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AN ECONOMIC EVALUATION AND REPLACEMENT MODEL
FOR THE LACTATING DAIRY COW
INCLUDING BIOLOGICAL COMPONENTS

By

Joseph G. Hlubik

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF SCIENCE

Department of Dairy Science

1979

ABSTRACT
AN ECONOMIC EVALUATION AND REPLACEMENT MODEL

FOR THE LACTATING DAIRY COW
INCLUDING BIOLOGICAL COMPONENTS

By
Joseph G. Hlubik

A computer model was developed to estimate the annu-
alized net present value of a dairy cow, enabling compari-
sons among cows as an aid in determining Optimum voluntary
replacement patterns. Expected values (probabilistic
sense) were used to account for the uncertainty underlying
involuntary culling. Milk production, hence income, is
estimated based upon DHIA estimated mature equivalent milk
yield, standardized for age and season of calving. Simi-
larly, a credit is made for the sale of cull animals.
Costs include both feed and nonfeed variable costs. The
feed cost component characterizes dry matter intake and
nutrient requirements over the life cycle, and within a
lactation, based on expected production performance. Feed
disappearance is estimated using a linear programming sub-
system which balances a diet and, in turn, projects feed
disappearance. The model is flexible; additional sub-
systems can easily be entered that deal with significant

biological and economic factors.

DEDICATION

This thesis is dedicated to

the Sacred Heart of Jesus Christ.

ii

ACKNOWLEDGMENTS

I would like to express my appreciation to John and
Sarah Dumschat for encouraging me to enter a Master's pro-
gram at Michigan State and for their hospitality and friend-
ship. Sincere thanks to Winston Ingalls for his much appre-
ciated advice and friendship. Special thanks are due to
Drs. Russel Erickson and Roy Black for invaluable assist-
ance and also to members of the committee: Dr. Roy Emery,
Dr. Glenn Johnson, and Dr. Donald Hillman.

I am indebted to the Department of Dairy Science for
financial assistance during the course of my studies.

I would also like to thank Mrs. Mary Hillman and

Susan MacMahon for helping prepare and type the manuscript.

iii

TABLE OF CONTENTS

INTRODUCTION 0 O O O O O O O C C O O O O O O O O 0
LITERATURE REVIEW OF REPLACEMENT MODELS . . . . .
REVIEW OF IMPORTANT BIOLOGICAL AND ENVIRONMENTAL

FACTORS THAT DEFINE THE INPUT-OUTPUT RELATIONSHIPS

OF THE DAIRY cow 0 O O O O O O O O O O O O O 0
Milk Production . . . . . . . . . . . . . . . .

Estimating the Lactation Curve . . . . . .
Effects of Nutrition on the Lactation Curve
Equations for Estimating the Lactation Curve
Environmental Factors Affecting the Shape of
the Curve . . . . . . . . . . .
Lactation Number and Persistency .
Season of Calving . . . . .
Days Open and the Calving Interval
Breeding Problems . .
Persistency and the Calving Interval
The Dry Period . . . . . . . . . . .

Factors Influencing Dry Matter Intake . . . .

Energy Content of Feeds . . .
Impact of Fermented Feeds . . . .
Impact of Crude Fiber . . . .
Relative Intake of Forages . .
Influence of Protein on Feed Intake .
Dry Matter Intake and High-Producing C
Seasonal Influences on Intake . . . .
Estimating Voluntary Intake . . . . . . .
General Observations Concerning Feed Intake
During the Lactation Cycle . . . . . . .

Nutrient Requirements . . . . . . . . . . . .

Mobilization of Nutrients from Body Stores
Energy and Fiber Requirements . . . . . .
Energy Requirements vs. Genetic Potential
Protein Requirements . . . . . . . . . . .

iv

0

Growth . . . . . . . . . . . . .
Expected Herd Life . . . . . . .
Reasons for Removal . . . . . . .

Longevity and High Producing Cows

Conclusions Drawn from the Literature Review . .

THE DAIRY COW MODEL . . . . . . . .
Economic Decision Rules . . . . .
Introduction to the Model . . . .
Cost . . . . . . . . . . . . . .
Subsystem: Milk Production . . .

Estimating Lifetime Production
Subsystem: Dry Matter Intake . .

Development of the Dry Matter Intake

Impact of Fermented Feeds . .

Impact of Energy Density and Crude

tent of the Ration .
Subroutines: DMILAC and DMIDRY
Characteristics of the Diet .

Subsystem: Requirements . . . .

Protein and Energy . . .
Calcium and Phosphorus .
Crude Fiber . . . . . .
Requirements for Growth .

Subsystem: Balancing the Ration
RESULTS AND DISCUSSION . . . . . . .
SUMMARY AND CONCLUSIONS . . . . . .
BIBLIOGRAPHY . . . . . . . . . . . .
APPENDIX (Computer Program)

Equation

Fiber Con-

O

Page

110
114
124

Table

7a

7b

10

ll
12

LIST OF TABLES

Review of Replacement Models . . . . . . . .

Expected Impact of Alternative Quantities of
Fermented Feeds on Daily Dry Matter Intake
of Lactating Dairy Cows . . . . . . . . .

Mature Equivalent (ME) Factors for Body
Weight Derived from Two Studies . . . . .

Probability of Failure (Involuntary) of Cows
by Lactation Number . . . . . . . . . . .

Reasons for Dairy Cattle Replacement . . . .
Percent of Total Gulls by Lactation Number .
Proportion of Cows Removed from the Herd Dur-
ing the Second Through Sixth Lactation
When Divided into Four Groups According
to First Lactation Production . . . . . .
Percent of Cows Removed from the Herd of
Those Surviving the Previous Lactation
When Divided into Four Groups According
to First Lactation Production . . . . . .
Average Season Effects on ME Milk . . . . . .

Regression Coefficients Defining Dry Matter
Intake/Day During the First Twenty Weeks
0: Lactation 0 O 0 O O O O O O O O O O O 0

Regression Coefficients Characterizing
Intake by Age in Early Lactation . . . . .

Milk (Average Daily) vs Milk (Weeks 6-8) . .

Regression Coefficients Defining Dry Matter
Intake in Relation to Moisture and Time .

vi

27

46

48
so
51

52

S2

62

69

71
73

75

Table Page
1} Regression Coefficients Characterizing the In—

fluence of Weeks Into Lactation and

Moisture Percent of the Ration on DMI/cwt. 76
14 Regression Coefficients Defining Energy,

Crude Fiber and Milk per Day on Dry

Matter Intake (DMI/th) . . . . . . . . . 79
15 Estimated Maximum Dry Matter Intake . . . . . 82

16 Energy Mobilization in Early Lactation Pro-

posed as a Percent of Requirements . . . . 87
1? Restriction on Maximum Energy Mobilization

and Re-conditioning During Lactation . . . 90
18 Nutrient Requirement Specifications for

Lactating Cows . . . . . . . . . . . . . . 92
19 Nutrient Requirement Specifications for

Dry Cows . . . . . . . . . . . . . . . . . 93
.20 Linear Programming Balance Matrix . . . . . . 95
21 Performance Characteristics of Cows of

Various ME Milk Production Levels at 3
Different Ages . . . . . . . . . . . . . . 99

22 Lactation Feed Summary for Cows Producing
Various Levels of Milk . . . . . . . . . . 100

25 Feed Consumption of a Cow Producing 12,100
Lbs 0f 5 O 5% Milk 0 O O D O O O O O O 0 O O 102

24 Feed Consumption of a Cow Producing 14,500
Lbs of 3.5%IMilk . . . . . . . . . . . . . 105

25 Feed Consumption of a Cow Producing 17,000
Lbs Of 505% Milk 0 o o o o o o o o o o o o 104

26 Feed Consumption of a Cow Producing 19,400
Lbs Of 505% Milk 0 O O O O O I C O O O O O 105

27 Projected Annualized Variable Net Present
Value of Replacement Heifers . . . . . . . 107

vii

LIST OF FIGURES

The Relationship Between Nutritive Value,
Dry Matter Intake ( ) and Energy
Intake (- ' -) o o o c o o o o o o o o o

 

Effect of Moisture Content on Dry Matter
Intake of Lactating Cows . . . . . . . .

Impact of Ration Fiber Content on Dry Matter
Intake of Lactating Cows Fed Four Levels
of Grain and Two Qualities of Hay . . . .

Energy Utilization of Lorna Cow . . . . . .

Relationship of Crude Fibre Content (%) in
the Ration and pH in the Rumen . . . . .

Net Revenue vs Time . . . . . . . . . . . .
Flow Chart of the Model . . . . . . . . . .

Expected Annualized Variable Net Present
value 0 O O 0 O O O O O O O O O 0 O O O 0

viii

Page

22

26

28

38

4O

59

108

INTRODUCTION

The objective of this research is to develop a dyna—
mic macro-level bio-economic model of the dairy cow, inte-
grating the following subsystem: 1) milk production; 2)
dry matter intake; 5) nutrient requirements; 4) growth; and
5) odds of involuntary individual animal removal from the
herd. The model's economic focus is to estimate the ex-
pected net present value of a cow at any point in her life-
time. Thus, the model can be used or a decision-aid by
dairymen when making cow replacement decisions. Questions
such as: "Should I replace a four-year old cow whose ma-
ture equivalent is 15,000 lbs of milk with a heifer whose
mature equivalent is 16,500 lbs?" can be asked.

The model has potential for other uses also, includ-
ing projection of feed budgets, estimation of variable
costs, projection of cash-flow statements, estimation of
herd turnover rate, and forecasting herd milk output.

Other studies have dealt with the replacement prob-
lem, but none have focused on the characteristics of the
particular replacement animal or the animal being replaced.
Production level is usually assumed at an average herd

value, and if genetic improvement is considered it is

usually expressed as a yearly rate of herd improvement.
Dry matter intake and diet characteristics have been typi-
cally considered as fixed factors or modeled in a rela-
tively naive manner. Therefore, the focus of this study
was on accurately forecasting feed disappearance for cows
of alternative mature equivalents.

This study is only a beginning point in the deveIOp-
ment of a "dairy cow model." As parameters become more re-
fined, corrections can be incorporated. The program is com-
posed of various subsystems which can be easily added to or
deleted.

With the age of electronic identification and com-
puter analysis upon us it is not difficult to imagine such

programs becoming important in dairy management.

LITERATURE REVIEW OF REPLACEMENT MODELS

Deve10pment of replacement decision theory, computer
simulation modeling and refinement in biological conceptual
framework and parameters have been concurrent with the evo-
lution of models dealing with the question of optimal re-
placement of dairy cows. Jenkins and Halter, (1962),
Redman and Kuo, (1969), and Giaver, (1966), presented the
problem as one involving maximization of present value us-
ing a multi-stage decision analysis. Whereas a single-stage
decision policy is obtained by looking at each decision in-
dependent of other decisions in time, a multi-stage policy
can be obtained by looking at all possible decision points
for the entire time period being considered (Ahmed, l97#).
The policy which yields the maximum net returns will be the
optimal sequence of decisions. For example, each year a
farmer must decide whether to keep or replace an animal
based upon the expected returns of the cow versus her re-
placement candidate. The series of decisions which maxi-
mizes expected returns over the years considered is re-
ferred to as an optimal policy.

Solutions obtained by Giaver (1966), Redman and Kuo
(1969) involve a Markovian programming approach. A Marko-

vian process is a stochastic process where the probability

distribution of outcomes at any given stage depends only on
the outcome at the last preceding stage (i.e. if we use a
Markovian process and know the outcome of the last observa-
tion, we can neglect any information we have about previous
observations in predicting the future) (Buffa and Dyer,
1977). Hutton (1966) developed a simulated replacement
model which was intended for use at the farm level. Inter-
ested dairymen filled out a 50-item questionnaire specify-
ing conditions present at each particular farm. The com-
plexity of the questions made the model impractical.

Rundell (1967) examined replacement strategies among
6 operationally practical systems of culling cows. The
strategies employed were: (1) mature equivalent (M.E.)
milk production, (2) M.E. gross milk income, (5) actual
milk production, (4) actual gross income, (5) income over
feed cost, and (6) present value of expected gross income
of a cow and her subsequent replacement candidate. The ob-
jective criterion was maximization of income over feed
costs. Results of his study showed no significant differ-
ences among the strategies examined. Smith (1971) and
Stewart 33 a1. (1977) formulated generalized production
prediction models. As the models evolved, levels of vari-
ability of the factors advance from discrete to more con-
tinuous variation.

Table 1 shows a comparison among the various rephace-

ment studies. The following observations help illustrate

some of the strengths and weaknesses of the models examined

and the various factors taken into consideration.

(1)

(2)

(3)

(4)

(5)

(6)

The probability of success or failure of any
given lactation was handled as a stochastic fac-
tor across the various studies. Smith and
Stewart 23 a1. separated the probability of
death from the probability of failure in order
to more accurately account for salvage value.
Production prediction of milk was handled in a
variety of ways across the studies. Genetic im-
provement of replacement candidates was taken
into account by Smith, Rundell and Stewart 23
31. by assuming an increase in production per
year of replacements over the herd average pro-
duction level. Stewart 33 al. compared cows to
an "average" producing replacement candidate to
determine whether or not to cull.

Except for Stewart gt 31. differences in body
weight among cows within any age group were
ignored.

The calving interval was handled as a stochastic
factor only by Giaver and Smith.

Season of calving effects on production were not
accounted for in any of the studies to date.
Feed disappearance was not accurately accounted

for in any of the studies. Stewart 22 31. at-

tempted to deal with the problem in a more ad-
vanced manner but did not include enough flexi-
bility in their approach. Since feed costs
comprise over 60% of the variable costs of milk
production this is a serious weakness of pre-
vious replacement models. 1

Keeping the above observation in mind it is believed
that the prOper approach is to focus attention upon a more
flexible biological model which could be used to predict
inputs and outputs for any given cow at any stage of her
lifetime. Once the biological model was defined, an econ-
omic analysis could then be incorporated and the replace-
ment problem considered. Thus the objectives of this
study are two-fold: l) to begin the process of deve10ping
a biological dairy cow model and 2) to use the model to ad-
dress the question of the prOper time to replace a dairy
cow and who she should be replaced with. The analysis can
. be used in answering questions such as: "Should I replace
a 4-year old cow whose mature equivalent is 15,000 lbs with
a heifer whose mature equivalent is 16,500 lbs?"

The first part of the thesis examines the important
biological parameters needed to be taken into considera-
tion. Background research findings are presented to make
the reader aware of the sources of information used to de-
fine the parameters and the controversy that still envel—

Opes some of them such as the area of protein requirements.

7

The second part of the thesis explains the economic
analysis employed to solve the replacement problem and il-
lustrates how the model Operates. The quantitative and
qualitative restrictions and parameters used in this par-
ticular model are definedthere.

The last section presents the results and conclu-
sions of this study pointing out the strengths and weak-

nesses of the model to date.

 

 

 

 

 

 

 

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2.3.53 . .033.

REVIEW OF IMPORTANT BIOLOGICAL AND ENVIRONMENTAL FACTORS
THAT DEFINE THE INPUT-OUTPUT RELATIONSHIPS OF THE DAIRY CON

Milk Production
Estimating the Lactation Curve

The output of milk over the lactation cycle is char-
acterized by a rapid rise during the first few weeks after
calving until peak production per day is reached. Output
then begins to decline in a linear fashion. Rate of rise
in output, peak production and rate of decline in output
vary with individual cows. Ridler and Broster (1969) ex-
amined the milk yields of 218 Friesian and Shorthorn first
and second-half cows which had been individually rationed
and subjected to constant managerial conditions. These re-
cords were analyzed to find the major characteristics of
variability in the milk production cycle, with a view to
prediction of performance. The values for the coefficients
of variation were: (1) slope of curve from calving to
peak yield, 15%; (2) days from calving to peak yield, 45%;
(5) peak yield, 15%; (4) rate of decline per week in the
three months after peak yield, 50%; (5) rate of yield de-
cline per week in the period from peak yield to M peak
yield, 25%; and (6) lactation yield, 20%. Peak yield was

11

12

found to be the dominant feature of the curve for individ-
ual animals within groups. It markedly influenced the to-
tal output of milk in the lactation and the rate of decline
in milk yield in mid-lactation. First lactation heifers
were more persistent than cows, and cows calving in the
autumn were more persistent than those calving in the

spring.

Effects of Nutrition on the Lactation Curve

Line and Westgarth (1969) showed that the percent-
age decline in yield was linearly related to percentage
reduction in feed consumed. Hillman 23 a1. (1975) also
found that feed intake was linearly related to milk yield.

Trimberger 23 31. (1972), in an experiment involv-
ing levels of concentrate fed, found that the weeks of
peak production for both actual milk and 4 percent FCM
were uniform among the different concentrate feeding grougs
and fairly uniform for individual cows for the three years,
but variations from cow to cow were large. They concluded
that peak production was higher in all groups on liberal
grain during the three years when compared to the controls
with one exception. (The slape of the milk curve with re-
spect to days into lactation is an indication of persist-
ency. Animals on liberal grain dropped in production

slower than those fed limited grain.)

13

Van Ostergaard (1978) studied the effects of feeding
different concentrate levels throughout the lactation in-
dependent Of daily milk yield. The rate of decrease in
milk yield was very dependent upon the manner in which the
grain mix was fed. The decrease in milk yield is markedly
lower when the grain mix was fed constantly from day to
day instead of according to yield. Also, Thomas and Brown
(1974) found that switching from a liberal grain feeding
ratio Of 1:1 to 1:5.5 pounds of grain per pound of milk
caused a decrease in persistency from 92 percent to 79 Per-
cent Of the previous month's milk yield.

Broster (1974) states that persistency is dominated
by the individuality of the cows, but is also influenced
by the system of feeding. The ability of the cow to "es-
cape" at least temporarily, the effect Of underfeeding,
utilizing body reserves to support milk yield, adds to the
problem Of variation in persistency.

The potentially dangerous situation of low feed in-
take at the critically important stage of early lactation
is met bya withdrawal Of reserves to meet deficiencies.
Broster (1974) determined that the cow's peak milk yield
is critical in that her propensity to direct feed to milk
in mid-lactation is favored by a high peak yield and reduced
by a low one. The total milk yield output in the lactation
is dominated by that peak yield. Broster further stated

that once the peak yield is established, an Optimal rate

14

of decline in feed intake exists for an Optimal level Of
milk production, fertility and refurbishing the bodyweight

losses incurred earlier.

Equations for Estimating the Lactation Curve

Wood (1969, 1970, 1976) has studied the environ-
mental factors which affect the shape of the lactation
curve and how it varies between parities,of cows and among
herds. Also, he assessed the importance of variation in
seasonality from year to year with particular reference to
herd production forecasts. He found that in general cows
calving in the same parity at the same time of year showed
similar curves, modified only by total yield and abnormal
season of production.

He proposed using the following equation to explain
the shape of the lactation curve:

b on
Yn a an e

where: Y a yield of milk being measured at time period n
of the lactation

n a week into lactation
e a the base of natural logarithms
a,b,c a coefficients defining the lactation curve in
question.
The curve reaches a turning point at 11p a -(b/c).

The turning point is independent of "a" which is the

15

scaling factor. Thus, "b" and "c" define the shape of the
curve. It is therefore possible to construct a curve for
any given cow in any lactation by choosing the apprOpriate
value for "a" provided it possesses the shape defined by
"b" and "c" which have low coefficients of variation.

Wood (1969) ran a goodness of fit test using a
sample of 859 Friesian lactation records drawn from 1964
to 1965. The data consisted Of sets of weekly milk weights
from calving to week 44 of lactation, or earlier if the
cow went dry. Lactations were classified by parity number
(1, 2, 5, 4+) as well as season Of calving. The parame-
ters "a," "b" and "c" were evaluated for each curve. At
best, the model accounted for 91.2 percent of the varia-
tion in the logarithm of weekly yield and, at worst, 78.8
percent, with an average Of 82.5 percent. The model fit
best for those lactations beginning during the March-July
period and showed the poorest fit during the September-

December period.

Environmental Factors Affecting the Shape of the Curve

Shultz (1974) applied Wood's equation to Holstein
Friesian cattle using Wisconsin DHIA records and attempted
to determine which environmental factors had an effect on
the lactation curve. Lactation number, season of calving,

days open, days dry previous to lactation and an indication

16

of the management level of the herd as measured by the
folling herd average were investigated. 0f the factors
tested, lactation number, season of calving and days Open

exerted the greatest affect on the shape Of the curve.

Lactatiop Number and Persistency

Shultz (1974), in agreement with Wood (1969), Ridler
and Broster (1969). Sikka (1950) and Ripley gt a1. (1970),
showed that persistency of yield decreases with lactation

number.

Season Of Calving

Season of calving was found by Shultz (1974) to in-
fluence the shape of the curve in the following ways: (1)
Cows calving during the January-April period tend to be
more persistent than cows calving during other seasons;
(2) Cows calving during the May-June period show evidence
of both a smaller increase in output at the beginning and
the end of lactation; (5) Cows calving during the July-
October period show a decrease in the relative height of
the peak, resulting in a greater persistency than the p0pu-
lation average in agreement with Appleven (1969) and
Sikka (1950); (4) Cows calving during the November-Decem-
ber period produce a larger than normal percentage during
mid-lactation with smaller prOportions at the beginning

and end, consistent with the findings of Wood (1969).

17

There was a definite seasonal stimulation to milk
production, regardless of the stage of lactation, exerted
during the March-June period. The shape of the curve was
significantly determined by the relative position of these

calendar months in the lactation.

Days Open and the Calving Interval

The calving interval is the sum of the days in milk
and the days dry and depends upon how soon cows are re-bred
after parturition. Days open refers to the period of time
between parturition and subsequent conception. Ideally,
to achieve maximum production a cow should calve every
twelve months. However, this is usually not the case.

Some high producers do not return to estrus soon enough

after parturition to achieve a yearly calving interval.

Breeding Problems

Heritability and repeatability of factors relating
to breeding problems are very low. Johansson and Hansson
(1940) found a slight tendency of repeatability, .056, of
the calving interval. They assumed heritability of the
length of the calving interval to be in the range of 0 to
5%. Trimberger 23 a1. (1972) found it impossible to pre-
dict the breeding efficiency (the number of inseminations

required per conception) of a cow by her previous record.

18

Sonderegger gt gt. (1977) prOpose that excess di~
gestible protein, particularly at levels 250-500 gram per
cow per day lengthened the interval between parturition
and first service. They also found that an abundant
energy supply, particularly during the first 60 days after
parturition, decreased the interval from first service to

conception and from parturition to conception.

Persistenqy and the Calving Interval

Sanders (1925, 1950), Gains (1927) and Johansson gt
gt. (1940) have shown that persistency of milk yield de-
creases for cows with shorter calving intervals. Shultz
(1974) found that cows open more than 159 days produced
a significantly greater prOportion of their total during
months 9 and 10 of lactation than cows Open less than 70
days. Actually, it is not the length of the calving inter-
val that affects milk production as much as the stage of
pregnancy. Pregnancy begins to exert an effect upon lac-
tation approximately 140 days after conception (Foley gt
g1. 1972). At this time mammary cell numbers and milk
yield begin to decrease, as compared with non-pregnant
lactating cows.

If the calving interval is 550 days, this means
that the cow has become pregnant approximately 550 - 280 a

70 days after parturition since the average gestation

19

length is 280 days. The effects of pregnancy would begin
to be noticeable about 70 + 140 a 210 days into lactation.
For a longer calving interval of 420 days, the effects Of
pregnancy would not become evident until (420 - 280) +
140 a 280 days. This is approaching the time at which
she would be dried off to prepare her for the next lacta-
tion.

Smith (1975) examined lactation persistency and de-
rived factors for extending milk production beyond the
standard 505-day lactation, accounting for variability in
calving intervals. These factors were derived from 61,975
New York DHIA records and were split into 2 categories,
lactation l and lactations 1. These factors can be ap-
plied to the lactation curve across calving intervals be-

ginning 4 months after conception.

The Dry Period

Cows should be given a rest period of 6-8 weeks be-
tween lactations to allow refurbishment of the mammary
gland (Foley gt gt. 1972). Shorter or longer periods of
time will reduce subsequent milk production. Cows not
given a normal dry period produced only 62 to 75 percent
as much milk as their twins which were given a rest of 60

days between lactations (Foley gt gt. 1972).

20
Factors Influencing Dry Matter Intake

The stimulus initiating feed intake arises from an
interaction of environmental and biological conditions
mediated through the hypothalamus (Bailey, 1970; Baum-
gardt, 1970). These conditions define the physiological
and physical status of the animal at any time. Body size,
sex, age, species, previous nutritional history and produc-
tion state (pregnancy, lactation, growth and fattening, en-
vironment and genetics) "set" the energy demands of the ani-
mal. Animals attempt to eat to satisfy this demand and
achieve energy balance. Animals change voluntary intake in
response to a change in energy demands and thus intake can-
not be considered a constant attribute of any particular
feed (Butler and Bailey, 1975).

In ruminants, the rate of energy expenditure, en-
vironmental temperature, qualitative characteristics of
the diet and the physical effects of food in the gut are
significant factors influencing the level and day to day
changes in food consumption and therefore the amount of
energy. Over a rather wide range of energy concentrations
in the ration, animals are able to adjust the amount of
feed voluntarily consumed so as to maintain equal caloric
intakes (Baumgardt, 1970).

Ruminants appear to be exceptions to the energy
homeostasis mechanism. Feed intake appears to operate in

reverse on many roughage feeding programs. For example,

21

ruminants consume more early-cut immature forage than late-
cut mature forage. Since the digestible energy content of
the early-cut forage is higher, the animal consumes more
energy from the early-cut forage (the energy intake differs
between these two forages). This is an example of a break-
down in a homeostatic system due to a secondary but potent
force. The very low energy concentration in the late-cut
forage coupled with its bulky nature results in a filling
of the digestive tract capacity at a level of intake below
that which is called for by the homeostatic mechanism
(Baumgardt, 1970). This phenomenon can be demonstrated in
nonruminant species as well as in ruminants if the ration
is diluted to a very large extent with indigestible, bulky
material. Such a response was demonstrated by Cowgill with
dogs as early as 1928 (Balch and Compling, 1962) and has
since been shown in many species including chickens, rats,
swine, sheep and cattle (Baumgardt, 1970).

Feed intake is proportional to body size when eating
capacity is restricted by intestinal fill and undigested
residue (Conrad, 1964). Mather and Rimm (1958) found the
ratio of feed intake to Wo‘73 was equal to the least-squares
regression for adjusting intake for differences in body size
of lactating cows. Blaxter gt gt. (1961) concluded that

voluntary intake in sheep varies with metabolic size

(w0'74).

22

Energy Content of Feeds

When the nutritive value is high, fill does not
limit feed intake and rats, dairy heifers, lactating dairy
cows and sheep adjust the amount of feed eaten to regulate
energy intake. This explains why it was possible for
Blaxter (1950) and Crampton (1957) to claim that the amount
of feed consumed in terms of dry matter increases with in-
creasing concentrations of net energy in the rations,
whereas, Greenhalgh and Runcie (1962) found no causative
relationship between feed intake and digestibility. Fig-
ure 1 demonstrates the relationship of nutritive value of

rations and feed dry matter and erengy intake.

 

 

 

 

o
.34 A
65 on
4-? .545
CIA m.
HLfi pm
a“. 54%
(DH) Cfl
43.x pus
p‘\~ w
T 90 new
.6 a:
>3 1a
H
c: 33
175‘ 2.0 2.2 2.5 570

Nutritive value (K cal ME/g DM)

Figure l. The Relationship Between Nutritive Value, Dry
Matter Intake ( ) and Energy Intake (- - -).

 

 

_ Source: Butler, G. and R. W. Bailey. 1975. Chemis-
try and Biochemistry of Herbage. (Academic Press: ‘New York)

Vol. 3, p. 141.

25

Dry matter intake increases with nutritive value
until a value of 2.2 Kcal of metabolizable energy per gram
of dry matter (65 to 70 percent apparent digestibility) is
achieved. Above this value dry matter intake frequently
decreases (Butler and Bailey, 1975). Energy intake (Kcal
of metabolizable energy per gram of dry matter) also in-
creases with nutritive value until a concentration of ap-
proximately 2.2 Kcal, after which it is relatively constant
(Butler and Bailey, 1975).

Digestion of low nutritive value herbage within the
gastrointestinal tract seems to form the basis for the con-
trol Of intake, while changes caused within the animal's
tissues by the absorbed end—products of digestion form the
basis of the control of high nutritive value herbages
(Baumgardt, 1969).

Physical characteristics Of the diet such as volume
displacement, surface area of particles, length of cut of
forages, pelleting, grinding and heat processing, energy
content of the ration, the rate of passage Of digesta out
of the rumen and the rate of absorption Of nutrients, all
affect the amount Of energy derived from the feed.

In order to partially account for these factors,
Montgomery gt gt. (1965) prOposed the concept of multiply-
ing a measure of digestibility and nutrient density and
arriving at a caloric density measurement (Kcal of digesti-

ble energy/ml of diet). This provides a basis for estimadng

24 q

total dry matter intake. Baumgardt gt gt. (1976) showed
that DE/ml (caloric density) accounted for 88 percent of
the variation in body weight gains, whereas DE/gm accounted
for only 67 percent. Bull gt gt. (1976) studied the rela-
tionship between caloric density and energy intake in 24
lactating cows fed five mixed diets of alfalfa hay and
concentrate. For the most dilute rations, A and B, physi-
cal fill was limiting intake. DE intake of diets of increas-
ing energy density, C,{D and E, was similar indicating that
gut fill was not limiting intake and that physiological
regulation was occurring. In a similar study by DePeters
(1975) with lactating cows, physical fill limited the in-
take of all four rations of grass and hay. These two data
sets were combined by Baumgardt (1977) who found neutral
detergent fiber (NDF) and bulk density (gm/ml) highly cor-
related with dry matter intake (r = 0.91 and 0.95, respect-
ively). Dry matter digestibility was not correlated with
intake. Mertens gt gt. (1975) and Thornton gt gt. (1972)
found that density, the digestion coefficient for NDF and
the rate of NDF digestion are parameters that are related
to rumen retention time and rate of passage.

The maintenance of relatively constant energy intake
may be related to some end product of digestion. In rumi-
nants this may be acetate, the major VFA produced in the
rumen. Rate of utilization of acetate may limit voluntary

intake. Thus, in early lactation there is generally an

25

increase in energy intake which may be linked to an increase

in acetate utilization for milk synthesis.

Impact Of Fermented Feeds

Investigations have shown that the voluntary intake
of silage is lower than that of hay made from the same crOp,
harvested at the same time. Work by Thomas gt gt. (1975)
showed that the lower dry matter content of the silage was
not a causal factor per se in limiting intake. This work
is supported by Baumgardt and Clancy (1975) who studied
intake of alfalfa forage in five forms and found that vol-
untary intake was not significantly correlated with dry
matter content of the forage per se. However, there was
an indication that some chemical compound in the silage
juice may have depressed intake. Data of Clark (1972),
Thomas gt gt. (1970) and Brown (1965) were analyzed by
Hillman gt gt. (1975) to elucidate the effect of moisture
content of the ration on intake and are graphically illus-
trated in Figure 2. The data show that DMI expressed as a
percent of body weight changed approximately -.016 to -.025
per 1% increase in the moisture content of the ration above
20%.

Table 2 estimates the percentage decline in intake
with increasing moisture additions to the ration (Hillman

23 2.1..- . 1975)-

26

    

 

 

3-5—

5.0L
,\
3'
:5 -.017 (Clark, 1971)
3’3 2.5..
0
M
m
43
a
H
5;: 2.0..
.p
.p
s
.2
E (Thomas,
G 1970)

los—

(31‘ W11. 1965)
l 11 1 1 J
20 4O 60 80 100

Ration Moisture Percent

Figure 2. Effect of Moisture Content on Dry Matter Intake
of Lactating Cows.

 

Source: Hillman, D. gt gt. 1975. Least Cost Ra-
tions: A Look at the Michigan System. Unpublished. Mich-
igan State Univ., East Lansing.

27

Table 2. Expected Impact of Alternative Quantities of Fer-
mented Feeds on Daily Dry Matter Intake of Lactat—
ing Dairy Cows‘

 

 

 

Percent Moisture Expected Intake Expected Daily Dry
in the Ration as a % of Potential Matter Intake for a
Intake 1400 lb Cow Producing
60 lbs Milk

20 or less 100.0 45.8

25 96.4 44.2

50 95.0 42.6

35 89.9 41.2

45 84.2 58.6

50 81.6 57.4

 

‘The percent moisture in the ration is a proxy for
the impact of fermented feeds. It does not depict the impact
of wetting dry feeds.

 

Source: Hillman, D. gt gt. 1975. Least Cost Ra-
tions: A Look at the Michigan System. Unpublished. Mich-
igan State Univ., East Lansing.

Impact of Crude Fiber

The level Of fiber in the diet affects the energy
density of the ration, the pH Of the rumen and the rate of
passage of material through the digestive system; thus the
percent fiber has an influence on the level of intake. The

data in Figure 5 show the impact of varying the roughage:

28

 

3.2 P -,019 (lepe)
3'
tn 5.0 L
E5
0
.M
w
.p
8 2.8 -
n
(D
p
4.?
s
.5
f: 2.6 '-
Q

 

L L L L l

V"

20 25 50 35
%»Fiber in Ration
Figure 5. Impact Of Ration Fiber Content on Dry Matter In-

take of Lactating Cows Fed Four Levels of Grain
and Two Qualities of Hay.

 

Source: Hillman, D. gt gt. 1975. Least Cost Ra-
tion: A Look at the Michigan System. Unpublished. Mich-
igan State Univ., East Lansing, as adapted from: Stoddard
and Anderson, J. Dairy Sci. 48:798, 1965.

29

concentrate ratios on daily feed intake based On a study

by Stoddard and Anderson (1965). At low levels Of concen-
trate consumption, dry matter consumed as a percent of body
weight increased by .076 lbs per percent decrease in fiber.
From .25 lbs of grain fed above 20 lbs of milk to .5 lbs,
the increase in intake as per percent decrease in fiber is

.046. The change for the next increment is .019.

Relative Intake of Forages

Intake of legumes tends to be higher than that of
grasses when they are harvested at the same digestibility
(Wilkinson, 1976). Since legumes generally contain more
protein and minerals than do grasses, they contain more
cell contents and a lower proportion of digestible cell
walls than grasses when harvested at the same digestibil-
ity. Cell contents are believed to be digested at a faster
rate than cell walls. Also, the buffering effect Of pro-
tein and minerals in legumes which alters rumen pH should
give a faster rate of digestion of the digestible cell
wall portions. Stage of maturity at harvest influences
the digestibility of forages and therefore can influence
intake. Its effect on the rate of decline in digestibil-
ity differs between forage species, and is greater after

seed-head emergence than prior to it (Wilkinson, 1976).

30
Influence of Protein on Feed Intake

Rogers gt gt. (1975) have shown in rats that intake
is depressed when diets are: (1) low or devoid of a single
AA, (2) contain an excess of a single AA, or (5) contain a
high level of protein. According to Baumgardt (1969), ex-
tremes in protein level have marked effects on intake by
ruminants. The low feed intake on a low protein diet is
related to the inability of rumen microbiota to function
properly and failure of such a diet to support normal
growth and milk production. Depressed intake on very high
protein rations is due in part to the high Specific dyna-
mic activity (SDA) associated with protein metabolism and
in part due to a deficiency of enzymes involved in amino

acid catabolism (Baumgardt, 1969).

Dry Matter Intake and High-Producing Cows

For a limited period of time, high-producing cows
regulate energy intake in a very acceptable manner. How-
.ever, some cows with a lower genetic ability for milk pro-
duction and also high-ability cows late in lactation, will
deposit body fat at an increasing rate rather than convert
the extra energy into milk. Body fat deposition becomes
uneconomical, but it does not necessarily mean that the
cow lacks the ability to regulate energy intake. At least

two other possible explanations are presented by Baumgardt

51

(1969). First, the set point on the cow's energy has been
raised to an unusually high level. This can be explained
on the basis of selection for high production. Such selec-
tion may have resulted in animals that would be considered
"pathological." Since lactation has a higher biological
priority than fattening, obesity is not observed as often
in dairy cattle where limited feeding is practiced. The
second explanation involves no change in the set point of
the regulator. The main difference is that energy status
is monitored on the basis of a circulating metabolite pool
or undissipated heat load rather than on the basis of en-
ergy balance per se. Thus, increasing enzymatic potential
(the level of which is under genetic control) can be visu-
alized as removing metabolites from the circulating pool

at an accelerated rate in the fattening animal. Intake is
related to energy output, but all functions using energy,
including fattening, are considered as drains on the energy
metabolite pool, which is the parameter being monitored
(Baumgardt, 1969).

Eckles and Reed (1910) state, "The cause of the dif-
ference in the amount of milk produced is the amount of
feed that cows are able to consume and use above maintemmre
requirements." There is little doubt that high levels of
milk production are accompanied by great appetites. It is
also true, however, that the variation from cow to cow in

this regard is very great. Not all cows that have a greater

52

ability for milk production demonstrate greater appetites.
The result is that some cows produce milk with great losses
in body weight, while others are able to more nearly meet
their energy requirements with increased intake (Flatt,
1967). A Pennsylvania cow (Kreig, 1975) produced over
50,000 pounds Of milk in one lactation. During this time
it was calculated that she was consuming over 7 percent of

her body weight in DMI per day.

Seasonal Influences on Intake

Environmental temperatures have predictable results
on feed intake. Homeotherms increase feed intake in the
cold and decrease intake in the heat. There is a differ-
ence in temperature effect on younger and older animals
and also between lactating and non-lactating animals (Baum-

gardt, 1969).

Estimating Voluntary Intake

Conrad and his co-workers (1964) at the Ohio experi-
ment station published the results of the factors which in-
fluence the dry matter intake of dairy cows. The basic
equation they derived was:

Total DMI a Weight/1000 ' 10.7/(1 - % digested)‘

 

‘Percent digested expressed as a decimal fraction.

33

The equation assumes that cows excrete a maximum of 10.7
lbs of indigestible dry matter per day (McCullough, 1975).
This estimate has since been challenged by Bull gt gt.
(1976) who propose a value of 15.2. Using 10.7 as a base
value, Conrad accounted for a major portion Of the varia-
bility when requirements for maintenance and milk produc-
tion were added to the equation. When this was done, the

equation for calculating maximum feed intake was:

Max. DMI 310.7-Weight/10004- .58 Weight0'73 + .33 Milk + .53

Brown and Chandler (1978) derived an intake predic-
tion equation from data assembled from nineteen experiments
conducted at eleven universities across the country. Each
observation represented the average daily intake per cow
during a twenty-eight day period. The data set included
4,155 Holstein records and 704 Jersey records. The regres-

sion equation developed had an R2

value of .74, and an
average error or 12. % when predicted values were compared
to actual Observed values.
The equation reads as follows:
ln DMI 2 b0 + Season + b1(DIL) + b2(ln DIL) + b3(ln Milk)
+ b4(BF) + b5(BW) + b6(CF) + b7(CF)2
where: DMI a dry matter intake,kilograms per day.

DIL a the average number of days into lactation
for a cow or a group Of cows

Milk 2 kilograms of milk produced per day by a cow
or group of cows

34

BF a the kilograms of butterfat produced per day
BW a the body weight in kilograms

CF a crude fiber, percent of ration dry matter
CF2 a crude fiber squared

Season = season of the year

ln a natural logarithm

b0 and bl 2 parameters

The estimated parameters were:
Season = .0418 (Fall and Winter)

-.0041 (Spring)
-.0576 (Summer)

bl = -.oos27 (DIL) b5 . .000675 (3w)
b2 . .148075 (1n DIL) b6 = .018001 (or)
b3 . .339220 (1n Milk) b7 = -.000557 (CF2)
b4 = .099266 (BF)

Hillman gt gt. (1975) developed an intake equation
using mean data of Slack gt gt. (1960) which incorporates
characteristics of the diet such as net energy and mois-
ture content of the ration. The moisture factor was in-
tended to account for the negative impact Of fermented
feed on intake, the quality of fermentation being influ-
enced by the moisture content.

The equation reads as follows:

Y a 1.021 + (-.005)Xl + (.0187)X2 + (1.476)X5
where: Y a dry matter intake/100 lbs of body weight

X1 a percent moisture in the ration

35

X2 . lbs of milk
X3 2 estimated net energy (ENE) in mcal/lb of DM

The R2 value for the equation is .78.

General Observations Concerning Feed Intake During the

Lactation Cycle

Intake is relatively low immediately postpartum. Dry
matter consumed can be below 2% of body weight of the ani-
mal at this time (Jorgenson, 1978). Intake begins to rise
sharply during the first few weeks of lactation and usually
peaks between weeks 7 and 12 (Hillman, 1975). At this time
it will be 50-40% greater than it was immediately post-
partum. Peak intake occurs after peak production, usually
following within a couple of weeks. Once peak consumption
has occurred, intake progressively declines in a linear
fashion with milk yield. The relationship ranges between

.02 and .07 lbs of dry matter per lb of milk produced.

Nutrient Requirements

With the onset of lactation, there is initiated a tre-
mendous nutrient sink, the mammary gland, which requires
acquisition of energy and protein as well as certain vita-
mins and minerals in quantities far exceeding maintenance
levels in order to achieve an output of milk commensurate

with the genetic ability of the animal. Only that level

36

of milk production which can be supported by the most
limiting nutrient available will be attained.

Peak production level significantly affects total
milk yield of a given lactation. Therefore, attention to
the nutritional status of the animal during the period ex-
tending from freshening to peak daily production is of cri-
tical biological and economic significance. This situation
is aggravated by the fact that although early lactation re-
quirements are greater than at any other stage of lactation,

intake levels are the lowest.

Mobilization of Nutrients from Body Stores

Animals meet their needs by absorption of nutrients
from the digestive tract. However, mobilization of body
stores can contribute significantly in meeting certain de-
mands, particularly in early lactation (Flatt gt gt., 1967).
This is especially true in regard to energy. Energy mobili-
zation from fat stores is typical for high-producing dairy
cows. It is not unusual_for cows to lose lOO-200 lbs of
body weight during the first 75 days of lactation; some
cows have been noted to lose over 400 lbs (Moe, 1971).

The famous "Lorna" cow (Flatt gt gt., 1967) mobilized an
average of 20 Mcal of body reserves per day during the
first four weeks of lactation. This quantity represents
over 40% of her daily energy requirements during that

period. During early lactation (weeks 1—8) her production

57

ranged from 55 to 27 Mcal of milk energy per day. Figure

4 illustrates Lorna's energy balance throughout lactation.

Parameters concerning the specific relationship of

energy mobilization with genetic potential, body condition

and ration composition are not explicitly defined, however

the following Observations are presented:

(1) Hickman gt gt. (1971) found that animals which

(2)

(3)

lost the most weight or recovered weight most
slowly were the higher producers. Poos gt gt.
(1978) noted that mature cows lost more weight
than first lactation animals. These Observa-
tions suggest that higher producers have a
greater capacity for fat mobilization.‘
Davenport and Ricks (1969) noted body weight
losses in early lactation for cows given a spe-
cified feeding level, were greater for fat ver-
sus thin cows but not for medium conditioned
animals versus thin animals.

Flatt gt gt. (1967) related energy mobilization
to the roughage concentration of the ration of
cows whose peak milk yield ranged from 25 to 40
kg per day. Cows on the high roughage ration
(60% alfalfa) mobilized an average of 10.1 Mcal
Of body tissue per day during the first 8 weeks
of lactation while those on the 40 or 20% alfalfa
rations only mobilized 7.0 and 5.5 Mcal respec-

tively. Production during this period was 22.4

58

Mcal of milk energy daily for cows receiving the
60%ialfalfa ration as compared with 19.0 and
14.4 Mcal by cows consuming 40 or 20% of the ra-
tion dry matter as hay. The average body tissue
lost during early lactation was 6.9 Meal/day.
The effect of restricting intake during early
lactation was to reduce milk yield rather than

increase tissue loss.

 

100i
Early “to Ll"
-80’
a
u
2
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21 2‘ 39 42 6 -3

Wuhspouparm
The utilization of energy by Lorna (cow 3884), :1 hi h roducin dai w
She produced 8,768 kg 4% fat corrected milk durifig 31 is 305-dgay 13:51:10}:
and her average body weight was 643 kg. Each bar represents the mean of
two .b-day total energy balance trials. The extension of a bar below the base
line indicates loss of body tissue. Rations D, E and F were 60:40, 40:60 and
20: 80 alfalfazconcentrate respectively. The balance trials were conducted
4, 8, 2|, 24, 39, 42 weeks post partum. The measurements when she was
dry and pregnant were made 6 to 3 weeks pro-perm»;

Figure 4. Energy Utilization of Lorna Cow.

 

Source: Flatt, W. P. et gt. 1967. Energy Utiliza-
tion by High Producing Dairy—Cows. II. Summary of Energy
Balance Experiments with Lactating Holstein Cows. Energy
Metabolism of Farm Animals. (Oriel Press Limited: New-
castle Upon Tyne, England). Edited by K. L. Blaxter, p.
225.

59
Energy and Fiber Requirements

Maintenance requirements of lactating cows was found
to be 75 Kcal/kg of body weight'75 (Moe gt gt., 1972). Be-
cause maintenance requirements depend on the level of acti-
vity, an allowance Of 10% is included in the 1978 NRC re-
quirements. Blaxter (1962) found that the yield of milk
exhibits diminishing marginal return as the level of energy
consumed increases. This could be partially due to de-
creased digestibility of the ration as the level of energy
consumed increases. Reid (1965) and Tyrell and Moe (1974)
suggest digestibility of high concentrate rations is inverse-
ly related to the level of energy intake, amounting to an
average decrease in digestibility of 4% for each multiple
of maintenance requirements ingested. The digestibility
decreases as the prOportion of grain increases when hay or
haylage is the only forage. Cellulose and hemicellulose
digestibility appear to be affected most, possibly due to
a negative effect on the cellulolytic bacterial pOpulation
exerted by a decrease in pH when high levels Of grain are
fed (Kaufman, 1976). Decreased pH is also believed to
negatively affect starch digestibility (Wheeler gt gt.,
1976). According to Kaufmann there is a direct relation-
ship between crude fiber in the ration and pH in the rumen
as illustrated in Figure 5.

Crude fiber is a rough estimate of chewing time of

feed or rumination. Chewing in turn results in salivation.

4O

  

 

65
:n
0‘ 60 pH . 5.115 + .066 x % Crude
Fibre
r2 ' 081
55

 

10 ll 12 l5 14 15 l6 l7 l8 19 20 21
% Crude Fibre

Figure 5. Relationship of Crude Fibre Content (%) in the
Ration and pH in the Rumen.

Ruminant saliva contains large quantities of bicarbonate
which acts as a buffer to prevent a decrease in the pH
(Emery, 1979). The pH of the rumen influences the ratio

Of acetic:pr0pionic acid produced which in turn influences
the fat content of the milk (Kaufman, 1976). When the
ratio of acetate to propionate decreases as is the case

in high grain rations, it is believed to be influential in
partitioning energy from milk to body fat synthesis. Thus,
ruminal pH is an important consideration in terms of ration

digestibility and milk yield. Kaufmann (1976) recommends

41

at least 20% crude fiber (CF) in the ration to maintain

prOper rumen function and avoid lactic acidosis. With 2
times a day feeding he lowers the limit to 17.5% of the

ration dry matter as crude fiber.

Dean gt gt. (1969) used a value of 15% as a minimum
level of CF in the California computer ration balancing
program but have since changed the limit to 17% as a result
of problems with cows going off feed.

Along with the quantity of fiber, the quality of
fiber must also be considered. Roughages that are finely
chapped or fermented exert a less pronounced effect in
maintaining an optimally functioning rumen than is indi-
cated by the amount of crude fiber contained (Thomas, 1979).
The "effective" fiber capacity Of corn cob meal, on the
other hand, is greater than that of alfalfa hay, although the

actual crude fiber content is much lower (Van Soest, 1969).

Energy Requirements vs. Genetic Potential

Bath gt gt. (1971) reported that cows with greater
inherent potential utilize feed more efficiently and con-
sume more feed per unit of body weight than low producers.
Their observations are presented in Table . Blaxter
(1966) and Broster gt gt. (1969) have also shown that cows
with a great capacity for milk production respond by giving
more milk per unit of feed as compared to cows of lower

capacity.

42
Protein Requirements

Protein requirements of dairy cattle are not well de-
fined due to the complexity of the digestive process of ru-
minants. Proteins entering the rumen can be either digested
by the rumen microflora or bypass to the lower G.I. tract.
Most bacteria in the rumen first deaminate dietary protein
subsequently using the ammonia released in the process to
build bacterial protein. Because bacteria can synthesize
'protein from ammonia, non-protein nitrogen (NPN) which is
converted to ammonia can be added as a source of nitrogen
in some instances to support protein anabolism. The con-
centration of ammonia in the rumen is a determinant in the
rate of microbial growth when energy is not limiting (Sat-
ter, 1978b). However, there is a maximum concentration of
ammonia in the rumen after which additional amounts are of
no benefit as far as microbial activity is concerned.
Satter and Roffler (1975) suggest this maximum is achieved
when the ammonia concentration in the rumen exceeds 5 mg %.
On typical dairy cattle rations this would be equivalent
to 12 to 15% crude protein (CP) in the diet. Huber (1976)
proposes NPN to be of benefit for predominantly corn silage
rations when requirements are as high as 14 to 14.5% CF.

In vitro studies by Helferich gt gt. (1976) have shown that
net protein synthesis does not reach a maximum until the

ammonia concentration is 15 to 20 mg %. Orskov (1976) has

43

shown that the ammonia level in sheep facilitating optimum
rumen synthesis is greater than 5 mg %.

The concentration of ammonia from the degradation
of dietary proteins will determine the additional amounts
of NPN which could additionally benefit microbial protein
synthesis. Factors affecting the rate of degradation of
dietary protein include: physical characteristics of the
protein, the amount of readily fermentable carbohydrates,
the pH of the rumen and other factors affecting the nutri-
tional environment, as well as retention of the feed in
the rumen (Satter, 1978a).

Protein requirements are met by microbial protein
synthesized in the rumen and undegraded digestible protein
that has escaped fermentation and to a limited extent by
protein mobilization from body stores during early lacta-
tion (Setter, 19786). In the lower GI tract of ruminants the
quality or amino acid array presented is important as well
as total amounts. Microbial protein has a relatively high
biological value in this regard (Satter, 1978) and is
therefore an excellent complement to the undegraded dietary
protein in meeting the amino acid requirement of dairy cat-
tle under most conditions.

Foldager (1977) tested the requirements of protein
and the efficacy of NPN addition to diets using 68 Holsteins
during the first 20 weeks post-partum. Seventeen cows were

assigned to each of the 4 treatment groups. The first two

41+

groups received rations of only plant protein containing
12-15%»CP in DM (group NC) and l5-16% (group P0). The
other two groups were also fed rations with 15-16% CP, but
approximately 25%rof the total nitrogen came from NPN as
urea (group U) or ammonia (group AU). It was concluded
that high yielding cows fed rations of corn, corn silage
and limited hay require no more than 15% CP in DM, which
is approximately equal to 80-90% of 1971 NRC standards.
Poos gt gt. (1978) found that 11-12% CP is an adequate
level to support first calf heifers in early lactation
but is not adequate for mature cows. Cressman gt gt.
(1977) obtained similar results.

Daily body weight losses in Foldager's experiment
(1977) averaged -.954, -1.845, -.692 and -l.200 kg for
groups NC, PC, U and AU respectively. Taking a value of
.954 kg/day of weight loss for the NC groups, it is possi-
ble to estimate a feasible amount of body protein that was
possibly mobilized based on 1978 NRC.

.954 kg/day x 520 g protein/kg body tissue mobil-
ized (1978 NRC) - 505 g protein. Estimating intake at
19.75 kgs DM (Foldager, 1977) the following is postulated:

0 rotein
13.75 kg feed consumed ” 1'5% CP

This means that the calculation for CP could be Off

 

by 1.5% CP and it would be masked by protein mobilization.

45

Lamb 23 gl. (1975) found that cows fed rations con-
taining 15.1 or 16.1% CF in the ration showed no differ-
ence in milk yield. Van Horn 33 31. (1968) found no sig-
nificant difference in performance between groups of cows
fed 15.5% CF and 15.2% CF using soybean meal as a source

of protein supplement.

Growth

Heifers entering the milking herd are still grow-
ing, and will continue to do so at a decreasing rate until
they approach 84 months of age. McDaniel and Legates (1965)
derived a cubic regression of weight on age within-year-
season on 1,595 Holstein cows. The equation is:

Y . 757 + 20.91 M - 0.2056M2 + .oooeem3
where: Y a estimated weight

M 3 age in months

Mature equivalent factors relating weight to age can
be established by using weight at 84 months as a mature
weight to compare with weights at different ages. Data of
Matthews and Fohrman (1954) were analyzed similarly. Table
5 lists the mature equivalent factors derived from both
sets of data. Almost identical factors result from both

studies.

46

Table 3. Mature Equivalent (ME) Factors for Body Weight

Derived from Two Studies.

 
 

 
  

_- - v... .1- ..._---_.- -A_-._r-._a_.____._..-.—.~_... *-—--__—.-——— -_.__.-—_. - —-.___ ~_ H.— ,

 

 

 

 

McDaniel and Legates Matthews and Fohrman
Age Fraction of ME1 Fraction of ME

(Months) Mature Wt. (Weight) Mature Wt. (Weight)
24 .784 1.275 .775 1.290
28 .816 1.225 .810 1.255
52 .844 1.185 .841 1.189
56 .870 1.149 .868 1.152
40 .892 1.121 .892 1.121
44 .912 1.096 .915 1.095
48 .950 1.075 .951 1.074
52 .945 1.058 .947 1.056
56 .957 1.045 .960 1.042
60 .968 1.055 .971 1.050
64 .977 1.024 .980 1.020
68 .984 1.016 .986 1.014
72 .990 1.010 .992 1.008
76 .995 1.005 .996 1.004
80 .998 1.002 .998 1.002
84 1.000 1.000 1.000 1.000

 

 

lMature weight a weight at 84 months of age.

 

Sources: McDaniel, B. T. and J. E. Legates. 1965.
Associations Between Body Weight Predicted from Heart-Girth
and Production. Journal of Dairy Sci. 48:947.

Matthews, 0. A. and M. H. Fohrman. 1974. Belts-
ville Growth Standards for Holstein Cattle. Technical Bul-
letin No. 1099, U.S. Dept of Agric., Washington, D.C.

47
Expected Herd Life

The average dairy heifer freshens at approximately 27
months of age and leaves the herd at 65 months, completing
5 lactations.

Disappearance of cows from the herd occur as a conse-
quence of mandatory (involuntary) or voluntary reasons.
Knowledge of removal for involuntary reasons will provide
an estimate of the potential lifetime of animals in the
herd and thus can be used as a weighting index in determin-
ing economic value.

Stewart gt gt. (1977) define involuntary removal as
that which is due to such reasons as: calving problems, di-
sease, foot and leg injury, and reproductive-problems such
ag sterility. Voluntary removal includes cows with low
milk or fat production, bad temperament, other faults of
the mammary system or general confirmation weaknesses.

Table 4 presents the probability of failure of cows
by lactation number as found by the various studies cited.
As indicated in the table the probability of removal in-
creases with lactation number in a relatively linear fash-
ion. Stewart gt gt. (1977) derived a prediction equation
based on data collected by Burnside gt gt. (1971) estimat-
ing the probability of involuntary removal as: .0575 +
.0170 (Li), where Li refers to the lactation number. Prob-

ability of death was estimated separately as: .0075 +
.0045 (Li).

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48

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49
Reasons for Removal

Many studies have dealt with the reasons for disposal
of dairy animals including: Arnold gt gt. (1958), Dayton
(1966), 0'B1eness and VanVleck (1962), White and Nichols
(1962), Renkema gt gt. (1977) and Gurtle and Smith (1970).
Table 5 compares their findings.

The most important reasons for culling among the
studies noted above were: low production, sterility and re-
productive disorders, teat and udder troubles and mastitis.

Low production is by far the largest single classifi-
cation, however this category is frequently used as a catch-
all for animals whose production has been reduced by other
conditions such as mastitis, milk fever, hard milkers as
well as nonbreeders in late lactation (Dayton 1966).

Table 6 enumerates the reasons for disposal for dif-
ferent age groups found by Dayton (1966). O'Bleness and
VanVleck (1962) and Dayton (1966) found that disposal for
sterility increases with age. White and Nichols (1962)
found only a slight increase due to sterility as age in-
creased. The following observations were drawn from these
studies:

1) Low production, sterility, udder trouble and dairy
purposes are the major reasons for disposal across
all age groups.

2) Udder and mastitis troubles become more prevalent

with increasing age.

50

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51

Table 6. Percent of Total Culls By Lactation Number.

Lactation Number

 

 

Reason 1 2 3

Dairy 18.1 10.5 6.3
Low Prod 46 47.1 32.1
Physical Injury 10.2 11.6 15.7
Hastitis 4.2 8.6 16.7
Disease 5.8 4.5 2.9
Milk Ability 5.9 2.6 2.5
Sterility 7.9 10.7 12.4
Old Age 0.2 0.1 5.7
Death 5.7 5-0 5-7

 

Source: Dayton, A. 1966. Differential Removal of Daugh-
ters Among A.I. Series. Unpublished M.S. Thesis.
Michigan State University, East Lansing.

5) Low production is the major reason for disposal
for cows under 6 years of age. After this time,
udder and mastitis troubles are the primary rea-

sons for disposal.

Longevity and High Producing Cows

Some dairymen believe that high producing heifers "burn
themselves out" early in life. Gibson (1977) examined the
records of 317,501 cows to test this hypothesis. The cows
were equally divided into 4 groups according to deviations
from herdmates during their first lactations. The average

percent removed for each class was calculated through the

52

Table 7a. PrOportion of Cows Removed from the Herd during
the Second through Sixth Lactation when Divided
into Four Groups According to First Lactation
Production.

:—
1

Percent Lost 81_The
No. Having 2nd 5rd 4th 5th 6th
Yield Class lst Record Rec. Rec. Rec. Rec. Rec.

 

 

 

 

TOp %. 87,409 16 55 5O 65 78
Third %. 85,467 11 59 57 69 80
Second %. 75,211 25 48 65 75 84
Bottom‘% 75,214 47 68 8O 87 92

 

Source: Gibson, D. 1977. Green Mountain News-
letter. Univ. of Vermont, Burlington.

Table 7b. Percent of Cows Removed from the Herd 9; Those
Surviving the Previous Lactation when Divided
into Four Groups According to First Lactation
Production.

 

 

__

Percent Lost of Those Surviving The

 

 

 

 

Yield No. Having lst 2nd 5rd 4th 5th
Class lst Record Rec. Rec. Rec. Rec. Rec.
TOp‘% 87,409 16 2O 25 5O 54
Third %. 85,467 20 2O 26 5O 56
Second %. 75,211 25 5O 5O 5O 56
Bottom %- 75,214 45 42 4O 55 38

 

Source: Meadows, C. July 1977. Cow Losses.
Dairy Notes. Michigan State Univ., East Lansing.

sixth lactation. The results presented in Tables 7a, b

demonstrate that survival is highest in the top quarter and

53

lowest in the bottom.

The majority of the culling in the first few lactations
is due to low production, therefore Meadows (1977) converted
Gibson's values to percent lost of those surviving the pre-
vious record. His calculations still show a small trend fa-
voring survival of the high group but he concluded there was
not much difference in total removals. As expected, as cows

get older, a higher proportion are removed.

Conclusions Drawn from the Literature Review

A biolOgical model employed for the prediction of milk
production and feed disappearance is only as good as the para-
meter estimates which define the system. From the review pre-
sented in the previous pages the following factors should be
further investigated to define more precisely the input-
output biological relationships of the dairy cow:

1) Prediction of dry matter intake.
2) Prediction of milk yield over time.
5) Protein requirements.

4) Protein metabolism and quantitative limits defin-
ing the use of supplemental NPN in rations.

5) The ability of cows to mobilize nutrient stores
(particularly energy) in early lactation.

6) The effect of pregnancy on persistency of milk
production.

The model presented in the following chapter can be
easily amended as new information becomes available. Expected
herdlife and other variables that will change with time can

be updated when necessary.

THE DAIRY COW MODEL

This chapter presents the specifications of the
model and the framework of the subsystems. The Fortran
computer code is presented in the appendix. Figure 7

presents a flowchart for the model.

Economic Decision Rules

One of the important objectives of the dairyman is
to maximize average net returns per unit of time. FigureG
will be used to illustrate this concept under the assump-
tion of nonstochastic relationships. The lepe of a line

from the origin to any point on the curve (e.g. line OD)

Net Returns 8

 

 

 

Time

Figure 6. Net Revenue vs Time.
54

55

gives the average net revenue per unit of time for a pro-
cess terminated at that point in time (e.g. cow culled from
the herd). The maximum net revenue (e.g. cumulative net
returns over the lifetime of a cow) occurs at point "A,"
while the maximum average net revenue occurs at point "B,"
where the steepest line from the origin is tangent to the
curve. Peint "B" therefore represents the maximum average
net revenue per unit of time. As time continues beyond
point "B," the decreasing additions to revenue begin to
pull down the average net revenue per unit of time. Thus
if all cows were of equal ability and if relationships were
nonstochastic, they would be culled at the age implied by
point "B."

Animals are not all of equal ability. Thus, a cri-
teria must be developed to compare an existing animal,
"the defender," against the potential replacement animal,
"the challenger." The rule is: "Keep if the marginal re-
turns per unit of time for the defender equals or exceeds
the average net returns per unit of time of the challenger,
otherwise replace" (Paris 1960).

The estimation of the future value of a dairy cow
is complicated by their long lifespan. For this reason fu-
ture revenues must be discounted using the Opportunity cost
of equity capital. For example, if the discount rate were
10%rper annum, the dairyman would be indifferent between
receiving 81.00 today or $1.10 a year from now. Therefore

31.10 a year from now is only worth $1.00 in terms of

56

today's dollars. Putting future returns into today's dol-
lars is referred to as estimating the "net present value
(NPV)" (Nelson gt a1” 1975). Because we are concerned with
comparing revenues relative to the present time when the
replacement decision is being made, estimated NPV's are
calculated. Average revenue per unit of time must be de-
termined once the net present value of a cow has been cal-
culated, since the choice criteria involves maximizing aver-
age net returns per unit of time. The average net revenue
per unit of time is comparable to an annuity payment (Paris,
1960). Thus it is possible to compare cows with different
expected times in the herd based upon their expected annual-
ized net returns.

The decision could involve, for example, replacing a
6-year-old mature cow producing 15,000 pounds of milk with a
heifer whose mature equivalent is 17,000 pounds. The time for
replacement occurs when the marginal revenue of the "de-
fender" is less than the annualized net revenue of the
challenger.

Future gross revenues are projected based on milk
production and the milk price subsystems. Costs are pro-
Jected based on feed disappearance, price, and other vari-
able costs such as veterinary expenses subsystems. Costs
and returns considered identical for both the "challenger"
and the "defender" are ignored. It is differences in costs
and returns that will determine which animal belongs in

the herd. Past incomes and expenses are ignored as we are

57

concerned with maximizing future income.
The dairy cow replacement problem is complicated by
the fact that the relevant relationships are stochastic.
In particular, the probability of a cow surviving from one
lactation to the next is less than 100 percent (Stewart 23 a1”
1977 ). The rule becomes: "Keep if the expected (in a
Probalistic sense) marginal returns per unit of time for
the defender equal or exceed the expected (in a probalistic
sense) average returns per unit of time of the challenger."
The net present value (NPV) and annualized net pre-
sent value (ANPV) calculations, in a probalistic environ-
ment are calculated as follows:
1. Calculate the NPV and ANPV for each potential
lifespan of the cow, 3 - 1, . . ., J.
2. Solve for the age, 3, which maximizes ANPV.
5. Calculate the expected NPV (ENPV)and expected
ANPV (EANIW) That is:
ENPV J
. E21 (NPVJ) (P3)

and
EANPV - J
Z (ANPVJ) (P3)
3-1
where P3 is the probability that the cow will
survive J lactations, P3 . l.
Salvage values must be included in NPV calculations.

However, they are ignored in EANPV calculations for making

replacement decisions since either the defender or the

58

challenger will be sold. More generally, their salvage

value differential, if any, should be included.

Introduction to the Model

This program was designed to simulate the costs
and returns for individual dairy cows through.time from the
present moment onward until the end of lactation 7.

The investigator inputs: age, calving interval, mature
equivalent milk production, percent butterfat, mature equi-
valent weight, milk and feedstuffs prices, opportunity cost
on capital and the number of lactations to consider. Model
outputs include: predicted milk yield for each lactation
and average daily milk for each month within each lacta-
tion; amounts of each feedstuff necessary to balance a diet
to meet nutrient requirements in a least cost way for each
month within each lactation; amount of weight lost or
gained for each month within each lactation; net present
value of the animal and annualized net present value of the
animal.

Revenue is calculated for each lactation. Lifetime
milk production and feed disappearance are generated and
salvage value appropriate variable costs and probabilities
of involuntary removal from the herd are accounted for.

The model provides an economic basis for making voluntary
culling decisions. Past expenses and revenues are ignored

as the model is concerned with future revenues.

"here:

Figure 7 Flow chart of

lactation number

lactation number
the beginning of
analysis

ultimate lacta-

tion number thus
far considered

maximum number
of lactations
considered

month within the
calving-interval

number of months

at
the

 

Enter

 

Age, calving
ProJected ME
PrOJected ME
Feed prices,

interval

weight

milk production, % BF

milk price, salvage price, discount
rate, M lactations to consider

 

 

 

l
[box-lsco:.'7>——¢.- ...|

 

 

Project: 1.
5.
4.

Age at calving at the beginning'of lact (1)

Body wt of cow at calving during lact (i) I
Lact # -

505 day milk of lact (i)

 

 

within the calving

interval

l‘

I

 

DO J a 1 to J a n ::>-H6 -I ’

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

I
Project: 1. Body wt during time period (3) I I
2. Ave daily milk (J) I
I . , '
.[Estimate daily nutrient requirements (3) '
lProJect potential daily DMI (J) I I
. J1 I
LBalance a daily diet (3) _*] I if
i 4.: I
Accumulate to date for all (3) within i: '
. Quantity of each feed consumed I
2. Hilk production T
5. Income and costs I
4. Other variable costs |
5. Discounted net revenue (DNR)
l

 

Calculate Cumulative Discounted Revenue to Date

- P mm
2:4

 

l

 

 

(Zpd DNR) + Salvage Value
1

 

- NPV for i - d to p |

 

_. ._ ._ -+-—-- _.,__

 

 

Accumulate to date for each (i) - (p) Z 7 DNR
E::p DNR + Salvage Value ‘ probability of occurrence

i-d

 

 

the model

 

‘L.
/ \

6O

Feed and other variable costs are estimated in the

subroutine:
9g§§

Other variable costs are based upon production level
considering Michigan farm data analyzed by Nott (1974),
adjusted to current price levels using a multiplier of
1.75 (Black 1978).

Costs are subtracted from milk income to estimate
the revenue generated during each lactation cycle. Revenue
is discounted using present value factors consistent with
the interest rate and the time period being considered.

The discounted net returns (DNR) are accumulated for
all time periods considered (i.e., if considering a cow
which is presently in her third lactation, accumulate the
expected revenue for lactation 5, then for the period con-
sidering lactations 5 + 4, then for 5 + 4 + 5, 5 + 4 + 5 + 6,
and finally for the period 5 + 4 + 5 + 6 + 7).

The accumulated discounted net returns, including
each time period are subsequently weighted by the marginal
probability of their occurrence, thus estimating the vari-
able net present value for each time period considered.

Net present value is converted to annualized net

present value using the formula below

NPV x rate (1 + rate)I/ (l + rate)I - l
where
I = lactation number

rate a interest rate.

61
Subsystem: Milk Production

The model projects average daily milk production of
a cow for each month within each lactation from the present
moment onward until the end of lactation 7. Factors that
are important to consider in projecting milk yield include:
1) genetic potential and environment, 2) age and lactation
number, 5) season of calving, 4) feed intake and ration
quality and 5) health of the animal.

Genetic potential and environment are taken into
account using DHIA (1974) mature equivalent (ME milk) fac-
tors which project the 505-day yield of the cow if she
were mature (approximately 24 mos cf age).\ They are pri-
marily used to compare the producing capacity of contempor-
ary herdmates during the present year. These factors take
into account breed, regional location in the United States
and represent the expected phenotypic character of the
animal within a given environment. Using these factors
thus implies relatively constant environmental conditions
across all lactations.

Projected mature equivalent milk production is ad-
justed by age and season of calving correction factors
enabling prediction of the total milk yield for the entire
lactation for animals of any age calving at various sea-

sons of the year.

62

DHIA (1974) age and season of calving correction
factors were fitted to reciprocal curves resulting in
equations for total milk with age as the dependent vari-
able and read as follows:

If age 5 70 months, the mature equivalent (XMEmilk)

factor for milk equals:

XMEmilk . .809 + (10.68 é age in months)“
If age > 70 and i 94 months:
XMEmilk ' '96

If 389 > 94 months:
XMEmilk ' '96 + :0016 X (age in months - 94)

Table 8. Average Season Effects on ME Milk.

Month Jan. Feb. March April May June

 

 

Factor 0 1.005 1.00625 1.0194 1.0568 1.0719
Month July Aug. Sept. Oct. Nov. Dec.

 

Factor 1.09625 1.095 1.0625 1.056 1.0194 1.0156

 

Adapted from: Norman, H. 23 a1. 1974. USDA-DHIA.
Factors for Standardizing 505-Day Lactation Records for
Age and Month of Calving. ARS-NE-40. Agricultural Re-
search Service, US Dept of Agr., p. 48.

The effect of season of calving was adjusted for
using DHIA monthly factors averaged across all age groups
for the months January through December and are listed in

Table 8.

65

Once milk yield for the total lactation is estimated,
average daily milk yield for each month within a lactation
is projected using Wood's model (1969):

Yn a anbe-

cn
where: Yn a milk yield in week n

n weeks into lactation

e . base of the natural logarithm

a, b, c the parameter estimates

Wood's equation (1969) was adapted for use in the
model employing Shultz's (1974) standard values for the
parameters "a," "b," and "c." (See subroutine:

X LACT (AGE, WEEK, SEASON, PRODYR, PRODDY).
The parameter "a" defines the level of peak production
while "b" and "c" characterize the shape of the curve for
the age and season of calving subgroups.

The parameter "a" can be estimated for any level of
production using the equation:

a' a "a" + 1n (Z/Y)

In this equation a' is the adjusted value for "a,"
where "a" is the standard value built into the model. Z
is the actual level of production (in our model, Z is the
505-day predicted MEmilk production level). Y is the accu-
mulated production level of the standard (in our model Y
equals the 505-day total for the standard cow).

The following example illustrates these calculations

for a 2-year old heifer with a mature equivalent of

64
15,200 pounds of milk and calving in January.

Step 1: Divide the mature equivalent by DHIA age and sea-
son of calving factors

53,
12,200 lbs of milk/505 days = 12,285 lbs of milk/505 days_
1. 4

Step 2: The lactation curve parameters applicable to this
heifer are within the subclass of age (-2) and
season of calving (January-February). The stan-
dard value for "a" is 5.574, "b" is (.202) and
"c" is (.00562). The adjusted value of "a" equals:

5.574 + 1n (12,258 lbs of milk/12,655 lbs of milk) a 5.544

Therefore, the lactation equation for this particu-
lar heifer in her first lactation is defined by

the equation:

b

. n

In this case:

.202 - .00562) ( )
Yb . 3.544 (n) e ( n

Estimating Lifetime Production

Production in future lactations is calculated in the

following manner:

65

1. Estimate the season of calving.

If the calving interval is 15 months, the season
of calving shifts one month with each successive
lactation (i.e. if the first calving occurs in
January the second will be in February, the third
in March, etc.). If the calving interval is 14
months, the season of calving will shift 2 months
with each successive lactation.

2. Age at second calving is equal to age at first
calving + length of the calving interval.

5. Total milk production is projected by adjusting
the ME by the reciprocal of the appropriate DHIA
age and season of calving factor.

4. The appropriate standard parameters of the lacta-
tion curve are now defined by the age and season
of calving factors.

5. An adjusted value for "a" is calculated as ex-
plained on page 65.

6. The appropriate parameters of the lactation equa-
tion are applied and average daily milk is pro-
jected for every month of lactation 2.
The process is continued for lactations 5, 4, 5, 6,
and 7 to calculate weekly production during the lactation
cycle. The process is terminated after lactation 7 because

the probability of a cow remaining in the herd beyond this
length of time is very small (<2%) (Andrus 33 31., 1970).

66
Subsystem: Dry Matter Intake

Development of the Dry Matter Intake Equation

Equations used in the model are based upon statisti-
cal analysis of the experimental data of Foldager (1978)
and the literature. Foldager's experiment was designed to
estimate protein requirements in early lactation. Weekly
intake data for 68 Holsteins during the first 20 weeks of
lactation were available. Regression methods were used to
estimate the parameters of dry matter intake per day equa-
tions including testing hypotheses about linearity of the
impacts of milk production potential and weight. Chandler
and Brown's (1976) study and the literature were used in the
selection of explanatory variables; variables include age,
season of calving, milk produced per day and weeks into
lactation.

There was no evidence to support the hypothesis
that weight at calving and milk production potential are
nonlinearly related to dry matter intake. The resultant
equation when WTCLF2 and MILK2(6-8) are excluded is pre-
sented in Table 9. Dry matter intake was influenced by the
protein system; cows receiving the 15-15% CP diet unsupple-
mented with NPN and both NPN supplemented diets had lower
intakes than the cows receiving 12-15% CP diets unsupple-
mented with NPN. While the hypothesis that intakes were
equivalent was rejected, there was no evidence that a dif-

ference existed between 2nd, 5rd and 4th treatments.

DMI

AGE:
AGE 2

AGE 5

AGE 4
TIME:

WEEK

WEEKZ

WEEK5
PRODUCTION:

MILK(6-8)i

67

Daily dry matter intake for the ith
cow during the jth week, pounds/day

if two years old
otherwise

if 5 years old
otherwise

if 4 years old
otherwise

OH OH OH

Weeks into lactation
Week squared

Week cubed

Daily milk production for the ith cow
during the 6th through 8th weeks,
pounds per day, a proxy for production
potential.

MILK2(6-8)j Milk(6-8) squared

MILKi’J

MILK21,j
WEIGHT:
WTCLF
WTCLF2
SEASON:
SEASON 1

SEASON 2

SEASON 5

Daily milk production for the ith cow
during the jth week, pounds per day.

MILKi’j squared

Weight at one week post partum

NTCLF squared

if December, January or February
otherwise

if March or April
otherwise

if May or June
otherwise

I
OH OH OH

68

I 1 if July August or Sept.
SAASON 4 a 0 otherwise ,
1 if October or November
SEASON 5 a 0 otherwise

PROTEIN TREATMENTS:
1 if cow received 12-15%

TREAT 1 a crude protein diet; no
supplemental NPN
0 otherwise
1 if cow received 15—16%
TREAT 2 = crude protein diet; no

supplemental NPN
0 otherwise

1 if cow received l5-16%
crude protein diet; urea
TREAT 5 a added at .65%iof corn si-
lage, fresh basis
0 otherwise

1 if cow received l5-l6%
crude protein diet; ammo-
TREAT 4 = nia added at .40% of corn
silage, fresh basis
0 otherwise

The initial equation estimated was:
DMIi,j a + 2 SEASON 2 + 5 SEASON 5 + 4 SEASON 4 + 5 SEASON 5

AGE 5 + AGE 4

4
TREAT 2 +

* 3
+ 2 5
+ S1 WEEK + $2 fiEEK2 + S3 WEEK5

TREAT 5 + 4 TREAT 4

0

+

l WTCLFi’J + 02 WTCLF2i,J
. n1 MILK(6-8)i,j + N2 MIL1<2(6-8)i,j
* “1.3

where ui,j is a random error. A subsequent model was formu-
lated in which the data were divided into two groups, two

year olds and cows at least three years old.

69

opeem essences .emenoas .meamnea .m .mamona .n.nm
swam Hem nmwoapaz samponmuqoz new musmaesfizvem naeuonm

.hpamse>wnb

.qowpwpown haamm cw meoo mcaoseoam
.uuma .h .Aewwoaom «mossom

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mooo. mmoo. name we a;
mooo. meow. Amnovsaas
mooo. 400.. name;
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mooo. oomo.m Mom;
moo. ommm. m 4
mooo. ammo.a m 4
H00. Scam. m m
0N0. mmnm. e m
mam. mmem.n m m
mam. ammo. m m
mooo. waeu.u e 9
N00. mmmm.n m a
mooo. mmmw.n N a
0H0. meom.a psepmqoo
He>eq enam> nowpmanomen
meadowmaswam

 

psofleflmeoo noEmeHMem

maem.m . opsaapmm so songs unseempm
050m. n NW

 

.noapepoeq no @3003 hpsmsa pesam on» meansn
hmn\exwpsH Heaps: has madcamen museaoammooo scammenmem .m magma

70

Twenty-two of the 68 cows in the experiment were first
calf heifers; previous studies have suggested the intake
curve for heifers is "flatter" than that of other cows.
Thus, cows were sorted into two groups; cows 2 years of age
and cows at least 5 years of age. Table KDdepicts the
regression equation for 2, -5, and the combined data, re-
spectively. The first step is to test the hypothesis that
first calf heifers and cows have equivalent intake equations
against the alternative that they do not. The apprOpriate
test (Madalla, G. S., 1977, p. 460) is:

 

Q5/ K
Q2/(m+n-2E)
where
Q1 3 SSE of combined data sets
02 . ssE ost-yr olds + SSE orzi-yr olds

\fiD
I

Q1 ‘ Q2

,# of regression coefficients

P1
u

(2+3) . # of observations on 2-yr olds (m) + # of
observations on 5-yr olds (g). '

The ratio is distributed as an Fd,K,m+n—2K° Here,

Q1 " 6947
Q2 - 6205
Q3 3 742
K a 12
m+n - 1560

42 12
F 'W‘ 15-32

a significance level .001. Thus, the hypothesis that the

and

intake equations are equivalent was rejected.

71

.nmenoHs .mnHmamH .m

.mHmeaa .n.nm

thmne>HsD mumpm smmHnoHs
.soHpMpomH thwm 0H msoo wnHescon

 

 

 

 

 

 

anm you nemoaqu sHeposmlsoz 0cm museaesHsaem sHeposm .550H .6 .nomdeom "meadow 0000
500. 005. 0 mm:
0000. 00.H m 00:
0000. :000. 0000. 5:00. 0000. 0500. H00. m:00. 0000. mHHo. mqoe;
0000. m50m. 0000. 000w. 0000. :::m. 0000. :00H. 0000. 000H. A0I0VHHH2
0000. 5:00. 0000. 5:00. 02
0000. H00H.I 0000. HO0H.t N2
0000. 0000.m 0000. 0000.m a
0000. 5:00. 0000. 0000. 02
0000. H00H.I 0000. 00Hm.: m;
0000. 000m.m 0000. 0NHO.m a
0000. 0:00. 0000.0 m000.+ 02
mooo. mmaa.- mooo.o same.. me
0000. 0000.H 0000.0 000:.H 2
H00. 50H0. 0000. H050. 0000. 0:H5. H00. 0:05. mHo. 0005. 0m
0m0. mm5:. 0H0. 0:H0. :H0. 0000. 000. 5:05. H00. 0000. :0
m0m. 0m:m.u :H:. 000H.| 0m:. 050H.| 0000. 0050.H| 0000.0 050H.H mm
mHm. mmoo. mam. omeo. 006. 000:. mom. mmmo.u coo.H 0000. mm
0600. mH:5.- mooo. meH5.- mooo. :H50.- 0006. mamm.u 00:. Hmaa. as
moo. :000.| H00. :000.1 000. 000:.1 H00. 0m00.| 0H0. 00Hm. 09
0000. 0000.: 0000. ::m5.: 0000. 0H05.| 0000. 50:0.: 0:0. 5:H0. me
0H0. 0:00.H 0000. mm:.m mmH. 0:00. H00. 0:00.0 05H. N:0m.H pneumooo

.mHm HHe .mHm Ha: .mam HH< .mam m .mHm m new:

oamm. . we once. . mm amen. . mm mmae. . mm same. . mm
mHmm.m HH5m.m mm5:.m
epwsHpmm esp we momma unwuswpm
soprpemH hHuem QH 004
an emanaH mnHNHnepemnmno mpaeHonmeoo sonmeHMmmn .QHeHnea

72

There are several differences in the equations.
Protein treatment system had little impact on the DMI of
2-year heifers, as contrasted to a substantial impact on
cows at least 5 years old. The seasonal impacts are simi-
lar except for the May-June period when the impacts were
in the Opposite direction. The most important difference
is that the DMI curve is much flatter for the two-year olds
than for the older cows. DMI of the older cows was more
sensitive to production potential when it was relatively
more sensitive to weight for the 2-year olds.

The analysis indicated that protein treatments 2, 5,
and 4 be combined and that seasons 1 and 2 be combined as
well as seasons 4 and 5. This reduced the total number of
variables to 8. However, separate equations should be con-
sidered for two-year olds vs. older cows.

Use of the proxy variable for genetic potential
(MILK(6-8)j) resulted in a lower R2 than use of average
daily production during the week (MILKi), .588 vs. .6519.
These regressions are presented in Table 11. These results
must be interpreted with caution, however, since use of
MILK];j will result in biased estimators if MILKij and DMIij
are simultaneously determined (Dean, et al., 1972). Single
equation estimators sometimes result in better forcasts than
those derived from systems of equations, even when simul-
taneity is present (Madalla, 1977). IHowever, biased estima-

tors are inappropriate for managerial decision making.

75

Table 11. Milk (Average Daily) vs Milk (Weeks 6-8).
1560 Cases.

 

 

 

 

 

Milk Milk
(Average 53115) (Weeks 6-8)

§2 .6519 .588
Standard Error

of the Estimate 2.09 2.27
Constant 1.401 .785
T 2 - .545 - .615
S 5 .190 - .264
S 4 .776 .511
Milk .508 0
Milk (6-8) 0 .245
Weeks 1.161 2.091
(Weeks)2 - .091 - .185
(weeks)5 .002 .0047

Wt at Calf .007 .007

 

74

The regressions described above pertain to data col-
lected over the first 20 weeks of lactation. Extrapolating
the regressions beyond that point in time resulted in absurd

estimates.

Impact of Fermented Feeds

The equations presented in Table 12 illustrate the
impact of fermented feeds on dry matter intake per cwt. of
body weight based on an analysis of monthly treatment mean
data from Brown et a1. (1965) involving 40 cows and 4 dif-
ferent roughage rations. During the experiment corn silage
was fed ad libitum to groups 1, 2 and 5. Average quality
alfalfa was fed at the rate of 0, 10, 20 and ad lib pounds
to groups 1, 2, 5 and 4 respectively.

The regressions involved 2 independent variables:
months into lactation and moisture percent of the ration
(which is used as a proxy variable indicating the degree of
fermentation). The R2 value of the analysis was .95. A
second equation was run containing moisture, the square root
of moisture as well as months. Including non-linear rela-
tionships did not improve the analysis. A second series
of regressions were run excluding the first 10 weeks into
lactation, thus removing the confounding effects of early
lactation on intake. This analysis involved 52 cases of

mean data and is reported in Table 15. The results are

similar to those presented in Tablel2; however there is

75

.mne .0Hmum: .moamnom

hnHwn Ho Hmsnsoh .msoo hnHmn msHuwpowH on nHmso

mo mHe>0H anm 09H; hem new ommHHm snoo Ho mHe>eH mac
:Hnm> wsteem mo panama .000H .H0 90 ..n .H .ceonm “eonsom

 

.wpmw sees no memes 00 no deems one mQOHmmeHmom

.AsoHpes on» He ensanoa 5v nmfienspmHos
0 soHumH on» no onzpmHoa.R n ensumHos

 

 

 

 

soHpmpeeH :H causes a apnea «when;

50H. 050H. 0.esspmHos

000. 0000.: 0000. 05H0.: enzpmHos

0000. 00H0.: 0000. 55H0.: Anne:

0000. 0Hm.m 0000. H050.0 pampmcoo

H0>0H Ho>eH meHanHm>
cosmoHHquHm eeqmonHanm

saw. . mm Ham. . mm

 

 

eaHa 0cm manpmHoE on qupwHom
QH eHMqu Heaps: 5H9 msHaHHen mpsmHonmeoo nonmesmmm .NHmHnwa

76

.eeeeHom thwQ .0

.m90 .0Hmum:

.msoo AHHmn msHpmuowH op sHmso Ho mHe>eHIAWH0 thB 500 one

 

 

 

 

 

 

 

 

ommHHm anoo mo mHe>mH monam> wsteem Ho pommmm .000H .Hm pm ..n .H .03090 “009300 wumn

0000. :000.1 .mHoE x Mme;

#00. mQOO.I MOOO. HONO.I mOOO. ©NHO.I MOOO. HmHO.I mOOO. BBHO.I mHSpmHoE

GOO. NMBOO.I mOOO. OMNO.I Néo. MONO.I mOOO. :mHO.I MOOO. BBHO.I x003

mOOO. #Bmm.m MOOO. Nmum.m mOOO. mm®m.m MOOO. MO¢B.M mOOO. Hmbw.m pawnmfioo

.mam .wam .mHm .mHm .mHm

5000. Nmmo. mmOH. mwmo. BNmO. mpmaflumm 02¢ Ho

Henna chewswpm

0H00. 0500. :055. N500. :H00. mm

mm ON NH mm @m 00000
soprpomH och 0x00; om MHIOH 0x002 quumpowH opsH mama HH:

meme; 0H
x002 0cm .mHo§
Ho soHpomheusH

mama; oH

 

 

0cm QOHpmpomH ousH mxeea mo mosmnHmsH map mGHNHHeuomnmno mpceHonmeoo aonmmnwom

.pso\Han so QOHpmm esp mo usoonem enspwHoE

.MHQHQMB

77

less evidence for a non-linear relationship.

The hypothesis that the impact of fermented feeds
might be different at various stages of lactation was ex-
amined. The data were divided into 2 periods: WEEKSlo_19
and WEEKS 20. The following hypothesis was tested:

Ho ‘ B10.19 ’ B 20
VS

HA : Ho false

where: B10-19 . regression coefficients characterizing
intake during weeks 10-19 of lactation.

B 20 . regression coefficient characterizing intake
during weeks 20 of lactation.

The apprOpriate F statistic is

FaQ/k
Q27M + N - 2k)

where: F . F statistic

 

Q1 . SSE of the regression characterizing intake for
weeks 10 into lactation.

02 . SSE of the regression characterizing intake for
weeks 10-19 + SSE of the regression characterizing intake for
weeks 20.

03-41-03

k . # variables considered in the regression.
(M + N) a # cases for weeks 10-19 (M) + # cases for weeks
20 (N).

78

SSE(<10 WEEKS) s .228818

SSE(lO-19 WEEKS) . .096018

SSE(22O WEEKS) - .026088

k a 2

M812

N a 20

F - 12.2556

F critical .05 (2,28) - 4.22
.01 (2,28) . 6.44

F exceeds F critical.

Therefore, we can reject Ho (prus .000005); there is
not enough evidence contained in Brown's data to show that
the regression coefficients are the same for weeks 10-19
and weeks 20 into lactation when characterizing intake by
weeks into lactation and moisture content of the diet.

The evidence does indeed suggest that the impact of
fermented feeds affects dry matter intake and changes with
time during the lactation.

Impact of Energy Density and Crude Fiber Content of the Ration

The impacts of energy density and crude fiber content
of the ration on dry matter intake per cwt. of body weight
were analyzed using period mean data of Lamb 23 gl. (1975).
The regression coefficients are presented in Table 14. Re-
gressions including either energy or crude fiber as vari-

ables provide similar R2

'3 indicating they are good substi-
tutes for one another and are highly related. The best fit
regressions included as variables net energy (NEL), (NEL)2

and crude fiber demonstrating a curvilinear response of in-

take as energy density increases.

9
7

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cevzHoX0 0H0 soHpmpowH Ho

.HHOu50

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seams 0H passe one

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om no 00000 0H0 onH000a00m

 

 

 

 

 

 

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mm
H030\H200 sssusH assess sun so Asa 000 sHHs
0am nenHm edsno .mwnenm manHmon mpquoHHmeoo nonmenwem. .:H0Hnme

80
Subroutines: DMILAC and DMIDRY

The subroutine:

DMILAC (AGE, WEEK, PROD, PRODDY, PRODLG, NEIGHT, DMI, DMILAG)
is used to estimate dry matter intake over the lactation.
Cows are differentiated on the basis of age (i.e. animals -
2 yrs of age at freshening and those 2 3 yrs old at freshen-
ing).

The intake equation estimated from the analysis of
Foldager's data (1977) was used to characterize the intake
response curve in early lactation (weeks i 10). An upward
adjustment of .5 was added to the constant providing a more
reasonable estimate of intake.

It is assumed that peak intake has occurred by week
10 into lactation. Intake for weeks > 9 is projected based
on intake during the previous month (DMILAG) and adjusted
downward to account for decreasing intake as the level of
milk production declines (PRODLG - PRODDY). PRODLG refers
to average daily milk produced during the previous period
and PRODDY refers to daily milk production per day during
the present period.

A value of .2 is used as the coefficient character-
izing the influence of the change in milk production on dry
matter intake. This value was derived from analysis of
Lamb's data (1973) presented in Table 14.

Hillman's analysis of Slack's data (1973) yields a

coefficient of .0187 x milk when expressing intake as a

81

percent of bodyweight. Assuming an average bodyweight of
1550 lbs the coefficient becomes:

.0187 x 15.5 a .25245 x (milk, lbs)
The coefficient found by analyzing Foldager's (1977) data

was 3

IA

.2578 x (milk, lbs) for cows 2 yrs old

.2740 x (milk, lbs) for cows 3 5 yrs old
When estimating lbs of dry matter intake, Broster (1978)
found the coefficient of:

.158 x milk (lbs)
The average of the above coefficients is .228. The value
used in the model, .2 x milk (lbs), compares favorably with
the average value of the coefficients.

Intake during the dry period is estimated in the
subroutine:

DMIDRY (AGE, NEEK, PROD, PRODDY, PRODLG, WEIGHT,

DMI, DMILAG)

Maximum intake is defined as being equal to intake
one period (month) prior to the dry period.

Table Eidefines how maximum intake is estimated in

the model.

82

Tablels. Estimated Maximum Dry Matter Intake

 

 

. For Weeks 1 5 9 into Lactation

 

IV

DMI, (lbs/day) . 2.50 + 1.03 x (Age 3 2) + .74 x (Age 5)
+ .0064649 x (Weight, lbs § 2.2) + .20719
x (Production, lbs of milk per day)
+ 2.0906 x (Week, into lactation)

2

- .155159 x Week + .0046654 x Week3

x 2.2

 

For Weeks 10 - 40

 

DMI, (lbs/day) a Dry matter intake during the previous
month (DMILAG) - .20 x Change in milk
production, (PRODLG - PRODDY)

 

For The Dry Period

 

DMI, (lbs/day) s DMILAG

 

83

Characteristics of the Diet

The effects on dry matter intake of characteristics
of the diet such as fermented feeds and energy density are
calculated in subsystem:

REQLAC (AGE, NEEK, PRODDY, HEIGHT, GRCMTH, NTCHG,

ITPCHG, DMI, EN, GP, GA, PHOS, SALT, XNPN, CAPHLB,

CAPHUB, FMIMP, ENIMP, CF)

DMI is a function of age, week into lactation, milk produc-
tion potential, energy density of the diet, and an index of
fermintation. The equation used is

-- -- ? NEEK -.007 MOISTURE V -.0059 353K-

DMI a MOISTURE % if w’EEK 4o.
.‘JTCLF

 

-- -- ? WEEK -.025 if WEEK 40.
The impact of the fermentation interaction term is equiva-
lent to the most severe declension on intake due to fer-
mented feeds noted by Hillman gt a1. (1975). The inter-

action term may explain differences in the slope grid: found

 

by Hillman gt 5;. (1975) ranging from -.017 to -.025. In
the model the impact of fermented feeds is designated as
FMIMP.

The relationship of DMI and energy density was de-
rived from Lamb's data: It is estimated that

This compares with Hillman, gt l.'s estimate of 1.76 when

estimated net energy (ENE) was used.

84

The impact of energy density is designated as ENIMP
in the program. Energy density does not begin to exert a
negative effect on intake until energy density exceeds

.72 Mcal NEL/lb of dry matter (see Baumgardt 1970).

85
Subsystem: Requirements

Maintenance requirements are estimated as a function

.75

of metabolic size, wtk8 , while production requirements
are a linear function of milk production and the percent fat
contained in the milk. Lactation requirements are described
in the subroutine:

REQLAC (AGE, flEEK, PRODDY, HEIGHT, EN, CP, XNPN,

CA, PHOS, XCAPH, SALT)
while dry cow requirements are described by the subroutine:

REQDRY (AGE, WEEK, WEIGHT, EN, CP, XNPN, CA,

PHOS, XCAPH, SALT).

Requirements for growth are based on gain per day.

Protein and Energy

The protein subsystem is relatively crude. Since
there is little consensus upon the appropriate conceptual
framework, NRC requirements were used in conjunction with
an upperbound on supplemental NPN use and NPN as a percent
of the total protein. There is controversy concerning
protein requirements and utilization of supplementary NPN
as a source of protein for high producing cows in early
lactation. As there is no evidence demonstrating beneficial
effects of supplemental NPN in rations of cows whose require-
ments exceed 14% CP in the ration, supplemental NPN is not
permitted as a source of protein if requirements exceed

this level. Supplemental NPN was restricted to a maximum

86

of 50 percent of the total crude protein requirement.

It is not clearly established that energy require-
ments per pound of milk produced decreases as a cow's gene-
tic potential increases as proposed by Smith (1975) and
Blaxter (1962); therefore constant partial efficiencies
are included in the model.

High producing cows in early lactation have the
ability to mobilize significant quantities of body stores
thereby helping to meet energy demands when dry matter in-
take levels are low. This ability appears to be related
to the level of milk produced (Poos g3 g1. 1978, Flatt 22
gl. 1967). Calculations in tablel6 suggest this relation-
ship may be approximated by expressing mobilization capacity
as a function of the level of milk produced. Amounts of
energy available through fat mobilization are not precisely
defined; however, based on experiments of Flatt and Moe
(Flatt gt 3;. 1967, Flatt 1966, Moe 1971) as well as general
observations of amounts of body weight lost during early
lactation (Trimberger 23 gl. 1972), the following assump-
tions are incorporated in the analysis:

1. Body fat can be used as a source of energy during
the first 8 weeks of lactation with up to 50% of
daily energy requirements being met during the
first 4 weeks of lactation. During weeks 4-8,
15% of requirements can come from body stores.
The body fat used in early lactation must be re-

plenished during mid and late lactation.

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88

2. Fat is assumed to contain 9.0 Mcal of energy per
kilogram. Conversion from fat to milk is con-
sidered to be .86 efficient (Moe, P. and W. Flatt
1969); therefore it is assumed to contain 7.56
Mcal per kg or 3.4 Mcal per pound.

3. Moe (1971) states that efficiency of gain in mid
and late lactation is the same as for milk produc-
tion; therefore Mcal required to replenish fat
stores can be estimated using NE. Replenishment
of body stores is assumed to occur during weeks
12 to 40 of lactation. By week 12 animals should
have reached their maximum dry matter intake, and
milk production is beginning to decline. After
week 40 the animal is approaching the dry period
and fattening in the dry period is not recommended.
Fat will be replaced evenly during this period by
averaging the amount of weight lost over this 196-
day period.

Energy requirements can be stated as follows:

Period 1: let 4 weeks of lactation

NEf - «Bu? + 9 Milk - 2r Body Weight Loss
Period 2: Weeks 4 - 8

NEE - «BW'75 + 9 Milk - 3' Body Weight Loss
Period 3: Weeks 8 - 12

NEf - «Bw'75 + 9 Milk
Period 4: Weeks 12 - 4O

89

NE? -¢<BW'75 + (E.Mcall$gbilized2

Period 5: Week 40 to Dry
NE? .«Bw‘75 + 9 Milk
where:
NEr is in Meal per day;
BW is in kilograms;
Milk is in kilograms per day; and
<x ,s ,t‘ are parameters, all 0.
Table 17 shows how the model accounts for changes in body

weight.

Calcium and Phosphorus

The Ca/Phos ratio during lactation must be less than
2.5 but greater than two to reduce the incidence of milk
fever (Hillman gt 3;. 1975). During the dry period, Jorgen-
sen (1978) recommends that calcium intake not exceed 100
grams per day. Also, the Ca/Phos ratio is set.$ 1.5 to in-
sure that excess phosphorus is not incorporated in the

diet.

Crude fiber
The level of crude fiber in the ration is set 2 16%

DMI in order to assure adequate rumen function.

Requirements for_growth

Requirements for growth are not clearly established
for a heifer once she enters the milking herd although cows
do not reach maturity until an age of approximately 7 years

(Foldager gt El° 1972). NRC recommends increasing the

9O

 

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5.. 0.0ap.

 

91

maintenance allowance for all nutrients by 20% during the
first lactation and 10%>during the second to allow for
growth.

Requirements in this study are based upon the animal's
growth rate which is estimated in the subroutine:

WT (MNTAGE, WTME, WEIGHT, GROflTH).

Values of 2.32 Mcal NE and .50 lbs. protein were used
per pound of gain for growing lactating cattle based on
1978 NRC.

Tables 18 and 19 show how the model accounts for

nutrient requirements.

92

TABLE 18. NUTRIENT REQUI.mm T SPECIFICATIONS FOR LACTATING

 

 

cows.
Daily
DMI 2 xj _<. DMIma"
ENERGY ZEnjaxJ _>. Enr Milk + Enr Maintenance

+ Enr Growth .t Rnr A Body Weight

 

CRUDE PROTEIN XCP 5‘ 2 CPr Milk + oPr Maintenance

3‘3
+ CPr Growth

 

 

CALCIUM a. ZCaJax. >. Car Milk + Car Maintenance
PHOSPHORUS g I. I.
:‘.2I?.hos'j xj Z Phos Milk + Phos Maintenance
a
2.5 2 Ca; ‘3 a 2.0
Phos.ax
J J
SALT :ZSalthJ a Saltr Milk + Saltr Maintenance

 

SUPPLEMENTAL ZXNPNJaxJ. . o if 0P1" > .14 z x.

 

 

 

NON-PROTEIN a J
NITROGEN ZXNPNJ. x3 5 .5 CPr

CRUDE FIBER ZijaxJ 2 .16 xJ.

where:

The superscript a indicates the amount of that
variable in feed xj;

x. indicates the amount of individual feedstuffs
cansidered dry matter basis; and

superscript r indicates the amount of that variable
that is required for the function stated.

 

95

TABLE 19. NUTRIENT REQUI anKENT SPECIFICATICNS FCR DRY COJS

Dry Cow Requirements

 

 

 

 

DRY MATTER x. S DMI of ultimate week of lactation
INTAKE J

ENERGY Enda 2 Enr Maintenance and Pregnancy

CRUDE CPJa Z CPr Maintenance and Pregnancy
PROTEIN

CALCIUM & Cada 2 Car Maintenance and Pregnancy
PROSPHORUS

PhosJ.a Z Phosr Maintenance and Pregnancy

CaJ.a 4 100 g.

 

 

 

Ca.a
___J__
Phos.a 4 1’5
J
SALT ZZSaltja a Saltr Maintenance and Pregnancy

 

Where:

The superscript a above a variable indicates the
amount of that variable in feed x3;

x. indicates the amount of individual feedstuffs
c nsidered on a dry matter basis; and

superscript r indicates the amount of that variable
that is required for the function stated.

94
§ubsystemz Balancing the Ration

Using a linear programming (LP) subsystem titled:

BAL (DMI, EN, CP, XNPN, CA, PHCS, XCAPH, SALT,

F, P, IOPTI)
a ration is balanced to meet the daily nutrient requirements
of dairy cattle taking into account the nutrient composition
of feedstuffs, feed prices and dry matter intake, and weight
change. The source of the nutrient composition of feed-
stuffs was Teleplan 31, a computer dairy ration balancing
program, developed by Harsh, Hillman and Black (1975).
Table 20 illustrates the matrix layout of the ration balanc-

ing subsystem and the restrictions that are incorporated.

95

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RESULTS AND DISCUSSION

The results of the simulation model are presented
including daily feed requirements for cows with varying
productive capacity, per lactation budgets, and ENPV and
AENPV. Feed prices per lb of dry matter were: shelled
corn, 8.042; corn silage, 8.023; alfalfa hay, 8.050; soy-
bean meal 44, 8.090; urea, 3.080; dicalcium phosphate,
81.25; limestone, 8.035; and salt, 8.04. Output prices
included the cow's salvage value at 8.40/lb and 3.5%-milk
at 89.25/cwt.

Before showing expected value results generated by
the model, examples of the rations generated throughout
lactation as well as across the lifespan of cows with vary-
ing productive capacity will be presented.

Table 21 presents performance characteristics of cows
with various production abilities at 3 different ages. Look-
ing across lactation feed summaries presented in Table 22, it
was found that as production level increases more corn,
soybean meal and alfalfa hay are incorporated in the ration
while less corn silage and urea are included. The reason
less urea is utilized is due to the restriction that urea
cannot be included in rations when crude protein exceeds
Lm% of the dry matter. The concentrate to forage ratio also

increases with production level, ranging from .29 to .49.

97

98

Beyond a level of 22,000 lbs of milk the computer
will not balance for rations during months 2 to 3 into lac-
tation. As there are herd averages above this level it is
apparent that either parameters estimating intake are incor—
rect or energy requirements are not constant for cows of
different production levels.

The feed summary of the cow producing 12,500 lbs
milk includes 69 lb urea.

69 lb urea x 2.81 lb protein/lb urea a 194 lb protein

194 lb protein/.44 lb protein/lb soybean meal a

441 lb of soybean meal that were spared by including

urea in the ration.

69 lb urea x $.08/lb = $5.52 for urea
441 lb soybean meal x 3.09/1b 2 839.70 for soybean

meal

This amounts to a savings of approximately $34.00/
cow/yr. Even at the highest level of production 32 lb of urea
were utilized. This amount of urea could substitute for
over 200 lb of soybean meal amounting to a savings of ap-
proximately 815.80/cow/yr. Thus, contrary to pOpular be-
lief, urea serves as a source of protein for cows exhibiting
high levels of production. (A note of caution is needed
here in interpreting these results; actual farm situations
shoukinot permit balancing diets for each month within each

lactation.)

99

Table 21. Performance Characteristics of Cows of Various
ME Milk Production Levels at 3 Different Ages.

 

 

Age Week 7 Milk Yieldl (lbs) Projected Milk Yield2 (lbs)

 

ME . 15,000 lbs

52.9 11,962
4 66.0 14,452
70.9 15,625
ME = 17,500 lbs
61.7 13.955
4 77.0 16,966
82.7 18,229
ME - 20,000 lbs
70.6 15,949
4 88.0 19,389
94.5 20,835
ME = 22,5005 lbs
79-4 17,945
4 98.9 21,813
6 106.3 25,458

 

lWeek 7 milk yield is an indication of peak yield.

2This assumes a 12-month calving interval (production
for 305 days).

3The model cannot solve beyond a level of 22,500 lbs,
suggesting that the amount of energy required per pound of
milk might not be constant as proposed by WRC.

lOC>

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101

Looking within lactations presented in Tables 23, 24,
25,;K5 amounts of corn and hay in the diet increase until
mid lactation and then begin to decline while corn silage
exhibits an opposite trend. Amounts of soybean meal
steadily decline as lactation progresses until about mid-
lactation subsequently being substituted for by urea. This
suggests that cows grouped by stage of lactation could
utilize urea in diet formulation for cows producing at high
levels of milk in late lactation. Cows producing at 17,500
and 19,400 lb are nearly able to meet calcium and phosphorous
requirements during mid lactation with little mineral supple-
mentation. The largest amount of weight was lost by the
cow producing at 19,400 lbs of milk. This amounted to an
average daily loss of -2.33 lb for the first 4 week period
and -.95 lb for weeks 4-8.

~2.33 lb x 28 days a 65.24 1b

-.95 lb x 28 days a 26:6 lb

91.84 lb

Thus 91.8 lb were lost during the first 8 weeks of

lactation. This amount of weight loss lies within range of

that expected (See Trimberger 32 a1. 1972).

102

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105

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104

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106

Once feed costs and income from milk are determined
the program proceeds to generate estimated variable net
present value (ENPV). Income is then weighted by the prob-
ability of survival. Expected income is converted to an
annuity amount (average yearly value over the lifespan of
the animal).

The first question to address when considering the
replacement question is that of determining the optimum
time of keeping a cow in the herd assuming she has no chal-
lenger to contend with.

Figures presents the expected variable annualized net
returns of a heifer whose ME is 15,000 lbs of 3.5%imilk,
starting with lactation l and continuing through lactation
7, taking into account probability of expected life at each
stage.

Net returns increase with age up until lactation 5
at which time they begin leveling off. By lactation 7 aver—
age net returns are actually beginning to decrease due to

decreasing milk production as age progresses past maturity.

//.
f

_: The optimum length of time to keep an animal in the
herd ranges between 5 and 7 lactations. The differences in
income at these ages is not significant.

The replacement question can be addressed from 2 dif-
ferent standpoints depending upon where we are situated in
regard to the production curve. If we are considering a cow
at an age where annualized net returns are decreasing with

age then the proper comparison is that of marginal net

107

returns of the current asset (over the next production
period) with the maximum annualized net returns of the re-
placement candidate. I

If we are considering a cow at an age where annualized
net returns are increasing with age, we should compare maxi-
mum annual returns of the existing cow with maximum annual
returns of a replacement candidate.

Suppose we have a 6-yr old cow whose ME(Milk) is
15,000 lbs, should she be kept or replaced? Figure illus-
trates that the cow is at an age where she has maximized
average net returns. Therefore, it is proper to compare
her marginal net returns for the next lactation cycle with
the maximum average net returns of the replacement candi—
date. According to the analysis, at age 6 (lactation 5)
annual variable net returns are approximately 5930. There-
fore any heifer whose variable annualized net income ex-

ceeds 8930 should replace the defender.

Table 27. Projected Annualized Variable Net
Present Value of Replacement Heifers.

 

 

Production level (MEMilk) Annualized Variable Net Earnings

 

12,500 3643
15,000 890
17,500 950

20,000 1066

 

108

 

1 100 4 .
’ MBMilk = 17,500 lbs
1,000 m
MEMilk = 15,000 lbs
900 +
800 w

 

‘

MEMilk . 12,500 lbs
700 1

600 »

 

 

H
N

3 4 5 6 7

Lactation NUmber

Figure 8. Expected Annualized Variable Net Present
Value.

109

From Table 27 it appears that any heifer whose
MEMilk is 2 17,500 lbs would replace the defender. To more
accurately access whether or not a cow should be replaced,
differences in salvage value should be accounted for.

This is a first endeavor in modelling the dairy cow
and it is realized that many parameters need to be defined
more precisely especially in regard to projecting milk pro-
duction and feed intake, estimating protein and energy re-
quirements across cows with differing genetic potential.

Important factors that the model fails to account for
include economic losses due to mastitis as well as the prob-
ability of occurrence of mastitis with age. Although
DHIA mature equivalents are used to predict lifetime milk
production their reliability for use for this purpose is
questionable. The further into lactation when the MB is
estimated the more reliable it will be. Genetic merit of
dams or calves in improving herd average production through

time is not accounted for.

SUMMARY AND CONCLUSIONS

The objective of this research was to initiate de-
velopment of a model to simulate the biological parameters
defining any particular dairy cow, and subsequently apply-
ing apprOpriate economic evaluation to ascertain the rela-
tive worth of any animal in relation to possible replace-
ment candidates.

The biolOgical model:

1. Projects animal growth until the age of 7 years,

at which time the cow is considered fully mature.

2. Estimates average daily milk yield for each month

within each lactation until the end of lactation 7.
3. Estimates daily nutrient requirements based on

body size, projected milk yield, % fat in the

milk and growth.

4. Projects potential daily dry matter intake con-

sidering: age, body weight and milk yield.

The model balances the diet for each month within
each lactation considering nutrient requirements and re-
strictions placed on nutrient parameters, feed ingredients
and their composition, and estimated dry matter intake which
is adjusted to account for the effects of fermentation and

energy density of feed ingredients on dry matter incake.

110

111

Because of the impossibility of meeting energy needs of
high-producing cows in early lactation, body fat is con-
sidered as a source of energy in early lactation which is
subsequently replenished in later lactation.

Assembly of the biological model revealed several
areas of research which require further investigation to
increase the accuracy of the model and make it more dynamic
including:

1. Defining energy requirements across cows of dif-

fering production levels across lactations.

2. Defining more accurately the relationship of the
energy status of the animal and weight loss in
early lactation.

3. Quantitatively defining how nutrient deficiencies
affect the lactation curve.

4. Accurately defining protein requirements and quan-
titatively accounting for protein and amino acid
metabolism to incorporate maximum use of non-
protein nitrogen as a less expensive source of
protein.

5. Estimating dry matter intake and the relationships
of energy density and the impact of fermented
feeds upon intake.

6. Quantifying the interactions of differing combina-
tions of feed ingredient on the nutrient value

of feedstuffs.

10.

112

Quantifying how the level of intake affects nu-
trient value of feedstuffs.

Predicting the shape of the lactation curve more
accurately and estimating milk production with a
greater degree of reliability.

Estimating the probability of mastitis and the
incidence of mastitis with age.

Estimating the effect of days open on the lacta-

tion curve.

The economic analysis:

1.

3.

Calculates the cumulative discounted variable net
revenue (DNR) for all time periods under consider-
ation taking into account income from milk, feed
cost and variable veterinary expenses.

Weights the DNR for each time period by the prob-
ability of occurrence.

Converts weighted income to an annuity amount for

comparisons of animals.

Factors in the economic analysis which require further

investigation include:

1.

Defining the appropriate way of handling salvage
value in the analysis.

Estimating the market value of calves depending
upon performance of sire progeny and the dam.
Defining more accurately the probabilities associ-

ated with herd life.

115

Statistically, methods should be developed to:
l. Integrate information from various studies into
a complete biological model.

2. Explore models that are based upon a time series

of cross sections, e.g. cows over time.

Modelling the dairy cow has tremendous potential in
teaching, research and extension and should be developed at
these levels consistent with the audience which it addresses
(i.e., simpler models illustrating basic principles for
teaching purposes; sophisticaled biological integrated
systems for research and an integrated economic analysis for
extension). The model to date has many weaknesses; however
it logically organizes current knowledge into a complete sys-
tem which when integrated illustrates what information is
still lacking and where inconsistencies in information exists
and where further research is necessary.

This is a first endeavor in modelling the dairy cow
and it is realized that many parameters need to be defined
more precisely especially in regard to projecting milk pro-
duction and feed intake, estimating protein and energy re-

wuirements across cows with differing genetic potential.

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O'Bleness, G. V. and L. D. Van Vlick. 1962. Reasons for

Disposal of Dairy Cows from New York Herds. J. Dairy
Sci. 48:1087.

Orskov, E. R. 1976. Unpublished data. Rowett Research
Institute.

Poos, M., L. S. Bull and R. W. Hawkins. 1978. Feeding NPN
to Dairy Cows: II. Evaluation of the Urea Fermenta-
tion Potential System Using a Negative UFP Diet. Sub-
mitted for publication to the British Journal of Nutr.

Redman, J. C., and L. P. H. K00. 1969. Replacement of
Dairy Cows, A Multi-Stage Decision Making Problem.
Research Report #1; University of Kentucky Agr.
Experiment Station, Lexington.

Reid, J. T., H. F. Tyrell, and P. W. Moe. 1965. Relation-
ship of Depressed Digestibility Associated with In-
creasing Feed Intakes to Energy Requirements of Milk
Production. Proc. of the Cornell Nutr. Conference,
Ithaca, New York, p. 56.

Renkema, J. and J. Stelwagen. 1977. De gebriuksduur van
melkvee en zyn economishe Istekenis. Publikatie
#1, Afdeling Agravische Econome Vakgroep Zootechniels
der Diergeneeskurde Utrecht.

121

Ridler, B., and W. H. Broster. 1969-1970. The Course of
Level of Milk Yield of the Cow Over the Lactation.
National Institute for Research in Dairying. Uni-
versity of Reading, England, p. 53.

Ripley, R. L. 1970. Effects of Days Open on Lactation
Production. Paper presented at the 65th Annual
Meeting of the American Dairy Science Association.
University of Florida, Gainesville.

Robb, G. W., R. W. Mellenberger and M. Zagsnczyk. 1979.
Mastitis Control Program, Lesson I, A Close Look at
Mastitis Itself. Dairy Notes, Michigan State Univ.
000perative Extension Service, East Lansing, Michi-
gan. May.

Rogers, 0., P. M. P. Leung. 1973. The Influence of Amino
Acids in the Neuroregulation of Feed Intake. Federa-
tion Proceedings, 32:1709.

Rundell, R. W. 1967. Optimum Dairy Cow Replacement Poli-
cies to Maximize Income Over Feed Cost. Ph.D.
Thesis. East Lansing, Michigan State Univ.

Sanders, H. G. 1923. The Shape of the Lactation Curve.
J. of Agr. Sci., 13:169.

Sanders, H. G. 1930. The Analysis of the Lactation Curve
into Maximum Yield and Persistency. Journal of Agr.
Sci., 20:145.

Satter, L. D., and R. E. Roeffler. 1975. Nitrogen Re-
quirement and Utilization in Dairy Cattle. J. Dairy
Sci., 58:129.

Satter, L. D. 1978a. Protein Degredation, Synthesis and
Flow Rate from the Rumen. 39th Minnesota Nutrition
Conference Proceedings assembled and edited by John
Donker and Gerald Wagner.

Satter, L. D. 1978b. Protein Requirements of Dairy Cattle.
39th Minnesota Nutrition Conference Proceedings
assembled and edited by John Donker and Gerald

Wagner, p . 45 .

Shultz, A. A. 1974. Factors Affecting the Shape of the
Lactation Curve. Master's Thesis. University of
Wisconsin, Madison.

Sikka, L. C. 1950. A Study of Lactations as Affected by
Heredity and Environment. J. Dairy Resources.
7:231.

122

Slack, S., W. K. Kennedy, K. L. Turk, J. T. Reid and G. W.
Trimberger. 1960. Effect of Curing Methods and
Stage of Maturity Upon Feeding Value of Roughages,
Part 2, Different Levels of Grain. Bulletin #957.
Cornell Univ., Agric. Exp. Sta., Ithaca, New York.

Smith, B. J. 1971. Dairy Cow Replacement Problem--An
Application of Dynamic PrOgramming. Technical Bul-
letin #745. Univ. of Florida Agric. Exp. Station,
Gainesville, Florida.

Smith, B. J. 1972., Factors for Extending Partial Milk
Records to Determine Economically Optimal Culling
Decisions. Research Report #144, Georgia Agric.
Exp. Sta. Research Bulletin, September.

Smith, N. E. 1975. Maximizing Income Over Feed Costs:
Evaluation of Production Response Relationships.
J. Dairy 8010) 5931195.

Sonderegger, H., and A. S. Church. 1977. A Study of the
Influence and Supply of the Energy and Protein Sup-
ply on the Fertility of Dairy Cows. Livestock Pro-
duction Science, 4:327.

Stewart, H. M., E. B. Burnside, J. J. Wilton and W. C.
Pfeiffer. 1977. A Dynamic Approach to Culling De-
cisions in Commercial Dairy Herds. J. Dairy Sci.,
60:602.

Thomas, J. W., L. D. Brown, and R. S. Emery. 1970. Corn
Silage Compared to Alfalfa Hay for Lactating Cows
When Fed Various Levels of Grain. J. Dairy Sci.,

53:342.

Thomas, J. M. 1971. Protein Requirements of Milk Cows.
J. Dairy Sci., 54:1629.

Thomas, J. W. 1979. Personal Communication.

Thornton, R. F. and D. J. Menson. 1972. The Relationship
Between Voluntary Intake and Mean Apparent Retention
Time in the Rumen. Aust. Journal of Agric. Res.,
23:871.

Trimberger, G. J., H. F. Tyrrell, D. A. Morrow, J. T. Reid,
M. J. Wright, W. F. Ships, 3. G. Merrill, J. K.
Loosli, C. E. C0ppock, L. A. More and C. H. Gordon.
1972. Effects of Liberal Concentrate Feeding on
Health, Reproductive Efficiency, Economy of Milk Pro-
duction and Related Responses of the Dairy Cow.
Food and Life Sciences Bulletin #8, Cornell University,
Ithaca, New York.

123

USDA-DHIA. 1974. Factors for Standardizing 305-day Lacta-
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40, Agricultural Research Service. U S Dept. of
Agric.

Van Horn, 33 31. 1968. Effect of High-Urea Supplementa-
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Cows. J. Dairy Sci., 50:709.

Van Ostergaard. 1978. Strategies for Concentrate Feeding
to Attain Optimum Feeding Level in High Yielding
Dairy Cows. National Institute of Animal Science.
Rolighedsvej 25, DK-1958. Copenhagen V, Denmark.

Wheeler, W. E., and C. H. Noller. 1976. Limestone Buf-
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J. Dairy Sci., 59:1788.

White, J. M. and J. R. Nichols. 1962. Reasons for Dis-
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Sci., 48:512.

Wilkinson, J. W. 1977. The Control of Voluntary Feed In-
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Lansing, Michigan.

Wood, P. D. 1969. Factors Affecting the Shape of the Lao-
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Wood, P. D. 1970. A Note on the Repeatability of Para-
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of Animal Production, 12:535.

Wood, P. D. 1976. Algebraic Models of the Lactation Curves
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Production, 22:35.

APPENDIX

124

VV‘VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV

THE DAIRY COW REPLACEMENT MODEL

ONCE THE USER HAS SIGNED ON HE/SHE SHOULD ENTER : $RUN FSUB

SLIST HOD6+NNT+NNATEQ+NXLACT+NDHI+NREQ+TBAL+NNUT+NLP+COST+REV

100
110
120
130
140
150
160

390
600
610

430
440
450
460
670
680

490
500

cc twee MAIN ante

CC THIS PROGRAM CALCULATES THE FEED DISAPPEARANCE, REVENUE,
CC COST, DISCOUNTED NET PRESENT VALUE, AND THE ANNUALIZED NET
CC PRESENT VALUE OVER THE COURSE OF THE LIFETIME OF A CON. '
CC THE FAILURE RATE -- THE ODDS THAT A COW DOESN'T MAKE IT

CC FROM YEAR T TO T+1 -- ARE EXPLICITY ACCOUNTED FOR. AS A

CC CONSEQUENCE, RESULTS ARE STATED ON AN "EXPECTED VALUE"

CC BASIS.

CC DEVELOPED BY JOE HLUBIK, DAIRY SCIENCE, NICH STATE UNIV
CC AND J.ROY BLACK,AG.ECONOHICS,HICH STATE UNIV.
CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

CC TO EXECUTE THE PROGRAM USER SHOULD ENTER:

CC 1)NUNBER OP LACTATIONS TO CONSIDER

CC 01 TO 07

CC 2)NONTHS IN NILK/LACTATION

CC 09 TO 15

CC 3)AGE OF THE CON AT THE BEGINNING OF ANALYSIS
CC 02. YEARS TO 08.YEARS

CC 4)NONTHS OF AGE AT THE BEGINNING OF ANALYSIS
CC 20 TO 96

CC 5)HE LBS OF MILK

CC 10000. TO 20000.

CC 6)ESTIHATED NATURE WEIGHT

CC 1200. TO 1800. -

CC 7)SALVAGE PRICE($/LB FOR CULL BEEF)

CC .00 TO ?.00

CC ‘J 8)DOES USER WANT NUTRIENT OUTPUT?

CC 01-N0,03-YES

CC 9)DOES USER WANT OUTPUT OTHER THAN NILE

CC PRODUCTION AND ESTIMATED WEIGHT?

CC 01-YES,OO-NO '

CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
CC OUTPUT:

CC OUTPUT IS INCLUDED FOR EACH MONTH WITHIN EACH LACTATION.

CC A SUMMARY OF LACTATION FEED DISAPPEARANCE AND ECONOMICS FOR

CC EACH LACTATION AS WELL AS THE EXPECTED ANNUALIZED NET PRESENT

CC VALUE(CONSIDERING PROBABLE HERD LIFE) CONCLUDES EACH ANALYSIS.
CC

CC NUTRITION OUTPUT FOR EACH MONTH WITHIN EACH LACTATION INCLUDES:
CC LINE 1: RESULTS ARE (COST OF RATION)

125

VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV-VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV

870
880
890

CC

CC
CC
CC
CC
CC
CC
CC
CC

CC
CC

CC

LINE 2:

LINE 3:

LINE 6:

LINE 1:

LINE 2:
LINE 3:

126

LBS OF EACH FEEDSTUFF DELIVERED(SHELLED CORN,CORN
SILAGE,ALFALFA HAY,SOY MEAL46,UREA,DICAL,LIMESTONE,
SALT)

NUTRIENT REQUIREMENTS AND RESTRICTIONS(DMI,NE,CP,CF,
CA,PHOS,CA:PHOS-LB,CA:PHOS-UB,SUPPLEMENTAL NPN,ENIMP,
FMIMP,WT LOSS,CONSTRAINTS ON ROUGHAGES)

REQUIREMENTS DELIVERED (LP SOLUTION).

ECONOMIC OUTPUT FOR EACH MONTH WITHIN EACH LACTATION INCLUDES:

FEED SUMMARY FOR THE MONTH

LACTATION FEED SUMARY TO DATE

FEED COST, OTHER COSTS, TOTAL COSTS, GROSS REVENUE,NET
REVENUE, DISCOUNTED NET REVENUE(CALCULATED AT THE END
OF THE LACTATION).

ECONOMIC SUMMARY AT THE END OF THE ANALYSIS INCLUDES:

NET PRESENT VALUE,SALVAGE VALUE,ANNUALIZED NPV(EXCLUDING
ODDSJAND A FEED SUMMARY FOR EACH LACTATION.FINALLY EXPECTED
ANNUALIZED NET PRESENT VALUE,INCLUDING PROBABLE HERD LIFE,
IS ESTIMATED.

CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

CC

DEFINITIONS:

MNTAGE
MNTCLF
XMEMLE
PRODYR

WTME
PRODDY
PROD
PRODLG
WEIGHT
GROWTH

DMI
DMILAG
CTFEED
CTOTHR
CTTOT
RATE

P

F

TF

GTF
GREV
DNREV
XNPV
ANPV
IOPT1

IOPTZ

NOLAC
MNLAC
AGEINT
MNTAGE
PRODME
WTME
PRSAL

AGE IN MONTHS(.GE.26)

MONTH OF CALVING(1...12)

MILK/305 DAY LACTATION,DHIA ME BASIS

MILK/305 DAY LACTATION,LBS PROJECTED FOR
LACTATION J.

ESTIMATED MATURE WEIGHT,LBS

MILK YIELD,LBS/DAY

MILK YIELD,LBS/DAY

MILK YIELD,PREVIOUS MONTH

BODY WEIGHT AT CALVING,FOR LACTATION J.

WEIGHT GAINED,LBS/DAY AS A FUNCTION OF
GROWING OLDER

DRY MATTER INTAKE,LBS/DAY

DRY MATTER INTAKE,LBS/DAY,PREVIOUS MONTH

FEED COST

OTHER COSTS

TOTAL COSTS

DISCOUNT RATE

PRICE

FEED

TOTAL FEED

GRAND TOTAL FEEDS

GROSS REVENUE

DISCOUNTED NET REVENUE

NET PRESENT VALUE

ANNUALIZED NET PRESENT VALUE

DECISION WHETHER OR NOT NUT INFO SHOULD
BE LISTED

DECISION WHETHER OR NOT INFO OTHER THAN

BODY WEIGHT AND MILK YIELD SHOULD BE LISTED

NUMBER OF LACTATIONS TO CONSIDER

NUMBER OF MONTHS COWS MILK/LACTATION

AGE IN YEARS AT THE BEGINNING OF THE ANALYSIS
MONTHS OF AGE AT THE BEGINNING OF THE ANALYSIS
PRODUCTION MATURE EQUIVALENT(MILK)

ESTIMATED MATURE WEIGHT

SALVAGE PRICE/LB OF CULL BEEF

CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

CC

CC CURRENT VERSION IS AS OF 28 NOVEMBER, 1977.

CC JULY 8,

CC

1978 REVISION

VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV

1410
1420
1430
1440
1450
1460
1470
1480
1490
1500
1510
1520
1530
1540
1550
1560
1570
1580
1590
1600
1610
1620
1630
1640
1650
1660
1670
1680
1690
1700
1710
1720
1730
1740
1750
1760
1770
1780
1790
1800
1810
1820

127

DIMENSION FEED(13,8),CTFEED(13),CTOTHR(13),
+ CTTOT(13),GREV(13),XNREV(13),DNREV(13),
+ XNPV(13),ANPV(13),GTF(8),TF(13,8),F(8),P(8)
cc
DATA P/.042,.0235,.0300,.09oo,.osoo,.125,.035,.04/
cc
Do 308 I-1,13
CTFEED(I)-O.
CTDTER(I)-o.
GREV(I)-O.
XNREV(I)-O.
DNREV(I)-o.
XNPV(I)-0.
308 ANPV(I)-O.
cc
Do 280 I-1,8
280 GTE(I)-o.
cc
Do 282 I-1,13
Do 282 J-1,8
282 TE(I,J)-o.
cc
640 E0RNAT(/)
cc
READ 1,NoLAc
READ 1,NNLAc
READ 2.AcEINT
READ 1.NNTAcE
READ 2,PRODME
READ 2,wTNE
READ 2,PR5AL
cc ‘
READ 1,10PTI
READ 1,10PT2
cc
2 FORMAT(FI0.0)
1 FORMAT(I2)
AGE-AGEINT
RATE.012
N0NTR-NNLAQ¥2
TIME-0.
MTHCLF-l
cc
PRINT 640
cc
Do 300 I-1,N0LAc
CALL XMATEQ(MNTAGE,MTHCLF,XMEMLK,XMEFAT)
PRODYR-PRODME/XMEMLK
CALL NT(NNTAcE,wTNE,wEIcRT,cRowTE)
CALL XLACT(AGE,7.,PRODYR,PROD)
cc
PRINT 600.AcE
600 E0RMAT(1x,'AcE:',Elo.0)
PRINT 620,wEIcRT
620 FORMAT(1X,'WEIGHT:',F10.0)
PRINT 610,PRODYR
610 FORMAT(1X,'PRODUCTION:',F10.0)

PRINT 640
CC
WTLOSSIO.
CC
D0 340 J-1,MONTH
CC
TIME-TIME+1.
CC

VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV

128

WEEK-((J+(J-1))/2.)*4.3
cc

IP(IoPT2.EQ.0)co To 683

PRINT 680,NEER

680 P0RNAT(1x,'wEER:',P10.0)
PRINT 682,TINE

682 P0RNAT<1R,'N0NTRS SINCE ENTERING HERD',P10.0)

cc
CC TEST WHETHER IN LACTATION 0R DRY PERIOD
683 IP(J.CT.NNLAC)00 To 684
CC
IP(NEER.CT.8.)PRoDLc-PRDDDT
CALL XLACT(AGE,WEEK,PRODYR,PRODDY)
PRINT 1092,PRODDY
1092 FORMAT(1X,'DAILY NILR',P10.2)
IP(WEEK.GI.8.)DMILAG-DMI
CALL DMILAC(AGE,WEEK,PROD,PRODDY,PRODLG,WEIGHT,
s DNI,DNILAC)
PCT-(DMI/WEIGHT)*100.
CALL REQLAC(ACE,wEER,PRODDP,REICET,CRowTR,
& WTCHG,ITPCHG,DMI,EN,CP,CA,PHOS,SALT,XNPN,CAPHLB,
& CAPHUB,FMIMP,ENIMP)
IF(WEEKCGT080 OAND. "EEK 0LT. 12.)WTCHG-0.
IP(wEER .CE. 12. .AND. NEER .LE. 60.)NTCRC-wTL055/196.
IF(WEEK.GT.40.)WTCHG-O.
CALL EAL(DNI,EN,CP,CA,PROS,SALT,XNPN,CAPELR,
+ CAPRUE,PNINP,ENINP,wTCRc,ITPCRC,P,P,10PT1)
IF(WBEK.LE.8.)WTLOSS-WTLOSS+WTCHG*30.
co To 686
cc
684 PRODDY-0.
DMILAG-DMI
CALL DMIDRY(AGE,WEEK,PROD,PRODDY,PRODLG,WEIGHT,
6 DNI,DNILAC)
PCT-(DMI/WEIGHT)*100.
CALL REQDRY(AGE,WEEK,PRODDY,WEIGHT,GROWTH,
& NTCRC,ITPCRC,DNI,EN,CP,CA,Puos,SALT,XNPN,CAPRL8,
& CAPRUE,PNINP,ENINP)
CALL 8AL(DNI,EN,CP,CA,PR08,SALT,xNPN,CAPNLE,
+ CAPRUR,PNINP,ENINP,wTCRc,ITPCNC,P,P.ICPT1)
wTCRc-o. -

686 CALL REVNUE(PRODDY,REV)

CALL C05T(PR00NE.0VC)

CC.
00 312 R-1,8

312 EEED(I,R)-0.

cc
D0 380 K-1,8
FEED(I,K)-FEED(I,K)+F(K)*30.
TF(I,K)-TF(I,K)+FEED(I,K)

380 CTFEED(I)-CTFEED(I)+F(K)*P(K)*30.
CTOTER(I)-CT0TNR(I)+0VC
CTT0T(I)-CTPEED(I)+CT0TER(I)
GREV(I)-GREV(I)+REV
RNREV(I)-CREV(I)-CTT0T(I)
IF(J.EQ.MONTH)DNREV(I)-XNREV(I)/((1.+RATE)**I)

cc
IP(I0PT2.EQ.0)co T0 343
PRINT 800,(PEED(I,R),R-1,8)
PRINT 800,(TP(I,R),R-1,8)
PRINT 800,CTPEED(I).CT0TER(I),CTT0T(I),CREV(I>,xNREV(I),
+ DNREV(I)
PRINT 640
800 FORMAT(IX,14P8.2)
CC
343 MNTAGE-MNTAGE+1

VVVVVVVVVVVVVVVVVVVVVVVV\‘VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV

190
200
210
220

240
250
260
270
280
290
300
310
320
330
340
350
360
370
380
390
400
410
420
430

CC
340
CC

300
CC
CC

CC

807

500

804
CC

CC
CC

CONTINUE

AGE-AGE+1
PRINT 640
PRINT 640
CONTINUE

EVALUE-OO
CUMODD-I.
AGE-AGEINT

D0 500 I-1,N0LAC

J-I-I
IF(I.LT.NOLAC)ODDS-1.-(.95-.0183*ACE)
IF(I.LT.NOLAC)CUMODD-CUMODD-ODDS
IP(I.EQ.NOLAC)0DDS-CUMODD
IP(I.EQ.1)xNPV(I)-DNREV(I)
IE(I.CT.1)XNPV(1)-xNPv(J)+DNREV(I)
xx-(1.+RATE)**I

SALVG-WBIGHT*PRSAL/XX
ANPV(£)-(XNPV(I)+SALVC)*(RATE*XX/(XX-1.))
PRINT 807,xNPV(I),SAch,ANPV(I),ODDS
PRINT 800,(TF(I,K),K-1,8)
P0RNAT(1R,5P10.2)
EVALUE-EVALUE+ODDS*ANPV(I)

ACE-ACE+1

PRINT 806,EVALUE

FORMAT(1X,'EXPECTED ANNUALIZED NPV Is:',P10.1)

END
SUBROUTINE WT(MNTAGE,WTME,WEIGHT,GROWTH)

CC CALCULATES WEIGHT BASED ON EXPECTED MATURE WEIGHT(ENTERED BY THE

CC

USER) AND MONTHS OF AGE.

CC BASED UPON:MC'DANIEL,B.T. AND J.E.LEGATES,"ASSOCIATIONS

CC
CC
CC

BETWEEN BODY WEIGHT PREDICTED FROM HEART GIRTH AND
PRODUCTION,J.DAIRY SCI.,48:947.1965.

CC DEFINITIONS:

CC
CC
CC

MNTAGE ACE IN NDNTNS
NTNE ESTIMATED NATURE NEICRT

CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

CC
CC

CC

IF(MNTACE.GE.84) GO TO 10
AGE-MNTAGE

TEMPI.5161+.01423*AGE-.000139*AGE*AGE+.00000045*AGE
*AGE*AGE
WEIGHT-TEMP*WTME

CC SECTION T0 CALCULATE THE GROWTH RATE, LBS/DAY.
CC CALCULATED BY CALCULATING THE CHANGE IN WEIGHT OVER THE
CC PERIOD OF A MONTH AND DIVIDING THAT RESULT BY 30 DAYS.

CC

CC

CC

AGETMP-AGE-1.

TEMP1-.5161+.01423*AGETMP-.000139*AGETMP*AGETMP+
.00000O45*AGETMP*AGETMP*AGETMP

WT1-TEMP1*WTME

GROWTH-(WEIGHT-WT1)/30.

‘IVVVVVVVVVVVVVVVVVVVV‘VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV

VVVVVVVVVVVVV

130

CC
RETURN
10 GROWTH'O.
WEIGHT-WTME
RETURN
CC
END

CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
SUBROUTINE XMATEQ(MNTAGE,MONTH,XMEMLK,XMEFAT)

CC COMPLETED ON JULY 4,1978

CC CALCULATES THE ADJUSTMENT FACTOR TO TAKE A COW OF ANY

CC AGE AND CONVERT HER TO A "MATURE EQUIVALENT" BASIS.

CC BASED ON: NORMAN,P.ET.AL."USDA-DHIA FACTORS FOR STANDARDIZING

CC 305-DAY LACTATION RECORDS FOR AGE AND MONTH OF CALVING"ARS-NE-40
CC 1974.THE DATA FOR XMNMLK AND XMNFAT ADJUST MILK YIELD FOR

CC MONTH OF CALVING USING JANURARY AS A STANDARD REFERENCE.

CC DEFINITIONS:

CC AGEMNT: AGE IN MONTHS V

CC XMEMLK: MATURE EQUIVALENT FACTOR FOR MILK

CC XMEFAT: MATURE EQUIVALENT FACTOR FOR BUTTER FAT

CC XMNMLK: MONTH OF CALVING ADJUSTMENT FACTOR FOR MILK

CC XMNFAT: MONTH OF CALVING ADJUSTMENT FACTOR FOR BUTTER FAT

CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
DIMENSION XMNMLK(12),XMNFAT(12)
DATA XMNMLK/0.,.02,.02,.O4,.06,.09,.12,.12,.08,.04,.02,.02/
DATA XMNFAT/O.,.01,.02,.04,.06,.08,.1,1.,.05,.02,.0,.0/

CC
AGEMNT-MNTAGE

CC
IF(AGEMNT.LE.70.)XMEMLKI.809+(10.68/AGEMNT)
IF(AGEMNT.GT.70..AND.AGEMNT.LE.94.)XMEMLK-.96
IF(AGEMNT.GT.94.)XMEMLK-.96+.0016*(AGEMNT-94.)
XMEMLK-XMEMLK+XMNMLK(MONTH)

CC
IF(AGEMNT.LE~73.)XMEFAT-.817+(10.276/AGEMNT)
IF(AGEMNT.GT.73..AND.AGEMNT.LE.80.)XMEFAT-.96
IF(AGEMNT.GT.80.)XMEFAT-.96+.0014*(AGEMNT-80.)
XMEFAT-XMEFAT+XMNFAT(MONTH) ‘

CC
RETURN
END

CC OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

CCOCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
SUBROUTINE XLACT(AGE,WEEK,PRODYR,PRODDY)

CC“CALCULATES MILK/DAY FROM LACTATION CURVE

CC DEFINITIONS:

CC AGE LACTATION NUMBER

CC WEEK WEEKS INTO LACTATION

CC PRODYR ANNUAL PRODUCTION,LBS MILK

CC PRODY AVERAGE DAILY PRODUCTION FOR WEEK N INTO
CC LACTATION

CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
DIMENSION A(3,3)
DATA A/3.661,.093,.00291,
+ 3.708,.129,.OO486,
+ 3.593,.165,.OO486/
DAY.REEK* 7 o
IAGE-AGE
IF(AGE.GE.3)IACE.3
CC
CC ADJUST INTERCEPT OF EQUATION FOR THE PRODUCTION OF THE

VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV-VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV

630

131

CC COW RELATIVE TO THE "STANDARD - 12,000 LB" COW. SEE: SHULTZ
CC A.A., "FACTORS AFFECTING THE SHAPE OF THE LACTATION

CC CURVE AND ITS MATHEMATICAL DESCRIPTION,MASTER'S THESIS,

CC U OF WISCONSIN,1974.

CC
TEMP-A(1,IAGE)+ALOG(PRODYR/12000.)
CC
PRODDY-EXP(TEMP+A(2,IAGE)*ALOG(DAY)-A(3,IAGE)*DAY)
RETURN
END

CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
SUBROUTINE DMILAC(AGE,WEEK,PROD,PRODDY,PRODLG,WEIGHT,
8 DMI,DMILAG)

CC A
CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
CC POTENTIAL DRY MATTER INTAKE

CC

CC

CC **** OBJECTIVE ****

CC ESTIMATES DAILY DRY MATTER INTAKE.THE REGRESSION ESTIMATING

CC INTAKE DURING EARLY LACTATION(WEEKS 1 TO 9),IS BASED ON

CC DATA OF: FOLDACER,J."PROTEIN REQUIREMENT AND NON PROTEIN NITROG
CC FOR HIGH PRODUCING COWS IN EARLY LACTATION,"PH.D.THESIS,MICH.
CC STATE UNIV.,E.LANSING.1977. INTERCEPT ADJUSTMENT(UPWARD)WAS
CC INCORPORATED BASED ON: TRIMBERGER,G.W., ET AL. CORNELL BULLETIN
CC N008519720

CC BEYOND WEEK 9 INTO LACTATION INTAKE IS ADJUSTED DOWNWARD

CC CONSIDERING THE IMPACT OF CHANGE IN MILK YIELD ON DRY MATTER

CC INTAKE(DMI).THIS ADJUSTMENT IS BASED ON DATA OF:SLACK,S. ET AL.
CC BULLETIN NO.957,CORNELL UNIV.AGRIC.EXP.STA.,ITHACA,NEW YORK

CC 1960. FOLDAGER,J.,PH.D.THESIS,MSU,1977. AND LAMB,R.C.

CC ET AL.,J.DAIRY SCI.57:811.1973.

CC THE IMPACTS OF ENERGY DENSITY AND FERMENTATION UPON DMI IS

CC ACCOUNTED FOR IN THE L.P. MATRIX.

CC
CC **** DEFINITIONS ****
CC WEEK NUMBER OF WEEKS INTO THE LACTATION
CC PROD MILK PRODUCTION, LES/DAY DURING WEEKS 6 TO 8
CC PRODDY MILK PRODUCTION,LBS/DAY
CC PRODLG PRODUCTION IN PREVIOUS PERIOD,LBS/DAY
CC WEICRT' COW'S BODY WEIGHT AT CALVING, LES
CC DMI DRY MATTER INTAKE, LES/DAY
CC DMILAG DRY MATTER INTAKE IN PREVIOUS PERIOD,LBS/DAY
CC ACE A2 IS TWO YR OLD
CC A3 IS THREE YEAR OLD
CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
CC
CC
IP(AGE.LE.2)AGE2-1.
IE(AGE.GT.2)AGEZ.O.
IF(AGEOEQ03)A683.10
IF(AGE.NE.3)AGE3'O.
CC
DMI-2.30+1.0S*ACE2+.74*ACE3
6 +.OOO4649*(WEIGHT/2.2)+.ZO719*(PROD/2.2)
CC
IE(WEER.LE.9.)DMI'(DMI+2.0906*WEEK-.183139*WEEK*WEEK
8 +.0046684*WEEK*WEER*WEER)*2.2
CC
IF(WEEK.CT.9.)DMI'DMILAG-.20*(PRODLG-PRODDY)
CC
RETURN
END
CC
CC

VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVvvv

640
650
660

670

132

SUBROUTINE DMIDRY(AGE,WEEK,PROD,PRODDY,PRODLG,WEIGHT,
6 DMI,DMILAG)

CC

DMI-DMILAG
CC

RETURN

END

CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
CC
SUBROUTINE REQLAC(AGE,WEEK,PRODDY,WEICHT,GROWTH,
& WTCHG,ITPCHG,DMI,EN,CP,CA,PHOS,SALT,XNPN,CAPHLB,
5 CAPHUB,FMIMP,ENIMP)

CC BASED UPON THE 1978 DAIRY NRC

CC IMPACT OF FERMENTED FEEDS AND ENERGY DENSITY OF THE
CC RATION ADDED DURING OCT.1978.

CC IMPACT OF ENERGY MOBILIZATION FROM BACKFAT DEVELOPED
CC DURING SEPTEMBER 1978.
CC DEFINITIONS:

CC EN NET ENERGY FOR LACTATION,MCAL/DAY

CC CP CRUDE PROTEIN,LBS/DAY

CC CA CALCIUM,LBS/DAY

CC PHOS PHOSPHORUS,LBS/DAY

CC CAPHLB MINIMUM CALCIUM TO PHOSPHORUS RATIO

CC CAPHUB MAXIMUM CALCIUM TO PHOSPHORUS RATIO

CC SALT SALT REQUIREMENT,LBS/DAY

CC CF CRUDE FIBER,LBS/LB OF DRY MATTER

CC FMIMP IMPACT ON DRY MATTER INTAKE(DMI)PER CWT

CC OF BODY WEIGHT OF A ONE PERCENT INCREASE

CC IN THE MOISTURE CONTENT OF THE DIET.MOISTURE
CC ACTS AS A PROXY FOR THE IMPACT OF FERMENTED
CC FEEDS.BASED ON DATA OF:BROWN,L.D. ET AL.,

CC "EFFECTS OF FEEDING VARIOUS LEVELS OF CORN

CC SILAGE AND HAY WITH HIGH LEVELS OF GRAIN

CC TO LACTATING DAIRY COWS," J.DAIRY SCI.,

CC 48:816.1965.

CC XNPN UPPER BOUND ON SUPPLEMENTAL NPN AS A FRACTION
CC OF CRUDE PROTEIN

CC FATPCT PERCENT BUTTER FAT IN THE MILK

CC WTCHG CHANGE IN THE AMOUNT OF BODY FAT/COW/DAY.THE

CC 6 COW IS PERMITTED TO LOOSE WEIGHT DURING THE
CC FIRST EIGHT WEEKS OF LACTATION.THE WEIGHT

CC MUST BE REGAINED DURING WEEKS 13 THROUGH 40.
CC BASED ON:FLATT,W.P. ET AL."ENERGY UTILIZATION
CC BY HIGH PRODUCING DAIRY COWS.II.SUMARY OF

CC - ENERGY BALANCE EXPERIMENTS WITH LACTATING

CC HOLSTEIN COWS."ENERGY METABOLISM OF FARM

CC ANIMALS.(ORIEL PRESS LTD.:NEWCASTLE UPON TYNE
CC ENGLAND)1967.P.235. AND TRIMBERGER,G.W.

CC ET AL."EFFECTS OF LIBERAL CONCENTRATE FEEDING
CC ON HEALTH,REPRODUCTIVE EFFICIENCY,ECONOMY

CC OF MILK PRODUCTION AND RELATED RESPONSES OF
CC THE DAIRY COW."FOOD AND LIFE SCIENCES BULL.
CC #8.CORNELL UNIV.ITHACA,NEW YORK.1972.

CC ITPCHG FLAG TO DEPICT WHETHER WTCHG IS A GAIN OR LOSS.
CC WTLOSS TOTAL FAT LOSS OVER THE FIRST 60 DAYS OF LACT.
CC LOSS IS REGAINED DURING THE 196 DAYS OF WEEKS
CC 13 THROUGH 40. ~

CC ENIMP IMPACT ON DRY MATTER INTAKE PER CWT OF BODY

CC WEIGHT OF A ONE MCAL CHANGE IN NET ENERGY

CC OF LACTATION PER LB OF DRY MATTER.BASED ON

CC DATA OF: LAMB,R.C. ET AL."RESPONSE TO

V V‘IV VVIV V\IV V‘IV V‘IV V\/V V\IV V\IV V\IV V\/V V‘IV.V\IV V‘IV V\/V V\/V V\IV V\IV V\IV V\IV V\/V V\/VV’V

1270
1280
1290
1300
1310

155

CC CONCENTRATES CONTAINING TWO PERCENTS 0F

CC PROTEIN FED AT FOUR RATES FOR COMPLETE
CC LACTATIONS," J.DAIRY SCI.,57:811.1973.

cco0000000000000000000000000000000000000000000000oooooooooooooooooo
cc
cc
FATPCT-3.5
NTRc-NEIcaT/2.2
WTMTKG-WTKG**.75
CC
EN-.O7996*WTMTKG+(.31+.O429*(FATPCT-3.5))*PRODDY
6 +GROWTH*2.32
CP-.00908*RTNTRC+(.082+.o1o7*(PATPCT-3.5))*PR0DDY
6 +GROWTB*.5
CA-.OOO38*WTMTKG+(.0026+.00022*(FATPCT-3.5))*PRODDY
PHOS-.0003*WTMTKG+(.0018+.000067*
8 (FATPCT-3.5))*PRODDY
SALT-.00009031*NEICRT+.00022*PRODDT
CC
IF(WEEK.LE.4.)WTCHG-(EN*.3/3.4)
IF(W£EK.GT.4..AND.WEEK.LE.8.)WTCEG-(EN‘.15/3.4)
IP(NEER.LE.8.)ITPCRc-1
IF(WEEK0G1080)ITPCHG.2
cc
PCTCP-CP/DMI
IP(PCTCP.GT..14)XNPN-O.
IF(PCTCP.LE..14)XNPN-.3O

CAPHLB-2.
CAPHUB-2.5
CC
IF(WEEK.LE.40.)FMIMP-(.OO7+.OOO39*WEEK)*(WEIGHT/100.)
IF(WEEK.GT.40)FMIMP-.023*(WEIGHT/100.)
ENIMP-1.76*(WEIGHTI100.)
CF-.16
CC
CC
RETURN
END
CC
CC '

SUBROUTINE REQDRY(AGE,WEEK,PRODDY,WEIGHT,GROWTH,
8 WTCHG,ITPCHG,DMI,EN,CP,CA,PHOS,SALT,XNPN,CAPHLB,
& CAPHUB,FMIMP,ENIMP)

CC
CC VERSION AS OF JULY 26,1978
CC
wac-NEICET/2.2
WTMTKG-WTKG**.75
CC
ENI.1040*WTMTKG+CROWTH*2.32
CP-.017*WTMTKG+GROWTH*.5
CA-.00066*WTMTKG
PHOS-.OOO46*WTMTKG
SALT-(.041*WEIGHT)/454.
CC
WTCHG-O.
IF(WEEK.LE.8.)ITPCHG-1
IF(WEEK.GT.8.)ITPCHG-2
CC
CAPHLB-1.
CAPHUB-1.5
CC
XNPN-.3
CC
CC

‘IVVVVVVVVVVVVVVVVVVVVVVVVV

V'vA/v V\IV v

IVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV

151+

ENIMP-.023*(WEIGHT/100.)
ENIMP-1.76*(WEIGHT/IOO.)
CF-o 25
CC
RETURN
END
CCOOOOOOOOOOOOOOOOOOO000000000000000000000000OOOOOOOOOOOOOOOOOOOOOO
CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO0000000000000000000000000000000
SUBROUTINE BAL(DMI,EN,CP,CA,PHOS,SALT,XNPN,CAPHLB,
+ CAPHUB,FMIMP,ENIMP,WTCHG,ITPCHG,F,P,IOPT1)
cc VERSION: JULY 5,1978
CC BALANCES A RATION IN A LEAST COST MANNER CONSIDERING:
CC NUTRIENT REQ.,NUT.RESTRICTIONS,POTENTIAL DRY MATTER INTAKE
CC (DMI),THE EPPECTS OF ENERGY DENSITY AND PERMENTATION OP PEEDS 0
CC DMI AND TEE ABILITY OP cows T0 DRAW UPON PAT RESERVES IN EARLY
cc LACTATION. BODY FAT 13 PRICED AT A RELATIVELY HIGH PRICE AND 13
CC DRAWN IN As A SOURCE 0P ENERGY NREN THE RATION WILL NOT
CC OTHERWISE EALANCE(DURINC EARLY LACT.)
CCOOOOOOOOOOOOOOOO000000OOOOOOOOOOOOOOO000000OOOOOOOOOOOOOOOOOOOOOO
CC
CC
COMMON/LP/DPM(16,36),RNS(16),ISACOL(16),INACT(16),
+ IRwTY(16),OEJ(36).ZC(36)
COMMON/NUT/A(8.9)
DIMENSION 8(16), P(8),P(8),ACT(36)
DIMENSION TEMP(16)
DOUELE PRECISION DPM,RES,OSJ,zC
CC
CC“? IS THE 2 OF EACH EEED IN TEE DIET DRY MATTER (8 PEEDS)
CC P 18 THE PRICE OP FEEDSTUFF. $ILE

CC RECALL, IBM READS DATA ACCORDING TO A(1,1), A(2,1),
CC A(2,2),ETC.

cc
cc *****MATRIX LAYOUT FOR DPM***

cc

CC Rows:

CC 1 - DM INTAKE, LES.

cc 2 - NET ENERGY, MCAL

CC 3 - CRUDE PROTEIN, LBS.

CC 4 - CRUDE FIBER, LBS

cc 5 - CA

CC 6 - PROS

CC 7 - SALT

CC 8 - CALCIUM:PEOSPROROUS RATIO (LOWER BOUND)

cc 9 - CALCIUM:PROSPROROUS RATIO (UPPER SOUND)

CC 10 - SUPPLEMENTAL NPN:CRUDE PROTEIN RATIO

cc 11 - IMPACT OP NET ENERGY/LE ON DRY MATTER INTAKE

cc 12 - IMPACT OE PERMENTATED PEEDS ON DRY MATTER INTAKE-
CC 13 - UPPER ROUND ON WEIGHT LOSS DURING 1ST 8 NEERS.
CC MEASURES WEIGHT GAIN TREREAPTER.

CC 14 - CONSTRAINTS ON zACEs 0P CORN SILAGE AND ALPALPA
CC IN THE 'ROUGEAGE'

cc

CC COLUMNS:

cc 1 - CORN GRAIN

cc 2 - CORN SILAGE

CC 3 - ALPALPA

cc 4 - SOY 44

CC 5 - UREA

cc 6 - DICAL

cc 7 - LIMESTONE

CC 8 - SALT

CC 9 - IMPACT OP PERMENTED PEEDS 0N DRY MATTER INTAKE, LES
CC 10 - IMPACT OP NET ENERGY/LR 0N DRY MATTER INTAKE, LES.
cc 11 - NEIGET LOSS (GAIN)

V\IV V‘IV‘V\IV V‘IV V\IV V\IV V\IV V\IV VNIV V\IV V\IV'K\IV V\IV V\IV’V\IV V\IV V\IV\IV‘VVVIV‘V‘IV\IV\MIV\IV'

C
C

CC

17

155

NROW-16
JREAL-ll
JCOL-ll

C .
C ZERO OUT ACT(J),OBJ(J)

DO 17 J-l,36
ACT‘J)-00
ODJ(J)-0o

CCTZERO OUT DPH

CC

CC

20

16

DO 20 [-1.16
DO 20 J-1.36
DPM(I,J)-O.

DO 16 J-l,8
OBJ(J)--P(J)
IF(ITPCRG.EQ.1)ODJ(11)--.20
IF(ITPCHGOEQ02)OBJ(11)-o20

CC“LOAD DPH

CC

CC

CC

CC

CC

30

DO 30 J-1,8

DPM(1,J)-A(J,1)
DPM(2,J)-A(J,2)
DPM(3,J)-A(J.3)
DPM(4,J)-A(J.4)-.16
DPM(5,J)-A(J,5)
DPM(6,J)-A(J,6)
DPM(7,J)-A(J,7)
DPM(8,J)-A(J,S)-CAPHLB*A(J,6)
DPM(9,J)-A(J,S)-CAPHUB*A(J,6)
DPM(10,J)-A(J,9)-XNPN*A(J,3)
DPM(11,J)-(.72-A(J,2))/DMI
DPM(12,J)-(A(J,8)*100.-20.)IDMI
CONTINUE

DPH(14,2)--1.

DPH(12,9)--l.
DPM(1,9)-FHIMP

DPM(11,10)-él.
DPH(1,10)-ENIHP

IP(ITPCHC.EQ-1)DPM(2,11)-3o6
IF(ITPCHC.GE.2)DPH(2,11)--4o8
DPH(13,11)'1.

CCTLOAD RESTRICTIONS

CC

RHS(1)-DMI
RNS(2)-EN
RBS(3)-CP
RHS(4)-0.
RBS(5)-CA
RHS(6)-PBOS
RHS(7)-SALT
RBS(8)-0.
RBS(9)-0.
RDS(10)-O.
RHS(11)-O.
RBS(12)-O.
RBS(13)-HTCHG
RHS(16)-O.

IRWTY(1)-l
IRWTY(2)-2
IRWTY(3)'3

VVVVVVVVVVVVVV'JVVVVVVVVVVVVVVVVVVYVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV

CC

156

IRNTY(b)-3

IRNTY(5)-3

IRWTY(6)-3

IRWTY(7)-3

IRNTY(8)-3

IRWTY(9)-l

IRWTY(10)-1

IRWTY(11)-l

IRWTY(12)-1
IF(ITPCHG.EQ.1)IRWTY(13)-1
IF(ITPCRG.GE.2)IRWTY(13)-2
IRNTY(16)-1

DO 93 I-1,NRON

CC“B(I) ARE THE RESTRICTIONS

93
CC

B(I)-RRS(I)

CC‘FILL IN SLACKS AND ARTIFICIALS

90
CC

CC

DO 90 I-l,NROU
CALL RONSET(I,JCOL)

IF(IOPT1-EQ.2)CALL LPDUMP(NROW,JCOL)

CC‘CALL SOLUTION ALGORITHM(SINPLEX)

CC

CC“SET

110
100
CC

2040

CC
CC

CALL LPSOL(NROU,JCOL,ISOLTY,OBJV)

ACTIVITIES IN ASCENDING ORDER
DO 100 I-l,NROW

DO 110 J-1,JCOL
IF(INACT(I).NE.J)CO TO 110
ACT(J)-RHS(I)

GO TO 100

CONTINUE

CONTINUE

DO 2040 J-l,8
F(J)-ACT(J)
WTCHG-ACT(11)

DO 2008 I-lyNRON
IF(IRWTY(I).LE.2)TEMP(I)-E(I)-ACT(ISACOL(I))

CCCTEHP REFERS TO TEE AMMOUNT OP REQUIREMENTS DELIVERED

2008
CC

CC
2004

2002
CC

2000

2060
CC

2070

CC

IF(IRWTY(I).GT.2)TENP(I)-B(I)+ACT(ISACOL(I)+1)
CONTINUE

IF(IOPT1.LT.2)GO TO 2020

PRINT 2006

FORMAT(/)

PRINT 2002
FORMAT(1X,'**RESULTS ARE**')

PRINT 2000,ISOLTY,OBJV
FORMAT(IX,IS,3P10.3)
PRINT 2006

PRINT 2060,(ACT(J),J-1,11)
FORMAT(1X,11F7.3)

PRINT 2006

PRINT 2070,(B(I),I-1,NROU)
PRINT 2070,(TEMP(I),I-1,NROU)
FORMAT(1X,14F6.2)

PRINT 2006

VVVVVVVVVVVVVV.VVVVVVVVVVVVVVVVVVVVYVVVVVVVVVVVVVVVVVVVVVVVVVVVVVvv

157

2020 RETURN
END

CCOOOOOOO0OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

CC0000OOOOOOOOOOOOOOOOOOOOOOOOOOOOO0000000OOOOOOOOOOOOOOOOOOOOOOOOO
CC

CC
BLOCK DATA
CC
CC NUTRIENT DENSITY OF THE FEEDSTUFPS

CC CONTAINS THE NUTRIENT DENSITY OF THE FEEDS BASED ON: NARSE

CC S. ET AL."TELPLAN 31:LEAST COST RATION,"MICH.STATE UNIV.,E.

CC LANSING.1971.
CC0000OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

COMMON/NUT/A(8,9)
CC PEEDSTUPPS(COLUMNS)
cc 1-CORN
CC 2-CORN SILAGE
CC 3-ALPALPA
cc A-SOY 66
cc s-UREA
cc 6-DICAL
cc 7-LIMESTONE
cc 8-SALT

CC Rows
CC I-NEIGNT

cc z-NE

CC 3-CP

cc 6-CRUDE PIBER
CC s-CA

CC G-PHOS

CC 7-SALT

CC 8-MOISTURE

CC 9-NPN PROTEIN

DATA A/1.000,1.000,l.000,1.000,1.000,1.000,1.000,1.000,
0.950,0.700,0.640,0.870,0.000,0.000,0.000,0.000.
0.101,0.080,0.169,0.508,2.810,0.000,0.000,0.000,
0.023,0.259,0.309,0.060,0.000,0.000,0.000,0.000,
.0002,.0028,0.013,.0028,0.000,0.231,0.338,0.000,
.0026;.0020,.0020,.0064,0.000,0.186,0.000,0.000,
.00,Oo,00,00.00,00,00,100,
0.140,0.680,0.108,0.100,0.000,0.000,0.000,0.000,
0.000,0.000,0.000,0.000,2.810,0.000,0.000,0.000/

++++++++

CC

END
CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO000000
CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
CC
CC

SUBROUTINE ROWSET(I,JCOL)
CC
CC
CC THESE SUBROUTINES ARE USED IN THE BALANCE SUBROUTINE TO
CC FORMULATE A LEAST COST RATION.
CC
CCOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
CC
CC

COMMON/LP/DPM(16,36),RHS(16),ISACOL(16),INACT(16),

+ IRWTY(16),OEJ(36),ZC(36)

DOUBLE PRECISION DPM,RES,OEJ,ZC

JCOL-JCOL+1

INACT(I)-JCOL

ISACOL(I)-JCOL

DPM(I,JCOL)-1.

IP(IRNTY(I).GT.1) GO TO 202

VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV'VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV

820
830
860
850
860
870
880
890
900
910
920
930
960
950

202

160

200

102
103
106

101

600
112

113

115

120

110

165

158

OBJ(JCOL)-0.

GO TO 160

OBJ(JCOL)--(9*(10**S))

IF(IRWTY(I).EQ.2) GO TO 160

JCOL-JCOL+1

DPH(I,JCOL)--1

OBJ(JCOL)-0.

RETURN

END

SUBROUTINE LPSOL(NORON,NOCOL,ISOLTY,OBJV)

A SUBROUTINE TO SOLVE LINEAR PROGRAM PROBLEMS
DEVELOPED BY STEVE HARSN, DEPT OF AGR. ECON.,
MICE. STATE UNIV., EAST LANSING, 68826
FOLLOWS BASIC OUTLINE OF CHURCHHAN, ET. AL.

COMMON/LP/DPM(16,36),RHS(16),ISACOL(16),INACT(16),

+ IRNTY(16),OBJ(36).ZC(36)

DOUBLE PRECISION DPM,RHS,OBJ,ZC
DOUBLE PRECISION COLKEY,R,RMIN,R1,R2,X,ZCHX,Z
DIMENSION COLKEY(16)
OBJv-O.

HXITER'NORO"*6

NOITER'-1

NOITER'NOITER+1

JZCMX'O

JZCHx-O

ZCMX'O.

DO 101 J-1,NOCOL,1

Z'O.

DO 102 I-1,NOROW,1
K'INACT(I)
ZIZ+(DPH(I,J)*OEJ(K))
ZC(J)'Z-ODJ(J)
IF(EC(J))103,101,101
IF(ZC(J)-ZCMX)IO6,101,101
ZCHXIZC(J)

JZCMx-J

CONTINUE

IF(JZCMX .CT. O)Go TO 110
IF(NOITER.GT¢O) GO TO 112
ISOLTY'O

RETURN

ISOLTY'E

RETURN

XI-(9*(10**5))

DO 115 I-1,NOROW,1
J-INACT(I)
IF(RNS(I).LT..OOOI)GO TO 113
IF(OBJ(J) .EQo X)GO TO 600
IP(IRNTY(I).EO.1)GO TO 115
J-ISACOL(I)

ZC(J)'ZC(J)+X

CONTINUE

OBJv-OO

DO 120 I'1,NOROW,1
K-INACT(I)
OBJV'OEJV+(RHS(I)*ODJ(K))
ISOLTY‘I

RETURN
IP(NOITER.LT-HXITER)GO TO 165
ISOLTY'6

RETURN

ERIN-9999999999.

NKR-O

DO 150 I-I,NOROW,1

VV'VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV.VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV

CC
CC

151

155

156

160

150

169

170

176
175

10

20

30

60

50
60
70
75
120
125

136
135

139

IF(DPH(I,JZCMX))150,150,151
R-RRS(I)/DPM(I,J2CMX)
IP(R-RMIN)155,156,150

RMIN-R

NKR-I

GO TO 150

D0 160 J-1,NOCOL,1
R1-DPM(NRR,J)/DPM(NRR,J2CMX)
R2-DPM(I,J)/DPM(I,JZCMX)
IF(R2-R1)155,160,150

CONTINUE

ISOLTY-s

RETURN

CONTINUE

IP(NRR.GE.1)GO T0169

ISOLTY-3

RETURN

INACT<NRR)-JZCMx

DO 171 I-1,NORON,1
COLREY(I)-DPM(I.J2CMR)

DO 170 J-1,NOCOL,1
DPM(NKR,J)-DPM(NKR,J)/COLKEY(NKR)
RNS(NRR)-RRS(NRR)/COLREY(NRR)

DO 175 I-1,NOROU,1

IP(I .EO. NRR)GO TO 175
RHS(I)-RRS(I)-(RHS(NKR)*COLREY(I))
IP(COLREY(I) .EQ. O)GO TO 175

Do 176 J-1,NOCOL,1

IP(DPM(NRR,J) .EO. O)GO TO 176
DPM(I,J)-DPM(I,J)-(DPM(NKR,J)*COLREY(I))
IFtDPH(I,J).GT. .0000000001)GO TO 176
IP(DPM(I,J).OT. -.OOOOOOOOO1)DPM(I,J)-O.
CONTINUE

CONTINUE

GO TO 200

END

SUBROUTINE LPDUMP(NRON,NCOL)

COMMON/LP/DPM(16,36),RES(16),ISACOL(16),INACT(16),
IRNTY(16);OBJ(36),2C(36)

DOUBLE'PRECISION DPM,RRS,OBJ,zc

PORMAT(//)

PRINT 20

PORMAT(1x,'PRINT OUT OP RRs VALUES AND A(I,J) 8’)

PRINT 10

PRINT 30.NRow

PORMAT(ax,'Rows ', IS)

PRINT 40,NCOL

PORMAT(4E,'COLUMNS',I$)

PRINT 10

PRINT 50

FORMAT(IX.'RHS INPORNATION')

PRINT 60

FORMAT(1X,' NO TYPE SLACK INACT VALUE')

DO 70 I-1,NRON

PRINT 75,I,IRWTY(I),ISACOL(I),INACT(I),RES(I)
FORMAT(1X,I6,3I5,F15.6)

PRINT 10

PRINT 120

PORMAT(1X, 'OEJ EN AND LOCATION INPORHATION')
PRINT 125

FORMAT(1X,'COLUMN VALUE')

DO 130 J-1,NCOL

PRINT 135,J,OBJ(J)

FORHAT(1X,IS,2P12.3)

‘IVVV

VVVVV

VVVVVVVV

%\IV.V\IV V\IV vxrv vxrv V\IV.

1620
1630
1660
1650
1660
1670
1630
1690
1700
1710
1720
1730
1760
1750
1760
1770
20
NO
60
80
100
120
160
160
180
200
220
260
260
280
20
a0
60
80

END 0? FILE

CC
CC
CC
CC
CC
CC
CC
CC
CC

80

90

105
108
110
100

CC

. 0'
251

140

PRINT 10

PRINT 80
FORMAT(1X,'A(I,J) INFORflATION')
DO 100 IH1,NROW

PRINT 90,1
FORMAT(1X,'ROU',I5)
DO 110 J-1,NCOL
IF(DPN(I,J))105,110,105
PRINT 108,J,DPN(I,J)
FORMAT(1X,110,F15.6)
CONTINUE
CONTINUE

PRINT 10

PRINT 10

'RETURN

END
SUBROUTINE COST(PRODME,0VC)

VERSION IS: JULY 8,1978

[AMTES COSTS FOR VET AND BREEDING BASED ON

PRODUCTION LEVEL. BASED UPON S.B. NOTT, 1976, TELFARN ANALYSES

BY

PRODUCTION LEVEL FOR MICHIGAN SPECIALIZED DAIRY FARM IN

1973, AG ECON STAFF PAPER 76-36, MICE STATE UNIV.

THE "1.75" REFLECTS AN ADJUSTMENT FOR INFLATION

OVC-( (-18050+000362* (PRODHE.’091))/120)*1075

RETURN

END

SUBROUTINE REVNUE(PRODDY,REV)
REV-.0925*PRODDY*30.

RETURN

END

"11111111111115