DMI, the CNCPS had a 12Y0 lower SY.=. Observed NE,

requirement averaged 5% under NRC and 6 % under CNCPS predicted values at temperatures above 9' C but were 18% over NRC and 9 YO under CNCPS at temperatures under 9°C. Energy retained was predicted with an R2 of .80 and .95 and a bias of 8 and 4% for the NRC and CNCPS, respectively. Protein retained was predicted with an R2 of .75 and .85 with a bias of 0 and -1% for NRC and CNCPS, respectively. Biases due to frame size, implant, or NE, were small. Body condition scores predicted body fat percentage in dairy cows with a n R2 of .93 and a Sy.xof 2.35% body fat. The CNCPS predicted metabolizable protein allowable ADG with a bias of 1.6% with a Sy.xof .07 kg compared to values of -30% and .10 kg, respectively for the NRC system.

Key Words: Cattle, Requirements, Nutrition, Models

J. Anim. Sci. 1992. 70:3578-3596

Introduction National Research Council nutrient requirements are the standards most often used in the United States to predict requirements of cattle. These standard typically have been based on research data from uniform cattle with little environmental stress and do not account for all the variables of actual field conditions (Cunha, 19871. Cattle can differ significantly in their biological potential, and these animals are produced in

'Appreciation is expressed to T. C. Perry, T. P. Tylutki, and S. J. Ainslie for assistance with the data analysis. 2Present address: W. H. Miner Agnc. Res. Inst., Chazy, NY 12921.

3Present address: P.O. Box 2077, Corvallis, OR 97339. Received May 24, 1991. Accepted July 13, 1992.

nearly every environmental condition. Feed energy and protein vary widely from farm to farm and depend on soil type, plant variety, growth condition, fertilization, harvest, and storage management. The Cornell Net Carbohydrate and Protein System (CNCPS) predicts nutrient requirements and animal performance over wide variations in cattle, feed, management, and environmental conditions. Submodels for predicting the supply of ME and protein in cattle are given in companion papers (Russell et al., 1992; Sniffen et al., 1992). This paper describes submodels for predicting cattle requirements and provides validation data on CNCPS and NRC (1984,1985, 19891 predictions of DMI, energy and protein retained, energy reserves in cows, and metabolizable protein allowable production, using data observed in studies independent of those used in model development.

3578 Downloaded from https://academic.oup.com/jas/article-abstract/70/11/3578/4705739 by guest on 04 March 2018

CATTLE REQUIREMENTS

Model Development and Validation Fox et al. (1988) presented the first version of the requirement submodels as applied to beef cattle. The CNCPS presented here includes modifications to describe both beef and dairy cattle across the U.S. cattle population, and validation with independent data. The total system is a n assemblage of physiological and metabolic submodels: intake; ruminal carbohydrate fermentation and protein degradation; intestinal digestion, absorption, and excretion; heat production; and nutrient partitioning and utilization for maintenance, growth, lactation, and reserves. A mix of mechanistic and empirical approaches was used, depending on the data available, variables needed to drive the equations, and level of aggregation needed for practical application. The first step in model development was to identify those factors known to influence cattle requirements under field conditions that could be quantified with response functions and adjustment factors. Descriptive codes were developed and are presented that can be universally understood and applied in program formulas to calculate responses. The next step was to integrate a nutrient utilization submodel with the requirements submodel so that animal responses to given diets could be quantified under field conditions. Model validation data are presented for components where adequate independent data were available to the authors to test that component, including DMI, NE,, energy and protein retained, energy reserves, and ME allowable production. Linear regressions were calculated between observed and values predicted by NRC and CNCPS systems, as described by Rayburn and Fox (19901. The standard error of the Y estimate (Sy.xlgave a n estimate of the precision of the predicted values over the range of observations, and when the regression was forced through the origin the x coefficient gave an estimate of the bias in predicting the observed values.

AND DIET ADEQUACY

3579

estimates for the relationships between body condition score (CS)of growing cattle and their expected maintenance energy requirement and use of ME above maintenance (Fox et al., 1988). Appendix Table 3 presents the relationship of body CS to body fat content developed by George (1984). Condition scores for cows are based on the condition scoring systems of Cantrell et al. (1982) for beef cattle and Wildman et al. (1982) for dairy cattle. These scores are used to compute energy reserves available and their depletion or repletion rate, based on energy balance. Thompson et al. (19831found that CS was a more reliable estimator of body fat than was weight, linear measurements, or combinations of weight and linear measurements. As CS declines the proportion of body tissue that can be mobilized declines. Ferrell et al. (1986) found that previous planes of nutrition (low, medium, and high) resulted in a NE, of 51, 72, and 90 kcal per BW.75per day, respectively. Fox et al. (1972) and Carstens et al. (1987)found that compensatory growth involves both a reduction in NE, required and a n increase in efficiency of ME use above maintenance, resulting in higher diet NE, available. Appendix Table 2 gives adjustments for these effects.

Predicting Dry Matter Intake The NRC (19871 discusses alternative systems for predicting DMI; where available, historical information of the operator can be used. The following equations are used to predict intake for various cattle types; adjustments for various factors are given in Appendix Table 4 that can be used with these or other intake estimates. For growing cattle (NRC, 1987):

DMI

NEma - .046 NEma2 .01901 (BFAF) (AGE11 (BI) (ADTV)

= (W.75(.1493

(TEMP11 (MUD111 For beef cows (NRC, 19871:

Cattle Descriptions The system of Fox and Black (1984) for describing growing cattle with nine frame size categories was expanded to include breeding cattle based on the Beef Improvement Federation (1986) system, as described by Fox et al. (19881. Nutrient requirements were then related to weight and frame size of cattle (Fox et al., 1988). Appendix Table 1 summarizes the relationship of frame size to BW. Variations in energy intakes above maintenance can change body condition score, and this change can affect maintenance requirements, feed utilization, and energy reserves. Appendix Table 2 gives Downloaded from https://academic.oup.com/jas/article-abstract/70/11/3578/4705739 by guest on 04 March 2018

DMI = (BW.75LO194 + .0545 NEma) (TEMP11 (MUD11 + .2 MMI For lactating dairy cattle (Milligan et al., 19811: DMI

=

((.0185BW + .305 (.4 (TEMP11 (MUD111

+

.15 PQ) MM)

For nonlactating dairy cattle (Milligan et al., 1981): DMI

=

(.O2BW (TEMPI) (MUDIN

where DMI is animal dry matter intake, kg/d; BW is shrunk live body weight, kg; NEma is net energy

FOX ET AL.

3580

~

value of diet for maintenance, Mcal/kg; MM is milk production rate, kg/d; PQ is milk fat, %; MUD1 is mud adjustment factor for DMI (Appendix Table 41; BI is breed adjustment factor for DMI (Appendix Table 41; TEMP1 is temperature adjustment factor for DMI (Appendix Table 41; AGE1 = age adjustment factor for DMI (Appendix Table 41; ADTV = feed additive adjustment factor for DMI (Appendix Table 41; and BFAF = body fat adjustment factor (Appendix Table 41. The same environmental adjustments are used to adjust intake for all cattle types. Data collected during the feeding trials of Harpster (19781, Woody et al. (19831, Loy et al. (19881, and Wahlburg et al. (19881, in which initial and final body composition and feed NE values were determined, were used to validate the DMI equations for growing cattle (Figure 11. These data were used so that frame size and feed energy values could be accurately assigned. Included were 500 pen observations of DMI of steers and 78 pen observations of heifers, with most represent-

ing 28-d intervals during the experiment. Represented were diets ranging from all corn silage to all corn grain and nine frame sizes. For steers, the standard error of the Y estimate for NRC (19841 and CNCPS systems was .82 and .70 kg, respectively, with a bias of 6 and 3% and a n R2 of .64 and .63 for the respective systems. For heifers, the standard error of the Y estimate for NRC and CNCPS systems was .36 and .35 kg with a bias of 0 and 4%and a n R2 of .94 and .87 for the respective systems. Adjustments for environmental temperatures were not made because they were not available. We concluded that much of the variation was likely due to environmental effects, and may have masked much of the increased sensitivity of the CNCPS to stage of growth. In a n evaluation of the CNCPS with 299 pen observations with Holstein steers (Rayburn and Fox, lQ9Ol the CNCPS accounted for 93% of the variation in DMI with a n overall overprediction bias of 1% and a standard error of the Y estimate of .58 kg.

CNCPS

NRC

=r-

/I

y - x

/ = 33

4

5

6

7

8

9

1

0

1

1

1

02

33

"

I

4

- 5

6 I

7

8

9

10

11

12

DRY MATTER INTAKE, kg

DAY MATTER INTAKE. kg

v-x

/I

11 Y-Y

DAY MATTER INTAKE, kg

Figure 1. Relationship of NRC (1984)and CNCPS predicted and observed values for DMI. Set 1 includes 500 pen observations (28-d periods) of steers and Set 2 includes 78 pen observations of heifers. For steers, the standard error of the Y estimate for NRC (1984)and CNCPS systems is .82 and .70 kg, respectively, with a bias of 6 and 3% and an R2 of .64 and .63 for the respective systems. For heifers, the standard error of the Y estimate for NRC and CNCPS systems is .36 and .35 kg with a bias of 0 and -4% and an R2 of .94 and .87 for the respective systems. Downloaded from https://academic.oup.com/jas/article-abstract/70/11/3578/4705739 by guest on 04 March 2018

CATTLE REQUIREMENTS AND DIET ADEQUACY NRC

1

.;b

12

1;

IO

ACNK

10

&

3581

CNCPS

24

ZU

28

I

'$0

12

14

16

18

23

22

28

24

I P

Jo ACTUALDRY MATTER INTAKE. *U

DRY MATTER INTAKE. Q

Figure 2. Relationship of NRC (1989) and CNCPS predicted and observed values for DMI. The data include 149 period observations from 28 experiments with lactating Holstein cows. The standard error of the Y estimate for NRC and CNCPS systems is 1.5 and 1.7kg, respectively, with a bias of -5 and + 6% and a n R2 of .43and .49 for the respective systems.

Data from trials with lactating dairy cows reported previously and summarized by Rayburn (E. B. Rayburn, West Virginia Univ., Morgantown, personal communication), were used to validate the prediction of DMI for lactating dairy cows. Included in the database (Figure 21 are 149 period observations of DMI of 1,284 Holstein cows in 28 experiments. The CNCPS intake equation had an average underprediction bias of 5%, whereas the NRC had an average overprediction bias of 6%. Most of the CNCPS bias occurred at intakes > 2 0 kg [residuals, Figure 21, which would represent high-producing cows. The CNCPS had a 12% lower standard error of the Y estimate (1.5 kg) than did the NRC (1989; 1.7 kgl.

Protein Requirements For Maintenance Protein requirements for maintenance are the sum of scurf protein, urinary protein, and metabolic fecal protein (NRC, 1984). In the NRC (1984, 19851, metabolic fecal nitrogen is a function of indigestible DM. The CNCPS assumes that metabolic fecal protein is 99/0 of indigestible DM (100 digestible DMI, as does NRC (1985). This value agrees closely with that developed by CSIRO (1990) after a careful alalysis of the literature. SPA = 2.75 BW-5/.67 UPA = .20 BW.'/.87 FPN = .OQ-IDM XP = SPA + UPA + FPN where XP is metabolizable protein required for maintenance, g/d; SPA is scurf protein, g/d; UPA is urinary protein, g/d; IDM is indigestible dry matter, g/d; and FPN is metabolic fecal protein, g/ d. Downloaded from https://academic.oup.com/jas/article-abstract/70/11/3578/4705739 by guest on 04 March 2018

Energy Requirements For Maintenance Maintenance requirement for energy is variable, depending on weight, level of production, activity, and environmental effects (Fox et al., 19881. The equations presented by Fox et al. (1988) were modified to include adjustment for grazing cattle (George, 1984) and application with dairy cattle. The modified environmental submodel is presented in Appendix Table 5, which uses adjustment factors given in Appendix Tables 8 and 7.

~-

B

120 CNCPS PREDICTED

i Im_ s

75-

'

g

80

x = -

m

m

*

x

60

w x

+

+

+

52

RESIDUALS

+ + 0

~

-2017

r t - r

7

7

I

+

+

+

+

+ 1

+ 7 - 7

1

I

T

-7-7

-26-17 -7 -6 -5 -4 -3 -2 0 8 9 14 16 17 17 18 19 20 EFFECTIVE AMBIENT TEMPERATURE (C)

'

Figure 3. Relationship of CNCPS predicted and observed values for NE, (NRCvalue is a constant 77 kca.UBW.75).The data include 18 values computed from 840 pen observations of feedlot cattle fed in northern climates (Colorado, Iowa, Minnesota, and Canada). The standard error of the Y estimate is 14 k ~ a l / B W . with ~~, an R2 of .33.

FOX ET AL.

3582

Several researchers have suggested that energy for maintenance increases with lactation, and that this increase is due to larger internal organs. Smith and Baldwin (19741 indicated that lactating cows have a 10% higher maintenance expenditure than do nonlactating cows of similar weight, and Canas et a1. (19821 reported a 24% increase in maintenance energy expenditure that was independent of increases in feed intake. Lemenager et al. (1980) showed that lactating animals had a 5.3% higher energy expenditure than nonlactating animals, irrespective of breed. Appendix Table 6 lists the lactation maintenance energy requirement adjustment factors (Ll for various breeds, based on a literature review by George (19841. No adjustment is made for Holstein cows because of the effect of high milk production on hormonal status. Fox et al. (1988) compared NE, predicted with the CNCPS to values computed by Johnson (19861, based on 840 pen observations of feedlot cattle fed in northern climates in the United States and Canada in research and commercial feedlot locations over several years. The performance of the CNCPS with this database as described by Johnson (19861 was further evaluated (Figure 3). Diets fed were typically high-grain finishing diets. Apparent NEm was assumed to be diet NE, intake, which was computed from feed intake and apparent feed NE, required for the predicted energy retained. Predicted NEm was computed with the CNCPS model. The NEm required averaged 73 kcaVBW.75 when environmental temperatures ~ environwere < 9" C and were 91 k ~ a l / B W . 'when mental temperatures were > Q"C, compared to CNCPS average predicted values of 79 and 99 kcal, respectively. It is apparent that the NRC (1984) recommended NEm requirement of 77 kcall BW.75 was appropriate for lot conditions that occurred at temperatures > 9°C but was too low for lot conditions that occurred at lower tempera tures. The error of predicting these effects with the CNCPS was high, however, with a standard error of the Y estimate of 14. There were many variables that could not be accounted for by the CNCPS, such as hide and hair coat condition, because of limitations in the data. However, this evaluation clearly indicated the need for more refinement in recommendations for appropriate requirements to use to compute the maintenance requirement.

Requirements For Lactation Equations to describe the effect of milk production level and age on the lactation requirements of beef cows are presented in Appendix Table 8, Downloaded from https://academic.oup.com/jas/article-abstract/70/11/3578/4705739 by guest on 04 March 2018

Table 1. Woods' coefficients for predicting dairy milk yield Woods' equation coefficients

Lactation number

b .08 .12 .16

1 2

3+

C

-.002 -.004 -.005

d -.001 -.002 -.002

based on modifications of Fox et al. (1988). The average peak milk CYB) for the breed (Appendix Table 6)is used as the base, and a scale (PL) of one (extremely low for the breed) to nine (Extremely high for the breed) is used to adjust YB. Beef cow milk production level can be estimated from calf weaning weights (Fox et al., 19881, and peak yield can be related to the age of the calf (George, 19841. The CNCPS assumes that the time of peak yield is linearly related to peak yield and that daily milk yield for the day of lactation (TL) is based on calf age (George, 1984). Milk yield prediction equations for lactating dairy cows were developed based on Oltenacu et al. (1981) and Marsh et al. (1988) and on the Woods (18671 equation coefficients for lactation number. For first-lactation dairy cows, the A coefficient is determined from the following equations: A GNRHA

= =

(GNRHA - .01 - 20)/2.96, and rolling herd lactation average, lb milk/yr.

For multiparous dairy cows, the A coefficient is determined from the following equation:

A

= (14

+ GNRHA

.011/2.96

Using the Woods coefficients b, c, and d (Table 11, expected daily milk production of dairy cows is

MM

=

A (TLlb EXPkTL) EXP(gTGEST)

where TL = day of lactation and TGEST = day of gestation, d. The terms PQ, PP, and ML below are used to estimate milk protein, milk fat, and milk lactose, respectively (George, 19841, or average values of 3.5 and 3.3% can be used for milk fat and protein, respectively. The term LE computes metabolizable energy required for lactation, assuming a 85% efficiency unadjusted for level of DMI (NRC, 19891, because the rumen model adjusts ME for level of DMI. The term LP predicts metabolizable protein requirements from milk yield, milk protein content, and an efficiency of 65% (NRC, 1985).

CATTLE REQUIREMENTS AND DIET ADEQUACY

PQ = 1.01 MF UTL + 1)/71-.'3)(EXP(.02((TL+ 1)/7))) PP = 1.14 MP (((TL + 11/71-.'2)(EXP(.01((TL+ 1)/7))) ML = 5 - .0027 TL LE = .1 MM (PQ + 3.4)/.65 LP = 10 MM (PP)/.65 where MF is peak milk fat, YO; MP is peak milk protein, YO; PQ is milk fat for a particular d of lactation, % ; PP is milk protein on a particular d of lactation, YO;ML is milk lactose on the TLth d of lactation, OO/ ; LE is metabolizable energy required for lactation, Mcal/d; and LP is metabolizable protein required for lactation, g/d.

Requirements For Growth Requirements for optimum growth of replacement heifers and young cows are dependent on rate, composition, and efficiency of daily gain, and for growing heifers, prediction of daily gain is dependent on accurate prediction of NE available for gain, which in turn depends on accurate assessment of maintenance requirements and feed energy values. For pregnant herd replacements and young cows, growth requirements are determined with the equations in Appendix Table 9, based on modifications of Fox et al. (1988). Mature cow weight (MW) is determined from frame size and adjusted to moderate body condition (22.5% empty body fat). The Gompertz growth function has been used to describe growth in animals (Taylor, 1968). Optimal BW and rate of gain are derived from the Gompertz growth curve for dairy and from the Brody growth curve (George, 1984) for beef cattle (Appendix Table 10). Bruce et al. (1984) applied the Gompertz function to the growth of dairy cattle and energy systems of lactating and pregnant cows, and George (1984) applied the Brody equation to beef cattle. The two approaches are used because of the differences between beef and dairy in preweaning management. The term KW is the rate of maturing relative to mature weight. Equation W calculates the expected live weight of a moderately conditioned animal at t days of age, or the live weight of the animal may be used as a n input. The term WG gives the expected optimal daily weight gain at t days of age, or the expected daily weight gain may be a n input to the model. Equation EG calculates the empty weight gain. The term NG calculates ME required for growth, assuming a n efficiency of 40% (NRC, 1985). The term AF predicts empty body fat of a moderately conditioned female at t days of age. The term GP then calculates the metabolizable protein required for gain based on empty weight gain and protein composition of the gain, with a n efficiency of 50% (NRC, 1985). Downloaded from https://academic.oup.com/jas/article-abstract/70/11/3578/4705739 by guest on 04 March 2018

3583

Cattle have similar body compositions at similar degrees of maturity (Oltjen et al., 1986). Steers are assumed to average 28% body fat at the mature breeding female weight for a particular frame size (Appendix Table 11, based on data with steer composition and heifer mate mature weight (Smith et al., 1976; Harpster, 1978; Cundiff et al., 1981; Jenkins and Ferrell, 1984). Bulls are assumed to be 20% heavier and heifers 20% lighter at the same degree of maturity than steers of the same frame size, based on Klosterman and Parker (19761, Harpster et al. (19781, and Fortin et al. (1980). Because of the large body composition data base used to develop it (72 comparative slaughter experiments with 3,491 cattle; Garrett, 1980) and wide acceptance and success with its use, the NRC (1984) medium-framed steer equation is used as a base for predicting energy and protein retained and daily gain in growing cattle, including herd replacements until bred. Body weights for all frame sizes and sexes are converted to the 1984 medium-framed steer equivalent weight by multiplying their weight by the equivalent weight factor (Appendix Table 111. This factor was obtained by dividing the weights in Appendix Table 1 into the frame size 3 steer weight. These factors adjust all steers and heifers to a common weight (equivalent weight) for use in the medium-framed steer equations, and along with the body condition score adjustment give infinite possibilities, compared to the five choices given by the NRC (1984). These additional choices reduce the risk of a judgment error in describing the energy requirements of cattle. USDA statistics for 1991 (M. Berwin, USDA market news, Des Moines, IA, personal communication) indicate that steers average 542 kg and 50% Choice, a high percentage of them within a range of 395 to 644 kg. The frame size 5 weight of 533 kg with a range of 400 to 667 kg appendix Table 1) thus accommodates most of the U.S. cattle population. All implants containing a n estrogenic substance give similar improvements in performance when evaluated under similar conditions, and nearly all of this increase in gain can be accounted for as an increased growth of lean tissue and skeleton (Trenkle, 1990). The NRC (19841 increases energy content of gain 5% when growth stimulants are not used. Recent studies (Bartle et al., 1990; Trenkle, 1990; Perry et al., 1991) indicate that estrogenic implants increase protein content of gain equivalent to one frame size change, whereas estrogenic and trenbolone acetate (TBA) combination implants alter the protein content of gain equivalent to a change in two frame sizes (Appendix Table 111, based on observed effects on feed intake, daily gain, feed per gain, and weights at a similar chemical composition. Because the NRC

3584

FOX ET AL.

(1984) gain equation was developed from cattle receiving an estrogenic implant, the BW is adjusted one frame size smaller for no implant and one frame size larger for an estrogenic and TBA combination in the CNCPS for use in the equation to predict ADG. Predicted weight and rate of gain at various ages can then be compared to optimum growth rate for herd replacement heifers (Appendix Table 101.The NRC (1984)does not recommend optimum rates of gain; it is assumed to be variable, depending on feed and non-feed costs. However, there is an optimum growth rate for breeding herd replacement heifers or various body types if lifetime production is to be maximized.

and .31 Mcal, respectively, with a bias of 8 and 4 YO and an R2 of .80 and .95for the respective systems. For protein retained, the standard error of the Y estimate for NRC and CNCPS systems is 14 and 12 g, respectively, with a bias of 0 and -1 O/O and R2 of .75 and .85 for the respective systems. The relationship of NRC (1984) and CNCPS predicted and observed values for energy and protein retained in heifers is presented in Figure 5. The data include 17 pen observations with body composition on 237 heifers given a n estrogenic implant and fed from weaning to 28% body fat. Included are frame sizes 2 to 7 and diets fed from all corn silage to all corn grain. For energy retained, the standard error of the Y estimate for FFM = NE,/NEma NRC and CNCPS systems is .18 and .09 Mcal, NEFP = (DMI - FFM) NEga respectively, with a bias of 9 and 6% and an R2 of ADG = 13.91 NEFP.g"O WE-.0837 .95 and .99 for the respective systems. For protein GP = IWG . (268- (29.4(NEFPNVG))))/MPG retained, the standard error of the Y estimate for NRC and CNCPS systems is 5 and 3 g, respecTo predict daily gain, adjusted for environment: tively, with a bias of -4 and -1Yo and a n R2 of .91 and .96 for the respective systems. FFMenv = NEmenv/NEma This analysis indicates that the NRC (1984) NEFPenv = (DMI - FFMenv) NEga medium-framed steer equation can be used as a .~"~ ADGenv = 13.91 N E F P ~ ~ VWE-.0837 base to predict accurately the energy and protein requirements of growing and finishing steers and where NEm is net energy required for main heifers across widely varying frame sizes imtenance, Mcal/d; NEma is NEm concentration of planted with an estrogen when the CNCPS adjustdiet, Mcal/kg; NEga is NE, concentration of diet, ments for frame size are used as described above. Mcal/kg; FFM is feed needed for maintenance, kg/ The database just described included British and d; WE is BW adjusted for frame size (Appendix European beef and Holstein breed types. Similar Table 11); GP is metabolizable protein required for results have been obtained with Holstein steers gain, g/d; MPG is the coefficient for efficiency of alone (Rayburn and Fox, 1990). metabolizable protein for gain, which is varied from .75 to 181 kg to .50from 181 to 363 kg to .407 > 363 kg based on ARC (19801,NRC (19851, and Requirements For Pregnancy INRA (19891;ADG is average daily gain, kg/d; ADGenv is environmentally adjusted average Requirements for gestation (Appendix Table 12) daily gain, kg/d; and NEFP is net energy available are based on modifications of Fox et al. (19881. for production (gain), Mcal/d. Equations were modified to prevent negative The data of Crickenberger et al. (19781,Harpster accretion rates. Net energy and protein require(19781, Danner et al. (19801,Lomas et al. (19821, ments for pregnancy are the s u m of the contents of Woody et al. (19831,Loy et al. (19901,Perry et al. the fetus, cotyledon, placenta, uterus, and fetal (1991a,b), and Ainslie et al. (19921 were used to fluid. Metabolizable energy for pregnancy is comvalidate the CNCPS and NRC (19841 systems for puted with a n assumed efficiency of 12.5%. predicting net energy and protein requirements Metabolizable protein for pregnancy is based on for growth. The data were diverse in frame size, a n efficiency of 50%. breed type, implant use, and NE,. The relationship of NRC (19841and CNCPS predicted and observed Body Reserves values for energy and protein retained in steers is shown in Figure 4. The data include 153 pen Body reserves equations (Appendix Table 13) observations with body composition of 1,277steers are from Fox et al. (19881,modified so that body receiving a n estrogenic implant and fed from protein reserves would not contribute to energy weaning to 28% body fat. Included are nine frame available from reserves. Thompson et al. (19831 sizes and diets fed from all corn silage to all corn found that CS was a more reliable estimator of grain. For energy retained, the standard error of the Y estimate for NRC and CNCPS systems is .85 body fat than were weight, linear measurements,

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3585

CATTLE REQUIREMENTS AND DIET ADEQUACY

Table 2. Energy reserves at different condition scoresa Item Condition score Score system CNCPS Dairy

1 1

2 1+

3 2

4 2+

6 3+

5 3

7 4

8 4+

to

to

to

to

2-

3-

4-

5-

9 5

Mcal per condition score Body wt, g 455 500 545 591 636 682

0

0 0 0 0 0

133 147 160 174 187 200

145 159 174 188 202 216

155 171 187 203 218 233

167 184 200 217 234 25 1 ~

167 184 20 1 219 236 253 ~

168 186 203 220 237 254 ~~

169 187 204 22 1 238 255

170 188 205 222 239 256

~~

%e value given for each condition score was determined from the equations in Appendix Table 13 and is the net energy required to reach that score from the previous score or the energy that will be provided when the current score is mobilized.

or combinations of weight and linear measurements. Ferguson and Otto (1989) concluded that CS can be used to assess energy balance and tissue mobilization in dairy cows, with one dairy score providing an average of 400 Mcal when mobilized (equals 200 Mcal per CNCPS CS). As CS declines the proportion of body fat that can be mobilized declines. Table 2 was developed from the equations in Appendix Table 13, and it shows how this system can be applied to manage energy balance in cows varying in body size and CS. The value given for each CS was determined from the equations in Appendix Table 13 and is the NE required to reach that score from the previous score or the energy that will be provided when the current score is mobilized. This table can be used to compute days for a CS change as follows, assuming the following efficiencies INRC, 1989): ME for energy reserves gain, 75%; ME for milk production, 65%; and reserves NE for milk production, 82%. 1.

2.

Determine the energy reserves available (required) for the condition score to be lost (or gained). For example, a 591-kg cow in CS 8 will contribute 219 Mcal of NE as that CS is mobilized or will require 219 Mcal of NE available to regain that CS. If NEl intake is below requirements, 1 Mcal of tissue energy will substitute for .82 Mcal of diet NE1 (or .82/.64 = 1.26 Mcal of ME). Therefore, days to change 1 CNCPS CS = reserves energy in one CS change x .82 divided by the NE1 deficiency. For example, if the ration fed a 591-kg cow provides 3 Mcal of NE1 daily less than needed and the cow is

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3.

CNCPS CS 6, she will reach a CS 5 in (219 x .82)/3 equals 60 d. If NE1 intake exceeds requirements, 1 Mcal of NE1 will provide (1/.65) x .75 = 1.15 Mcal of tissue energy, or 1 Mcal of ME will provide .75 Mcal of tissue energy. In this example, the 591-kg cow a t CS 5 and consuming 3 Mcal of NEl in excess of requirements will move to a CS 6 in 219/((3/.65) x .75) = 63 d.

The original equations presented in Appendix Table 13 and the relationships between CS and body fat in Appendix Table 3 were developed by George (1984) from a summary of 14 studies on empty body fat determinations, primarily in beef cows. To test the CNCPS system of predicting body fat from CS in dairy cows, the data of Otto et al. (19911, who assessed body condition scores of 50 Holstein cows, and made subsequent body fat determinations, were used to compare CNCPS predicted with measured body fat values. The results are presented in Figure 8. The standard error of the Y estimate was 2.35, with an R2 of .93. The bias was 18% overprediction of body fat. Further analysis indicated that reducing the CS by 1 unit would have resulted in small residual errors (predicted - observed). These results agree with those of Houghton et al. (1990) in studies with Charolais-Angus crossbred cows. Using the energy value of one CS lower in Table 2 a 591-kg cow requires 203 rather than 217 Mcal to reach a CS 5, which is a difference of 7%. This value is nearly identical to that of Ferguson and Otto (19891, as discussed above. Use of the model directly could thus overpredict energy required for a change in CS or energy provided by a CS by approximately 7%.

3586

FOX ET AL.

Total Energy and Protein Requirements Total protein and energy requirements are the s u m of requirements for maintenance, lactation, gestation, and growth. Maintenance energy requirement is adjusted for the energetic cost to excrete N (urea) is excess of bacterial and tissue needs, based on NRC (19891.

UC ME NE MP

Neutral Detergent Fiber Requirement

= (.073 . EN); if EN > 0 = NEm/.65 PE LE NG

+

=

=

+ + + UC + .65 LE + .4 NG

NEm + .125 PE XP + GP + WP

where EN is nitrogen in excess of bacterial and tissue needs, g/d; UC is energetic cost of excreting excess rumen degradable nitrogen in urine, Mcal/ d; ME is metabolizable energy required per animal, Mcal/d; NE is net energy required per animal, Mcal/d; and MP is metabolizable protein required per animal, g/d.

Adequate fiber, which is measured as diet NDF, is necessary for rumination, saliva flow, ruminal buffering, and health of the rumen wall. In

+ LP

NRC

CNCPS

-05

20 25 30 3'5 40 OBSERVED RETAINED ENERGY, Mcalld

10

15

10

45

. . -. I . . ..:-

1 15 20 25 30 35 4 0 OBSERVED RETAINED ENERGY, Mcal/d

'5

RESIDUALS

f .:] -

-15 25

.

*

I

7;

96

-

16

65 3-1 OBSERVED PROTEIN IN THE GAIN, grnsld

1

ci 65

- 8 3 1

5

&5

ffi

&

lffi

li5

I I25 135

OBSERVED PROTEIN IN THE GAIN, gms/d

Figure 4.Relationship of NRC (1984)and CNCPS predicted and observed values for energy and protein retained in steers. The data include 153 pen observations with body composition on 1,277 steers fed from weaning to 28% body fat (range in adjusted final weight of 299 to 722; mean is 499 kg and SD is 71 kg). Breed types in the data base include Angus, Hereford,Simmental, Angus x Hereford, Angus x Simmental, Angus x Charolais, Angus x Hereford x Charolais, Holstein, and Angus x Hereford x Holstein. Diets fed ranged from all corn silage to all corn grain. For energy retained, the standard error of the Y estimate for NRC and CNCPS systems is .65 and .31 Mcal, respectively, with a bias of 8 and 4% and an R2 of .80and .95 for the respective systems. Similar results were obtained when energy retained was used to predict live weight gain with the live weight gain equation. For protein retained, the standard error of the Y estimate for NRC and CNCPS systems is 14 and 12 g, respectively, with a bias of 0 and -1% and an R2 of .75 and .85 for the respective systems. Downloaded from https://academic.oup.com/jas/article-abstract/70/11/3578/4705739 by guest on 04 March 2018

CATTLE REQUIREMENTS AND DIET ADEQUACY

lactating dairy cows, adequate NDF is also necessary to prevent milk fat depression. The NDF that is effective in meeting these requirements depends primarily on particle size (Mertens, 1985). Based on the values of Mertens (19851, adjustment factors to compute effective NDF were developed and presented in a companion paper with feed passage rates (Sniffen et al., 19921. Williams (19881 indicated that the NDF capacity in lactating dairy cows is .8, 1.2, and 1.0% of BW at calving, 100 d postpartum, and drying off, respectively. The following equation predicts effective NDF capacity from parturition to 100 d postpartum, based on Williams et al. (19881:

NDFPBW

3587 .8

=

+

.4.(DOL/IOOl

where NDFPBW is effective NDF capacity, as a percentage of body weight; .8 is NDF intake at parturition, as a percentage of body weight; .4 is change in NDF capacity per day of lactation, as a percentage of body weight; and DOL is day of lactation. From 100 d postpartum until d 160 of gestation, NDF capacity is assumed to be constant a t 1.2% of body weight and effective NDF is assumed to be NDFPBW

=

1.2

At d 160 of gestation, the enlarging uterus begins

NRC

CNCPS

RESIDUALS

-154 15

I

r

I

25

1

I

I

I

I

I

I

.

35 45 55 55 75 OBSERVED RETAINED ENERGY Mcalld

I

I

e5

I

-1.54 15

3

1

2.5

I

I

I

,

I

.

1

1

.

85 4.5 5.5 6.5 7.5 OBSERVED RETAINED ENERGY, Mcalla

.

I

8.5

r

Y=X D

75

95

115 135 156 175 195 215 OBSERVED PROTEIN IN THE GAIN, gms/d

235

75

95

115 135 156 175 195 215 OBSERVED PRO'EIN IN THE GAIN, gmsld

235

Figure 5. Relationship of NRC (1984) and CNCPS predicted and observed values for energy and protein retained in heifers. The data include 17 pen observations with body composition on 237 heifers fed from weaning to 28% body fat (ranging in adjusted final weight from 347 to 466 kg; mean is 395 kg and SD is 39 kg). Breed types included in the data base are Hereford, Angus x Hereford x Charolais, and Angus x Hereford x Holstein. Diets fed ranged from all corn silage to all corn grain. For energy retained, the standard error of the Y estimate for NRC and CNCPS systems is .18 and .09 Mcal, respectively, with a bias of 9 and 6% and an R2 .95 and .99 for the respective systems. For protein retained, the standard error of the Y estimate for NRC and CNCPS systems is 5 and 3 g, respectively, with a bias of -4 and -1% and an R2 of .91 and .96 for the respective systems. Downloaded from https://academic.oup.com/jas/article-abstract/70/11/3578/4705739 by guest on 04 March 2018

3588

FOX ET AL.

40-

35

~- -

~

1

I

CNCPS PREDICTED

X

__

For growing and finishing cattle, a minimum of

ACTUAL BODY FAT

/

15% of ration DM as NDF or 5% of ration DM as

/’

effective NDF was assumed, based on recommendations of Strasia and Gill (19901.They concluded that finishing rations for cattle should contain a t least a 7% “high roughage” factor.

x

RESIDUALS

+ +

__

~-

-5 O 1l T - 72 : ~

3

4 5 6 - 7 BODY CONDITION SCORE ~

8

9

Figure 6. Relationship of CNCPS predicted and observed body fat in Holstein cows varying from condition score 1 to 9. The data include 50 determinations with Holstein cows summarized by condition score. The standard error of the Y estimate is 2.35% body fat and the bias is la%, with an R2 of .94.

to displace rumen capacity. Therefore, NDF intake capacity decreases. A linear decrease from d 160 of gestation to parturition predicts effective NDF capacity as NDFPBW

=

1.2 - .4*[TGEST - 1601/120

where NDFPBW is NDF capacity, as a percentage of body weight, and TGEST is day of gestation. Effective NDF requirement is assumed to be 20% of ration DM for lactating dairy cows. For growing replacement heifers, .8% of BW as NDF was assumed to be adequate, based on information presented by Williams (1988). Therefore, the effective NDF requirement is NDFPBW

=

.8 NRC

Animal Validation of Performance Predicted by the Cornell Net Carbohydrate and Protein System Validation of the accuracy and precision of the CNCPS in predicting animal responses to variations in factors influencing feed utilization requires information on feed carbohydrate and protein fraction composition, in addition to accurate information on animal requirements. Experimental data in which this type of information is available are very limited. Consequently, four experiments were conducted in which growth responses of Holstein calves during a dietary protein sensitive stage of growth (from 110 to 250 kg) were used to evaluate the ability of the CNCPS to predict MP-allowable ADG (Ainslie, 1991). The isonitrogenous diets were based on corn silage or high-moisture ear corn and were supplemented with protein sources varying in protein degradability (urea, raw soybeans, 110°C roasted soybeans, 135’ C roasted soybeans, or corn gluten feed; Table 3). The CNCPS and NRC (1985)models were tested with the 30 growth periods (10 steers per 56-d period) when the calves responded to supplemental protein. The results are summarized in Figure 7. The NRC (1985)predicted the MP-allowable ADG with a bias of -30%, a n R2 of .46, and a standard error of the Y estimate of .10 kg. The CNCPS predicted CNCPS

Figure 7.Relationship of NRC (1985)and CNCPS predicted and observed metabolizable protein allowable ADG. The data include 30 growth periods of 56 d each from four experiments with growing Holstein steer calves (10 calves per observation). The standard error of the Y estimate for NRC and CNCPS systems is .10 and .05 kg, respectively, with a bias of -30 and 1.6% and an R2 of .46 and .57 for the respective systems. Downloaded from https://academic.oup.com/jas/article-abstract/70/11/3578/4705739 by guest on 04 March 2018

3589

CATTLE REQUIREMENTS AND DIET ADEQUACY

Table 3. Chemical composition of nitrogen supplements fed during the periods used to validate the CNCPSa 110°C RSTSB~

135'c RSTSB~

29.0 40.5 37.8 59.0

29.6 40.9 22.4

38.7 40.8

46.1

11.3 36.3

17.9

18.3 6.5

23.4 6.7

Item

RWSB~

Neutral detergent fiber, % Crude protein, % Protein solubility, % of crude protein Protein degradability, 016 of crude protein Neutral detergent insoluble protein, % of crude protein Acid detergent insoluble protein, % of crude protein

8.1

'CNCPS = Cornell Net Carbohydrate and hotein System. bRWSB = raw soybeans; RSTSB = roasted soybeans; CGF composites used to evaluate each feeding period.

MP-allowable ADG with a bias of l.0%, an R2 of 37,and a standard error of the Y estimate of .07 kg. These results suggest that the CNCPS has a structure that can be used to predict the supply of MP needed to meet cattle requirements, which is necessary to predict amino acid balances accurately. However, considerable unaccounted for variation still exists, and further research is needed to refine the ability of the CNCPS to predict the supply of MP under specific feeding conditions.

=

3.

4.

5.

0.

The CNCPS accounts for the following relation ships: Feed ME as a function of NDF, lignin, digestion and passage rates. Bacterial yield as a function of SC and NSC 2. pools, the rate at which the carbohydrates and proteins are degraded, and ruminal pH. Ruminal N requirements in relation to 3. microbial growth on intake of SC and NSC. The influence of carbohydrate on ammonia 4. production. The ME cost to excrete excess N. 5. 0. Maintenance requirement sensitive to animal and environmental conditions. 7. Growth requirements sensitive to variations in frame size and anabolic implants. Optimum growth rate for herd replacements. 8. 9. Body condition score and energy reserves. The CNCPS can be used to evaluate and diagnose diets, as follows: 1. 2.

Predicted intake for ration formulation and performance prediction and diagnosis. Predicted and actual performance. If daily gain or milk production is similar to the predicted value, the user should consider changes in animal type, environmental condi-

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Urea

35.8 28.0 53.2 70.8

0 287

10.0

0 0

1.0

100 100

corn gluten feed. These values are averages (9'0 of DMI of

Application

1.

CGF~

7.

tions, feed intake, feedstuff composition and processing and diet formula to further improve performance. Energy balance. If the energy balance is positive or negative in beef and dairy cows, the CNCPS gives the days for a condition score change [Table 2). A fiber deficiency. The CNCPS calculates and gives an effective fiber intake that can be compared to the requirement. Nonprotein N vs ruminally degraded protein. If the peptide or ammonia pools are too small, the user can easily select the appropriate form of N to add. Need for undegraded protein. If the supply of microbial protein is inadequate, sources of slowly degraded protein can be easily supplemented. Low ruminal pH. When forage NDF drops below 2 O % , bacterial yield is decreased by 2.5% for each 1% decrease in NDF.

Implications The CNCPS is a model that provides a biologically meaningful structure for evaluating cattle diets under widely varying conditions. Validations have indicated that it gives realistic estimates of animal performance. Although certain parameters will need future refinement, with lower aggregation models, the present model provides a structure that is biologically meaningful and can be used to develop cattle diets under diverse conditions.

Literature Cited Abdalla, H. O., D. G. Fox, and M. L. Thonney. 1988.Compensatory gain by Holstein calves after underfeeding protein. J. Anim. Sci. 66:2887. Ainslie, S.J. 1991. Management systems for Holstein steers to utilize alfalfa silage and improve carcass value. M.S. Thesis. Cornell Univ.,Ithaca, NY.

3590

FOX ET AL.

Ainslie, S. J., D. G. Fox, and T. C. Perry. 1992. Management systems for Holstein steers that utilize alfalfa silage and improve carcass value. J. Anim. Sci. 70:2643. ARC. 1980. The nutrient requirements of ruminant livestock. Commonwealth Agricultural Bureaux, London. Bartle, S.J., R.L. Preston, R. E. Brown, and R. J. Grant. 1990. Trenbolone acetate/estradiol combinations in feedlot steers: Dose-response and implant carrier effects. J. h i m . Sci. 70:1326. Beef Improvement Federation. 1986.Guidelines for uniform beef improvement programs (5th Ed.). Beef Improvement Federation, Raleigh, NC. Bruce, J. M., P.J. Broadbent, and J. H. Topps. 1984.A model of the energy system of lactating and pregnant cows. Anim. Prod. 38:351. Canas, R.,J. J. Romero, and R.L. Baldwin. 1982. Maintenance energy requirements during lactation in rats. J. Nutr. 112: 1876.

Cantrell, J. A., J. R.Kropp, S.L. Armbruster, K. S.Lusby, R. P. Wettemann, and R.L. Hintz. 1982.The influence of postpar. tum nutrition and weaning age of calves on cow body condition, estrus, conception rate and calf performance of fall-calving beef cows. Oklahoma Agric. Exp. Sta. Res. Rep. MP-112:53.

Carstens, G. E., D. E. Johnson, and M. A. Ellenberger. 1987.The energetics of compensatory growth in beef cattle. J. Anim. Sci. 65(Suppl. 11:263.(Abstr.I Crickenberger, R. G., D. G. Fox, and W. T. Magee. 1978.Effect of cattle size and protein level on the utilization of high corn silage or high grain rations. J. h i m . Sci. 46:1748. CSIRO. 1990. Feeding standards for Australian livestock. CSIRO Publications, East Melbourne, Aust. Cundiff, L. V., R.M. Koch, K. E. Gregory, and G. M. Smith. 1981. Characterization of biological types of cattle-cycle 11. IV. Postweaning growth and feed efficiency of steers. J. h i m . Sci. 53:332. Cunha, T. J. 1987. Variables in animal nutrition keep shifting the ‘requirements.’ Feedstuffs. 59(42):1. Danner, M. L., D. G. Fox, and J. R. Black. 1980.Effect of feeding system of performance and carcass characteristics of yearling steers, steer calves and heifer calves. J. Anim. Sci. 50:394.

Ferguson, J. D., and K. A. Otto. 1989. Managing body condition in cows. Proc. Cornell Nutr. Conf. 75. Ferrell, C. L., L. J. Koong, and J. A. Nienaber. 1986. Effect of previous nutrition on body composition and maintenance energy costs of growing lambs. Br. J. Nutr. 56:595. Fortin, A.,S.Simpfendorfer, J. T. Reid, H. J. Ayah, R.Amique, and A. F. Kertz. 1980.Effect of level of energy intake and of breed and sex on the chemical composition of cattle. J. h i m . sci. 51:804. Fox, D. G., and J. R. Black. 1984.A system for predicting body composition and performance of growing cattle. J. Anim. Sci. 58:725. Fox, D. G.,R. R.Johnson, R. L. Preston, T. R. Dockerty, and E. W. Klosterman. 1972.Protein and energy utilization during compensatory growth in beef cattle. J. h i m . Sci. 34:310. Fox, D. G.,C. J. Sniffen, and J. D. O’Connor. 1988. Adjusting nutrient requirements of beef cattle for animal and environmental variations. J. Anim.Sci. 66:1475. Garrett, W. N. 1980. Energy utilization by growing cattle aa determined in 72 comparative slaughter experiments. I n L. E. Mount (Ed.)Energy Metabolism. p 3.EAAP h b l . No. 26. Butterworths, London. George, P. D. 1884. A deterministic model of net nutrient requirements for the beef cow. Ph.D. Thesis. Cornell Univ., Ithaca, NY. Harpster, H. W. 1978. Energy requirements of cows and the effect of sex, selection, frame size, and energy level on performance of calves of four genetic types. Ph.D. Thesis. Downloaded from https://academic.oup.com/jas/article-abstract/70/11/3578/4705739 by guest on 04 March 2018

Michigan State Wniv., East Lansing. Houghton, P. L., R.P.Lemenager, G. E. Moss, and K. S.Hendrix. 1990. Prediction of postpartum beef cow body composition using weight to height ratio and visual body condition score. J. Anim. Sci. 68:1428. INRA. 1989. Ruminant nutrition. John Libbey Eurotext, Montrouge, France. Jenkins, T. G., and C. L. Ferrell. 1964.OutputAnput differences among biological types. I n Proc. Beef Cow Efficiency Symp. pp 15-37. Michigan State Univ., East Lansing. Johnson, D. E. 1986. Climatic stress and production efficiency. Utah Agric. Exp. Sta. Res. Bull. 51217. Klosterman, E. W., and C. F. Parker. 1976. Effect of size, breed and sex upon feed efficiency in beef cattle. Ohio Res. Bull. 1088.

Lemenager, R. P., L. A. Nelson, and K. S. Hendrix. 1880. Influence of cow size and breed type on energy requirements. J. Anim. Sci. 51:566. Lomas, L. W., D. G. Fox, and J. R. Black. 1982. Ammonia treatment of corn silage. I. Feedlot performance of growing and finishing cattle. J. Anim. Sci. 55:909. Loy, D. D., H. W. Harpster, and E. H. Cash. 1988.Rate, composition and efficiency of growth in feedlot steers reimplanted with growth stimulants. J. Anim. Sci. 66:2668. Marsh, W. E., D. T. Galligan, and W. Chalupa. 1988.Economics of recombinant bovine somatotropin use in individual dairy herds. J. Dairy Sci. 71:2944. Mertens, D. R. 1985. Recent concepts in optimizing nutrition of dairy cows. In: Proc. 1985 Monsanto Symp. Milligan, R. A,, L. E. Chase, C. J. Sniffen, and W. A. Knoblauch. 1981.Least-cost ration balanced dairy rations. A computer program user’s manual. Anim. Sci. Mimeo 54. Cornell Univ., Ithaca, NY. NRC. 1984 Nutrient Requirements of Beef Cattle. National Academy Press, Washington, DC. NRC. 1985. Ruminant Nitrogen Usage. National Academy Press, Washington, DC. NRC. 1987.Predicting Feed Intake of Food Producing Animals. National Academy Press, Washington, DC. NRC. 1988. Nutrient Requirements of Dairy Cattle. National Academy Press, Washington, DC. Oltenacu, P. A,, T. R. Rounsaville, R. A. Milligan, and R. H. Foote. 1981.Systems analysis for designing reproductive management programs to increase production and profits in dairy herds. J. Dairy Sci. 64:2096. Oltjen, J. W., A. C. Bywater, R. L. BaIdwin, and W. N. Garrett. 1886. Development of a dynamic model of beef cattle growth and composition. J. Anim. Sci. 62:86. Otto, K. L., J. D. Ferguson, D. G., Fox, and C. J. Sniffen. 1991. Relationship between body condition score and composition of 8-11 rib tissue in Holstein dairy cows. J. Dairy Sci. 74:852.

Perry, T. C., D. G. Fox, and D. H. Beermann. 1991a.Influence of final weight, implants, and frame size on performance and carcaas quality of Holstein and beef breed steers. Northeast Regional Agricultural Engineering Service Bull. 44. p 142. Ithaca, NY. Perry, T. C., D. G. Fox, and D. H. Beermann. 199lb.Effect of an implant of trenbolone acetate and estradiol on growth, feed efficiency and carcass composition of Holstein and beef breed steers. J. h i m . Sci. 69:4696. Rayburn, E. B., and D. G. Fox. 1990. Predicting growth and performance of Holstein steers. J. Anim. Sci. 68:788. Russell, J. B., J. D. O’Connor, D. G. Fox, P.J. Van Soest, and C. J. Sniffen. 1992. A net carbohydrate and protein system for evaluating cattle diets: I. Ruminal fermentation. J. Anim. Sci. 70:3551. Smith, N. E., and R. L. Baldwin. 1974. Effects of breed, preg nancy and lactation on weight of organs and tissues in dairy cattle. J. Dairy Sci. 57:1055.

-

3591

CATTLE REQUIREMENTS AND DIET ADEQUACY Sniffen, C. J., P. J. Van Soest, D. G. Fox,J. D. O’Connor, and J. B. Russell. 1992. A net carbohydrate and protein system for evaluating cattle diets: 11. Carbohydrate and protein avail. ability. J. h i m . Sci. 70:3582. Strasia, C. A., and D. R. Gill. 1990. Formulating feedlot diets. Great Plains Beef Cattle Handbook. Fact Sheet. 1800. Taylor, St, C. S. 1968. Time taken to mature in relation to mature weight for sexes, strains, and species for domesticated mammals and birds. Anim. Prod. 10:157. Thompson, W. R., D. H. Theuninck, J. C. Meiske, R. D. Goodrich, J. R. Rust, and F. M. Byers. 1983. Linear measurements and visual appraisal as estimators of percentage empty body fat of beef cows. J. Anim. Sci. 50755. Trenkle, A. 1990. Impact of implant strategies on performance and carcass merit of feedlot cattle. Roc. 1990 S.W. Nutr. Conf. p 13. Tempe, AZ.

USDA. 1980. U.S. standards for grades of feeder cattle. MarketService Bull. 586. Wahlberg, M. L, H. W. Harpster, and E. H. Cash. 1988. Nutrient utilization, eMciency and tissue gain in steers fed ensiled feedstufTs fiom the corn plant. J. Anim. Sci. 86:3021. Wildman, E. E., G. M. Jones, P. E. Wagner, and R. L. Bowman. 1982. A dairy cow body condition scoring system and its relationship to selected production characteristics. J. Dairy Sci. 85:495. Williams, C. B. 1988. Development of a simulation model to provide decision support in dairy herd nutritional management. Ph.D. Dissertation. Cornell Univ., Ithaca, NY. Woods, P. D. 1987. Algebraic model of the lactation curve in cattle. Nature (Lond.) 215:184. Woody, H. D., D. G. Fox, and J. R. Black. 1983. Effect of diet grain content on performance of growing and finishing cattle. J. Anim. Sci. 57:717.

APPENDIX

Appendix Table 1. Describing cattle of different frame sizesa

Frame codeb

Wt of feeder cattle at 28% body fat Bulls

Steers

Breeding females Heifers

205-d wt

428-d Wt

Mature wt

--1

2 3 4 5 6 7 8 9

400 433 487 500 533 587

480 520 580 800 640 880 720 760 800

320 348 374 400 428 454 480 508 534

800

833 667

162 170 180 190 199 208 218 227 237

400 433 467 500 533 567

284 281 297 315 33 1 348 385 38 1 400

600 633 667

*Mature female weights for various frame sizes are computed from BIF standards and published growth curves (George, 19841. Steers are assumed to be 28% body fat (low Choice grade) at mature female weight for that frame size, based on Smith et al. (19761, Cundiff et al. (1981), Jenkins and Ferrell(1984), and Harpster (19781, Bulls are assumed to be 20% heavier and heifers 20% lighter at the same degree of maturity than steers of the same kame size, based on Klosterman and Parker (19761, Harpster et al. (19781, and Fortin et al. (1980). Frame size 5 steer weight is similar to the U.S. average steer slaughter weight in 1991 (542 kg; 50% Choice),with a hi h percentage within a range of 395 to 844 kg [M. Berwin, USDA market news, Des Moines, IA, personal communication). %orresponding USDA 1980 feeder calf grades: 1-3 = S, 4 - 0 M, and 7-9 = L.

-

Appendix Table 2. Relationship of condition score to energy utilization in growing cattle Body condition score Multiplier*

1

For NE, For NE,,

1.10

.e55

3

5

7

9

.98 1.05

1 .oo

1.02 35

1.045 .90

1.oo

*Multiplier is used to adjust maintenance requirement and feed energy value; NE, is net energy requirement for maintenance and NEga is net energy value of diet for gain. Adjustments are based on the studies of Fox et el. (1972) and Abdalla et al. (1988).

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FOX ET AL.

3592

Appendix Table 3. Cow condition scorea Model

Dairy

cs

cs

Body fat, %

1

1

5.0

2

1+/2-

9.4

3

2

13.7

4

2+/3-

18.1

5

3

22.5

6

244-

26.9

7

4

31.2

8

4+/5-

35.6

9

5

40.0

Appearance of cow Cow extremely emaciated and listless, near death from starvation. Ribs, spine and hips very prominent. No visible fatty tissue. Cow appears somewhat emaciated; ribs, spine and hips are prominent. Can see individual ribs but they do not stand out. Can see some flesh along spine. Individual ribs not obvious; has some fat cover over ribs and hip bones. Can feel spine but it is not sharp. Can feel fat cover over ribs and on either side of tailhead. Backbone barely visible. Pressure required to feel backbone. Can feel considerable fat over ribs. Some fat in brisket, feels spongy over ribs. Patches of fat around tailhead. Very fleshy; brisket full, large fat deposits over ribs, back, tailhead, vulva and crotch; can’t feel backbone. Extremely fleshy and blocky. Bone structure not visible, nor can it be felt.

&Basedon a summary of 14 studies on empty body fat in female cattle by George (1984) and the condition scoring system of Cantre11 et a]. (1982).

Appendix Table 4. Adjustment factors for dry matter intake for cattlea Adjustment factor Age (age 1) Started on feed as yearling Breed (BI) Holstein Holstein x British Empty body fat, 46 (BFAF) 21.3 23.8 26.5 29.0 31.5

Feed additives 0 No anabolic stimulant Monensin only at 22 ppm Monensin only at 33 ppm Lasalocid only Estrogenic implant only Estrogenic plus trenbolone acetate implants Anabolic implant and monensin at 22 ppm Anabolic implant and monensin at 33 ppm Anabolic implant and lasalocid Temperature, O C (‘TEMP11 > 35, no night cooling > 35, with night cooling 25 to 35 15 to 25 5 to 15 -5 to 5 -15 to -5 < -15

Mud (MUD11 Mild (10-20 cml Severe (30-80 cml

Multiplier 1.10 1.08 1.04 1.0 97

90 .82 .73 94 .88 .84 .92 1.oo 1.oo .94 .eo .98 .85

.eo .90 1.oo 1.03 1.05 1.07 1.18 .85 .70

&NRC(1987). bCorresponds to average frame steer equivalent weights (kg)of c 350, 400, 450, 500, and 550, respectively. Downloaded from https://academic.oup.com/jas/article-abstract/70/11/3578/4705739 by guest on 04 March 2018

CATTLE REQUIREMENTS AND DIET ADEQUACY

3593

Appendix Table 5. Adjusting maintenance requirementsa a1

-

az

= 5.17 .75Tp = .007(20 - EATp'

EATp NEm FFM NEFP NEFP

+

- (al + a~lBW.750(BE) (L1 E

= =

SA HP E1 I LCT MECS NEmcs NEmenv NEmenv FFMenv

.077

NE,/NEma (DMI - FFMl NEga for growing cattle (DMI - FFMI MEC.NE1, for lactating cattle .09

P

=

I

= = = = =

BW75

(ME1 - NEFPVSA (7.36 - ,296 WIND + 2.55 HAIR.MUD2.HIDE TI + E1 39 - I.(HP) SA (LCT - EATc)/I 1.37 MECS - ,138 MEcs2 + .0105 M E c s ~- 1.12 NEm + NEmcs, or if panting, NEm.NEmhs NEmenv/NEma

'a1 is thermal neutral maintenance requirement (Mcal/d per BW75); T, is previous temperature, "C; EAT, is previous effective ambient temperature, "C; and NEm is net energy required for maintenance adjusted for acclimatization, breed (BE, Appendix Table 61, lactation (L, Appendix Table 81, and grazing WO effects, Mcal/d; FFM is feed for maintenance (no stress), kg of DM/d; NEFP is net energy available for production, Mcalld; NEma is net energy value of diet for maintenance, Mcalhg; NEga is net energy value of diet for gain, McalAcg; NEl, is net energy value of diet for lactation, Mcallkg; MEC is metabolizable energy content of diet, Mcal/kg; SA is surface area, m2; HP is heat production, (Mcal/m2)/d; ME1 is metabolizable energy intake, Mcal/d; LCT is animal's lower critical temperature, "C; Ttnz is temperature at thermal neutral zone, "C; I is insulation value, IoC/(Mcal/m211/d (Appendix Table 7); TI is tissue (internall insulation value, [" C/ (Mcal/m2)l/d (Appendix Table 7); E1 is external insulation value, [" C/(Mcal/m211/d (Appendix Table 7); WIND is wind speed, kph; HAIR is effective hair depth, cm; MUD2 is mud adjustment factor for external insulation (Appendix Table 7); HIDE is hide adjustment factor for external insulation (Appendix Table 7); Tc is current temperature, "C; EATc is current effective ambient temperature, "C; MEcs is metabolizable energy required due to cold stress, Mcal/d; NEmcs is net energy required due to cold stress, Mcal/d; NEmhs is 1.07 for rapid shallow panting and 1.18 for open. mouth panting; NEmenv is net energy for maintenance required adjusted for breed, lactation, grazing, acclimatization, and stress effects, Mcal/d; and FFMenv is feed for maintenance (adjusted for stress), kg of DM/d.

Appendix Table 6. Breed maintenance requirement factors, birth weights, and peak milk productionalb Cow maintenance multipliers Breed Angus Charolais Chianina Hereford Limousin Maine Anjou Murray Grey Simmental South Devon Tarentaise Ayrshire Brown Swiss Friesian Guernsey HolsteinC Jersey Brahman

Nonlactating (BE) 1 1

1 1 1 1 1 1.06 1 1 1.12 1.12 1.12 1.12 1.12 1.12 .89

Lactating (L)

1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.ooc 1.20 1.20

Calf birth wt, kg (CBW 31.8 36.6 30.9 34.1 36.3 29.5 29.5 41 31.8 34.1 31.8 38.6 35.0 31.8 40.0 31.8 31.8

Average peak milk, kg (YB) 6 5 4 5 4.5

6 5 11 6 5 36 37 37 35 43 34 4

Birth weight adjustment; age of dam, yr (Q11 1.8 3.6 3.6 2.3 1.8 3.6 3.6 3.2 3.6 3.6 3.6 3.6 3.6 3.6 3.6 3.6 3.6

CQ2)

.e 2.3

(43)

(Q4)

.2

.5 1.4 .5

.e

.e .e

.2 0

.Q

.O .9

2.3 2.3 1.4 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3

.Q .9 .9

.9 .9 .9

.Q .9

.Q

.Q .Q

&From P. D. George (1984). bVariable names (BE, L, BW, YB, Q1, Qz, 4 3 , Q4) are used in various equations to predict cow requirements. CNo adjustment is made for Holstein cows because of differences in hormonal status.

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.Q

.o 1.4 1.4

.o 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4

FOX ET AL.

3594

Appendix Table 7. Determination of insulation adjustment factors (IJa Flesh code Factor ~

Qualifier ~

~~

~

1

5

Coat depth, cm 9

< .5

1.0

7

External factorb 14 11.0 7.5 10

1.5

3.0

~

Age code

Tissue factor Newborn calf 1-6 mo old 5 1 2 mo old Adult

1 2

3 4

2.5 6.5

2.5 6.5

2.5 6.5

5.5

6.8

6.0

9.0

8.0 12.0

Wind speed, km/h 1.6 6.4 12.8

5 4

25.6

3

17 13.5

5.5

8

9

4.0

5

6.5

q i s s u e and external insulation values are combined for use in equations for determining body heat loss. bExternal insulation is adjusted by using the following multipliers to adjust for coat condition (mud 21: dry and clean (code 11, 1.0; some mud on lower body (code 21, .8; mud on lower body and sides (code 31, .5; covered with mud, cold rain, or wet snow (code 41, .2. External insulation is also adjusted for hide thickness (HIDE), using the following multipliers: thin hide (code 11, .8;average hide (code 21, 1.0; heavy hide (code 31, 1.2. Examples of hide types are: Holstein, code 1; Angus, code 2; Hereford, code 3.

Appendix Table 8. Predicting milk production of beef cowsa YP Y2 Y3 Y4 LM L4 L3

Lz AM A4 A3

A2 BM B4 B3 B2 CM C4 C3 C2 MM MM4 MM3 MM2

= (.125 PL = B O O YP = .825 YP = .925 YP

+ .375)

= YP+40

LM-5 LM -10 L M + 10 = 5.30 - .075 LM = 5.65 - .090 L4 6.65 - .110 L3 = 4.00 - .050 L2 = (LN(lO.0) - LNCAMII/(LN(LM + 14) - 1) = aN(9.25) - LN(A411/(LN(L4 + 14) - 11 = a"8.25) - LN(A3)1/(LN(L3+ 141 - 1) = [LN[6.00) - LN(A2))/1LN(L2 + 14) - 1) = BM/ILM + 14) = B4/U + 141 = B3/(L3 + 14) = B2/(Lz + 141 = UM((TL + 141BM) (Em-CM(TL + 14)))) (YP/lO.O) = (A4(ffL + 14lB4I(EXR- C4(TL + 141))) (Y419.25) = (A3IffL + 14IB31 (EXR- C3ffL + 141))) (Y318.251 = (A2(ffL + 14IB21 (Em-C2dL + 14)11) (Y2/6.001 = = =

*LN is natural logarithm; EXP is exponential function YB is breed average peak milk yield, kg/ d; PL is within-breed adjustment factor; YIJ is peak milk for mature cow adjusted for production level within breed, kg/d; Y2 is peak milk for 2-yr-old,kg/d; Y3 is peak milk for 3-yr-old,kg/d; Y4 is peak milk for 4 and > 10 yr old, kg/d; LM is day of peak yield for mature cow; L4 is day of peak yield for 4 and > 10 yr old; L3 is day of peak yield for 3-yr-old; L2 is day of peak yield for 2-yr-old;AM is Woods equation "a" coefficient for mature beef cow; A4 is Wood's equation "a" coefficient for 4 and > 10 yr old; A3 is Wood's equation ua"coefficient for 3 yr old; A2 is Wood's equation "a" coefficient for 2 yr old; BM is Wood's equation '73" coefficient for mature cow; B4 is Woods' equation "b" coefficient for 4 and > 10 yr old; B3 is Woods' equation 'b"coefficient for 3-yr-old;B2 is Woods' equation "b" coefficient for 2-yr-old;CM is Woods' equation "c" coefficient for mature cow; C4 is Woods equation "c" coefficient for 4 and > 10 yr old; C3 is Woods' equation "c" coefficient for 4 and > 10 yr old; C3 is Woods' equation "c" coefficient for 3-yr-old; C2 is Woods' equation "c" coefficient for 2-yr-old;TL is day of lactation, d; MM4 is milk yield for a 4 and > 10 yr old dam, kg/d; MM3 is milk yield for a 3-yr-olddam, kg/d; MM2 is milk yield for a 2-yr-old dam, kg/ d; and MM is milk yield for mature cow, kg/d; BM, B4, B3, and B2 in the last four equations are exponents.

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CATTLE REQUIREMENTS AND DIET ADEQUACY

3595

Appendix Table 9. Growth requirements of heifers from calving to maturitya MW EM KW B W WG IF T > EG NG AF PB GP

+

= 366.7 33.3 FS = .e926 MW - 4.7 = 1/(36.MW28) = -LOG(KW/MW) = MW.EXP(-B.EXP(-KW.T))

.W .LN(W/MW) - -KW then WG WG = = = =

1,000. 1848 = 0 ,8928 4.7 ((.05603 WG + .00001265 WG2) (W.FA)75)/400 22.5 (1 E m - .0053613) (100 - ((76.3 - ,973 AF)+ AF)) (.79951

=

EG.PB..O1/.50

-

~

~~

~~

&MWis predicted mature weight, kg; FS is cow frame size (Appendix Table 11; EM is mature cow empty body weight, kg; KW is live weight growth rate; coefficient W is expected current weight, kg; T is cow age, days; WG is weight gain, g/d; EG is empty weight gain, kg/d; NG is metabolizable energy requirements for gain, Mcal/d and 400 is percentage efficiency of ME for gain. 10; FA is frame adjustment (Appendix Table 11); AF is empty body fat, %; PB is protein in gain, O h ; GP is metabolizable protein required for gain, g/d; and .50 is efficiency of use of MP for growth.

Appendix Table 10. Optimum growth rate for breeding herd replacement heifersa Frame size 3

1

Item Optimum wt at first estrus, kg Mature wt, kg Age, mo 3 7 12 18 24 30 36 42 48 54 60

Beef

Dairy

Beef

31 1 400

-

Dairy

Beef

-

101 63 40 25 16 10

Dairy

-

590 926 636 454 227

681 426 302 196

7

Beef

38 1 533

347 467 499 635 545 363 136

602 402 253 180

5

127 83 54 35 23 15

Dairy

Beef

412

-

152 100 66 44 29 19

-

68 1 863 817 590 318

84 1 591 396 265

Dairy 443 667

600 636 817 426 545 272

761 529 349 230

9

68 1 908

392 654 442 298

177 119 79 53 36 24

908 726 409 202 136 92 62 42 28

&Dailygain that will result in optimum weight for first estrus at 426 d, first calving at 24 mo, and mature weight at 60 mo of age. Optimum weight and growth rate are targets at which reproductive cycles are initiated and reinitiated as soon as possible without excess fat being deposited that will inhibit milk production and reproduction. Separate values were developed for beef and dairy because of differences in management. In beef cows, higher planes of nutrition are necessary preweaning for cow milk production and reproduction and growth of feeder calves. The Brody growth function was used for beef as described by George (1984) and the Gompertz function was used for dairy as described by Taylor (1968).

Appendix Table 11. Adjusting body weight of growing cattle varying in frame size and sex to the weight of a 1984 NRC medium-framed steer of equal composition (equivalent shrunk weight) Frame size Sex

1

2

.97 1.16 1.45

BO 1.07 1.34

3

4

5

6

7

8

9

.64 .77 97

.61

.58 .70 .87

Adjustment factora Bulls Steers Heifers

.83 1.00 1.24

.77 93 1.16

.72 .87 1.09

.68 .82 1.02

.73 .92

&Multipliedtimes actual weight to obtain weight of frame size 3 (1984 NRC medium frame) steer of equal body composition for use in predicting energy and protein requirements for gain. Factors were computed by dividing frame size 3 steer weight (Appendix Table 11 by bulls, steers, and heifers in each frame size category. Downloaded from https://academic.oup.com/jas/article-abstract/70/11/3578/4705739 by guest on 04 March 2018

FOX ET AL.

3596

Appendix Table 12. Pregnancy requirementsa IF 0 < IF 731 < IF 1008 >

IF T FF

--

-- -

T 731 then WO CBW - Q1 T 1,006 then WO CBW Q2 T < 1.481 then We CBW - 43 3387 then WO CBW Q4

-

- .000000107TGMEXP((.0885- .00012820TG)TGll - .0001094TGMEXP((.OS88- .00000334TG)TGll 5.505 FP + 8.527001 FF (.0840470 - .0003087TG1(EXP((.05814- .00010310TG)TG)1 TGEST>203 THEN C W 28 ,539 cw ,08375.CW (.2885 - .000832TGMEXP((.04378- .00007800TG1TG)) =

(.0000881

9

LO3452

--

FP FE CW

-

0

IF CE CP NW IF NE

-----

TCEST>210 THEN NW

CT

.539

Nw

-

28

.08375*NW (1.3884 - .0038414TGMEX)P((.O2475- .0000348TG)TG)) TGEST>238 THEN U W 23

UW IF UE UP FE RP PE

WP

.OS2

UW

-

.E3288 U W

FE FP

+ CE t NE + UE + 8.877 + CP + Cl' + UP + .134 FE + 1.13

(FE(W0/36.411 8 .DO1 RP [W9/38.4)/.50

aWQis birth weight adjusted for the age of dam, kg; 4 4 is age of dam adjustment factor for a 4 and > 10 yr old (Appendix Table 81; 43 is age of dam adjustment factor for a 3-yr-old (Appendix Table 8); Q2 is age of dam adjustment factor for a 2.yr-old (Appendix Table 8); Q1 is age of dam adjustment factor for a 1-yr-old(Appendix Table 8); TG is day of gestation, d; CBW is calf birth weight (Appendix Table 8); FF is fetal fat accretion, g/d; FP is fetal protein accretion, g/d; FE is fetal energy, kcal/d; CW is cotyledon accretion rate, g/d; CE is cotyledon energy, kcal/d; CP is cotyledon protein, g/d; N W is placental accretion rate, g/d; NE is placental energy, kcal/d; CT is placental protein, g/d; U W is uterus accumulation, g/d; UE is uterine energy, kcal/d; Up is uterine protein, g/d; FE is total energy accumulation for pregnancy, kcal/d; RP is net pregnancy protein accretion, g/d; PE is metabolizable energy required for pregnancy, Mcal/d; and W P is metabolizable protein required for pregnancy, g/d.

Appendix Table 13. Predicting energy reservesa RF EW = AV = AB BT = CP = If cs > BR = BP = EC EF =

5 + (.25 AF - 1.25) (CS - 11 .E928 W - 4.7 (23.7 - .027 AF) .7095 (23.7 - .027 RFI .7995 EW (AV..O1)

cs

-

5, CP 5 BT l.75 + .0825 (CP - 1)) BR - .75 BT (BR/AB) 100 ((RF - 5)/100) EC = 9.499 EF

-

RE

*RF is empty body fat available for mobilization, %; CS is condition score (Appendix Table 3); EW is empty body weight, kg; AV is empty body protein, %; AB is empty body protein adjusted for available fat reserves %; BT is body protein, kg CP is condition score with an upper limit for protein accumulation; BR is body protein available adjusted for condition score, kg; BP is body protein that can be mobilized, kg;EC is empty body weight adjusted for available protein reserves, kg; EF is body fat that can be mobilized, kg; and RE is total body energy reserves, Mcal.

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