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This is to certify that the
thesis entitled
ESTIMATION OF RESIDUAL ENERGY INTAKE
IN GROWING YOUNG BULLS AND
LACTATING COWS USING ANIMAL MODELS

presented by

FLORAH NGWERUME

has been accepted towards fulfillment
of the requirements for

M.S. degree in Animal Science

 

W

Major professor

DateW26i /7%

07639 MS U is an Affirmative Action/Equal Opportunity Institution

 

 

 

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MSU Is An Affirmative Action/Equal Opportunity institution
«Warns-pd

 

 

 

 

ESTIMATION OF RESIDUAL ENERGY INTAKE IN GROWING YOUNG BULLS AND
LACTATING COWS USING ANIMAL MODELS

BY

FLORAH NGUERUME

A THESIS

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

MASTER OF SCIENCE

Department of Animal Science

1990

ABSTRACT

ESTIMATION OF RESIDUAL ENERGY INTAKE IN GROWING YOUNG BULLS
AND LACTATING COWS USING ANIMAL MODELS
BY

FLORAH NGWERUME

Residual energy intake (REI) is defined as total net energy intake
minus the predicted total energy requirements in a production period.
The idea of REI as a measure of energetic efficiency is that the more
the amount of energy that can be accounted for, or the smaller the RBI,
the more efficient is the animal in utilizing feed energy.

Using animal models, REI was estimated for growing young bulls and
lactating cows separately. In both instances, average daily NEI in a
production period was used as a depended variable to fit an animal
model that contained covariates of production, maintenance and weight
change. REI was computed as sum of animal and residual effects.

Animal and residual variance components were estimated by the EM-REML
procedure.

For both growing bulls and lactating cows, RBI had a small
heritability value. The proportion of phenotypic variation in NEI due
to REI was relatively small for the young bulls but high for lactating

COWS .

ACKNOWLEDGMENTS

The author expresses her sincere appreciation to Dr. Ivan L. Mao,
my academic advisor for his advice, encouragement of independent
thinking, and unselfish contribution of support and time during this
graduate program.

I thank Dr. Ted Ferris, Dr. Roy Emery and Dr. Kim Wilson, the
members of my graduate committee for their help and support.

I express sincere gratitude to Dr. Just Jensen for use of his
program. His counsel and willingness to convey his knowledge of
animal breeding which was most helpful in this is greatly appreciated.

Supply of data by Eli Lilly Research Laboratories, USDA, and the
Danish Research Centre Folum in Denmark is greatly acknowledged.

My sincere appreciation to my good friends who have been a source
of inspiration during my study. In particular, Daniel, Terri,
Gwang-Joo, Gustavo, Paul and Joe: they have all made the hard times a
little easier to get through.

Many thanks to Sherily Hulet for her outstanding technical support
in the preparation of this thesis manuscript.

Finally, deepest thanks to my family and relatives for their
unwaivering support and understanding during this period of graduate

study.

ii

TABLE OF CONTENTS

Page
LIST OF TABLES ............................................. v
LIST OF FIGURES ............................................ vii
1. Introduction ........................................... 1
2. Objectives ............................................. 2
2.1 Growing young bulls .............................. 2
2.2 Lactating cows ................................... 3
3. Review of Literature ................................... 4
3.1 Introduction ...................................... 4
3.2 Overview of Efficiency Measures ................... 4
3.2.1 Dairy cattle ................................ 4
3.2.2 Beef cattle ................................. 8
3.3 Feed intake ....................................... 12
3.4 Digestion and absorption .......................... 14
3.5 Partitioning of energy to useful product .......... 15
3.5.1 Energy requirements for maintenance ........ 15
3.5.1.1 Sources of variation .............. 18
3.5.2 Energy utilization for growth .............. 20
3.5.3 Energy utilization for weight change in
support of lactation ....................... 24
3.5.4 Energy utilization for pregnancy ........... 27
3.5.5 Energy utilization for lactation ........... 28
4. Estimation of Residual Energy Intake in Growing Young
Bulls using an Animal Model ............................ 33
4.1 Abstract .......................................... 34
4.2 Introduction ...................................... 34
4.3 Materials and Methods ............................. 37
4.3.1 Experiment ................................. 37
4.3.2 Data ....................................... 38
4.4.3 Model ...................................... 38
4.4 Results and Discussion ............................ 40
4.4.1 Partial energy requirements for growth ..... 41
4.4.2 Maintenance energy requirements ............ 42
4.4.3 Residual energy intake ..................... 43
4.5 Conclusions ....................................... 44

iii

Estimation of Residual Energy Intake in Lactating Cows
using an Animal Model ..................................

5.1 Abstract ..........................................
5.2 Introduction ......................................
5.3 Materials and Methods .............................
5.3.1 Data .......................................
5.3.2 Model ......................................
5.4 Results and Discussion ............................
5.4.1 Partial energy requirements for SCM ........
5.4.2 Maintenance energy requirements ............
5.4.3 Energy requirements for weight change ......
5.4.4 Residual Energy intake .....................
5.5 Conclusions .......................................
Summary ................................................
6.1 Growing young bulls ...............................
6.2 Lactating cows ....................................
Appendices .............................................
List of references .....................................

iv

45

46
46
49
49
50
51
51
52
53
54
54

56

56
57

59

72

Table

I.

10.

11.

12.

13.

14.

1.

LIST OF TABLES

Heritabilities of feed efficiency of milk yield ........

Genetic relationships between feed intake, feed
efficiency and milk yield ( Hooven et al., 1972) .......

Estimates of ME required for maintenance of various
breeds or breed crosses ( Ferrel & Jenkins, 1895) ......

Estimated maintenance requirements of various types of
cattle .................................................

Composition of gain of ad libitum fed steers and
heifers (Garrett, 1970) ................................

Regression of liveweight change on milk yield measured
at various stages of lactation (Broster et al., 1975)..

Example of animal differences in nutrient partitioning
(Bauman et al., 1984; Walter & Mao, 1989) ..............

Partial energy requirements for milk production (Moe
et al., 1970) ..........................................

Mean, SE, SD, CV for net energy intake, daily gain and
metabolic body weight ..................................

Partial energy requirements for growth and maintenance.

Estimate of additive genetic and phenotypic SD and
heritability values for residual energy intake ........

Simple statistics of variables used in analysis .......

Partial net energy requirements for weight change,
maintenance and production of SCM .....................

Estimates of additive genetic and phenotypic SD and
heritability values for residual energy intake ........

Energy composition of the diets during the entire
experiment ...........................................
v

Page

6

13

19

23

26

31

31

4O

43

44

51

54

54

6O

II.

II.

II.

III.

III.

III.

III.

III.

Distribution of records across the years and treatments

Basic statistics on net energy intake, daily gain, BW
and metabolic BW ......................................

Estimates of year by treatments subclasses ............

Estimates of initial age at beginning of young bulls at
beginning of experiment ...............................

Basic statistics of variables analyzed by a sire model
Partial NE requirements for growth and maintenance....

Estimates of additive genetic, phenotypic SD and
heritability values for residual energy intake ........

Distribution of cows in different parities ............
Number of cows in different herd season subclasses....
Simple statistics of variables in analysis ............
Estimates of fixed effects of parity ..................

Estimates of herd-year season subclasses ..............

vi

61

62

65

66

68

68

68

69

69

7O

71

71

LIST OF FIGURES

Figure
l. Utilization of nutrients and performance of dairy cattle in
relation to genetic state and constraints (Korver, 1987)...
2. Path diagram of the phenotypic inputs and the indirect
relationships between these inputs with body size
(Blake & Custodio, 1984) ...................................
3. Milk production system of the dairy cow (Blake & Custodio,

1984) ......................................................

vii

Page

5

1. INTRODUCTION

Cattle produce high quality protein as milk and meat. They can
utilize non-protein nitrogen and many materials not digestible by man
or simple stomached animals. However improving efficiency of
converting feed to milk or meat cannot be overemphasized. This concept
of feed efficiency should be an important goal for the cattle industry.

The efficiency with which a growing beef animal converts feed it
eats into meat or the efficiency with which a dairy cow convert feed
into milk for consumption by man is determined overwhelmingly by the
efficiency with which it uses the major nutrient which is energy. A
complete desciption of energy efficiency requires simultaneous
consideration of ingested feedstuffs, conversion of feed into energy in
the form of Adenosine Tryphosphate (ATP), partitioning of energy into
various forms of production, conversion of energy to products and
output of measurable products. Traditional measures of efficiency such
as feed conversion ratio are closely related to production. However
several workers have discussed many aspects of measuring feed
efficiency and concluded that efficiencies of energy utilization for
meat or milk production have not been influenced by selection for milk
production or growth rate. Since supply of feed energy is a major cost
in any cattle production system, alternative measures of feed energy
efficiency that account for nutrient absorption, rate of basal

metabolism and the energy utilization for the processes of growth or

milk production should be explored.

Among many alternative measures of energetic efficiency is the
concept of residual energy efficiency or residual feed intake. This
concept is best explained by an understanding of utilization of
metabolizable energy. The total intake of metabolizable energy can be
partitioned into productive (growth and milk production), and non-
productive (maintenance and efficiency of the digestive tract) use of
energy. These are collectively known as partial energy requirements.
The remaining energy after accounting for all the identifiable partial
energy requirements is called residual energy efficiency. A measure of
residual energy intake for individual animals would thus reveal the
differences between animals in utilizing metabolizable energy for
production and maintenance and a lower residual energy intake would

imply greater energetic efficiency.

2. OBJECTIVES

2.1 Growing Young Bulls

Using data from growing young bulls the objectives of the study
were to 1) estimate the partial net energy requirements for growth in
terms of daily gain and for maintenance as a function of metabolic body
weight; 2) estimate residual energy intake for each animal and; 3)

estimate genetic parameters for residual energy intake.

2.2 Lactating Cows

Using data from lactating cows, the objectives of the study were,
to 1) estimate the partial net energy requirements for production of
solids corrected milk, maintenance in terms of metabolic body weight,
and for weight change in a lactation; 2) estimate residual energy
intake for each animal and; 3) estimate the genetic parameters for

residual energy intake.

3. REVIEW OF LITERATURE

3.1 Introduction

Supply of feed energy constitutes the largest item of expense in
beef and milk production enterprises. It is therefore surprising that
so little attention has been paid to feed costs in cattle breeding.
Selection in dairy cattle has been mainly confined to milk production
traits and genetic increases in these traits have resulted. Similarly
in beef cattle when selecting animals for meat production it is
important to consider those that grow at the shortest possible time and
at least costs (Webster, 1977). Therefore, selection in beef breeds
has been confined to high growth rates. As such the question is, is it
worthwhile to include nutrient intake and utilization in the breeding
goal or more specifically what are the traits, their relative
importance and genetic parameters. The lack of information about the
genetic aspects of nutrient intake and utilization is caused by the
technical impossibility (labor and equipment) of measuring feed intake
on individual animal basis under normal conditions.

Brelin and Branning (1986), Buttazzoni and Mac (1988) and Walter
and Mao (1989) postulated that one way of reducing feed costs in the
long term is to breed for better efficiency. However, for information
on whether feed control is an urgent step in the practical work with
cattle we need to know about the genetic variation in feed efficiency
and its relationship with other economic traits such as, milk

production, growth rate, carcass traits and metabolic efficiency.

An overview of efficiency measures, and the biological components of
feed efficiency namely feed intake, digestion and absorption and
partitioning of energy to useful product will be reviewed.
3.2 Overview of Efficiency Measures
3.2.1 Dairy Cattle

For lactating dairy cows gross feed efficiency is usually defined
as the ratio of milk output over feed input or its inverse in
biological or economic terms (Bauman et al., 1984). Knowledge of the
biological component of efficiency is necessary before considering the
economic ones. Feed efficiency in dairy cattle is influenced by diet
and other environmental factors, genetic ability and physiological
state of the cow to utilize nutrients for milk. This total biological

complex as described by Korver (1988) is shown in Figure 1.

 

GENETIC AND
PHYSIOLOGICAL STATE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MAINTENANCE , FEED INTAKE
GROWTH FEED RESOURCES DIGESTION
LACTATION ENVIRONMENT - ~
REPRODUCTION A
DESIRED NUTRIENTs
NUTRIENTS - . - AVAILABLE
- , PARTITION 0F NUTRIENTs - A .
l_ \_ UTILIZATION
,» 1 PERFORMANCE

 

Figure l. Utilization of nutrients and performance of dairy cattle in
relation to genetic state and constraints. (Korver, 1988)

Heritability estimates for gross feed efficiencies are shown in
Table 1. The data indicate a heritability between 0.4 and 0.6. The
measurements of output and input traits differ between studies but all
were an attempt to measure a ratio for energy input to energy output.
The literature therefore indicates a clear genetic component for gross

efficiency.

TABLE 1. Heritabilitiea of feed efficiency of milk yield.

 

 

Trait Heritability SE Source

GROSS ENERGY EFFICIENCY

FCM/FU 0.48 0.07 Mason et a1. (1957)

Intake (TDN)/FCM 0.63 0.09 Hickman & Bowden (1967)
FCM/DE 0.47 0.23 Hooven et al. (1972)
Milk/DE 0.47 0.23 Lamb et al. (1977)

NET ENERGY EFFICIENCY

SCM/NEL 0.32 0.37 Buttazzoni & Mao (1988):STA
SCM/NEL 0.49 0.36 Buttazzoni & Mao (1988):MTA

 

FU - Feed Units, FCM - Fat Corrected Milk, ENE - Estimated Net

Energy, DE - Digestible Energy, SCM - Solids Corrected Milk, NEL

- Net Energy for Lactation, STA - Single trait analysis, MTA - Multiple
trait analysis.

The breeding goal in dairy cows is centered on productive
efficiency and the question arises: Is it worthwhile to measure this
trait on an individual basis and to incorporate it in a breeding
program in addition to milk yield? Freeman (1967) pointed that direct
measurements of efficiency under commercial conditions in large numbers
of herds does not seem economically feasible. His conclusion was

selection for higher milk yield automatically improves gross feed

efficiency and this indirect selection accounts for 70 to 95% of direct
selection when selection intensities are equal for the two traits.
Blake and Custodio (1984), extensively reviewed the more recent
literature about the correlation between milk yield and feed efficiency
and showed a range for the genetic correlation between 0.88 and 0.95.
while the phenotypic correlation ranged from 0.60 to 0.95.

Experimental procedures may have inflated the values of these
correlations. However, feed efficiency computed as a simple ratio
between input and output has been shown to have its limitations.

All of the genotypic and most of the phenotypic correlation were
calculated by feeding concentrates or grain according to milk yield,
thus forcing a high correlation with efficiency (Blake and Custodio,
1984, Korver, 1988). Also in dairy cattle feed efficiency is normally
defined as energy in milk divided by energy intake. However, Blake and
Custodio (1984) pointed out that milk production is dependent on two
inputs namely feed intake which is a function of appetite and body

tissue losses/gains and indirectly related to body size as shown in

 

Figure 2.
F, FEED INTAKE a
T ‘L MILK YIELD
TISSUE LOSS /
0R GAIN
BODY SIZE

 

Figure 2. Path diagram of the phenotypic inputs (appetite and body
tissue losses to milk yield and the indirect (correlated) relationships
between these inputs and with body size. (Blake & Custodio, 1984).

From the above relationship, Blake and Custodio (1984) pointed out
that the amount of milk production is the sum of feed consumption times
the digestibility coefficient plus the tissue available for catabolism
times rate of catabolism. These workers went on to define efficiency
as thus the rate of converting dietary nutrients to milk after
adjusting for nutrients supplied by catabolism (e.g. negative energy
balance or nutrients to replenish tissue reserves). It is apparent
from the discussion by Blake and Custodio (1984) that gross feed
efficiency is an imprecise measure of production efficiencies since it
does not account for the rates of catabolism or anabolism of tissue or
protein energy. As such, Blake and Custodio (1984), illustrated
diagramatically (Figure 3) the possible relationship of the components
of milk feed efficiency that should be considered when measuring this
trait.

3.2.2 Beef Cattle

Differences in converting feed into body tissue are important in
determining income from beef cattle operations. However, measuring
feed consumption is costly because of increased labor requirements
(Koch et al., 1963). In beef cattle, gross feed efficiency is defined
as a ratio between feed intake and weight gain in a given interval of
growth (Brelin and Martinsson, 1986). Feed efficiency calculated this
way has been found to have a high genetic correlation with growth
ranging from -0.60 to -0.95 and heritability values ranging from 0.3 to
0.6 (Brelin and Brannang, 1982; Brelin and Martinsson, 1986). However,
as in dairy cattle, feed efficiency calculated this way is a complex

measurement including a number of interacting factors.

 

 

 

 

 

 

 

 

 

.AGE

 

DAY OF LACTATION

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

K fiL—I

 

I I

 

‘——I MILK

 

7 YIELD

 

 

 

 

r.
I ?» BODY e
| SIZE
l T
I ——fi
I Tissue Balance
' I REPLETION
I ' V ‘1' I I
APPETITE

(FEED INTAKE) CATABOLISM
| A 4}
I ———j

 

 

 

 

 

 

 

 

Figure 3. Milk production system of the dairy cow.

 

 

/ i.
. ‘ r‘ _ I ‘I. ’ I
v Ls M ’- ’ ’ /

\ ‘JAP-{q’

box represent phenotypic and environmental relationships (closed arrows) and
associations among phenotypes (G, Open arrows) lie outside. 85 - body 5
appetite. TB - tissue balance, M - milk yield.

(Blake and Custudio.

ize.

1934).

Elements within the broken

.19
ou‘

0.

~

l’ne

10

Meyer and Garret (1967) pointed out that gross feed efficiency
assumes that all feed consumed is utilized for body gain and ignores
maintenance requirements and other uses of energy. It also assumes
that the origin passes through zero when gain is graphically plotted
against feed consumption. Weil (1962) stated that to use a ratio for
expressing information about two quantities, these three conditions
must be met: (1) the two items considered should increase or decrease
linearly, (2) the regression for the two variables should intercept at
zero and (3) the variance of the variables should increase with
increasing magnitude of the variables. Koch et al (1963) discussed the
problems of gain to feed ratios for measuring the efficiency of feed
use by beef cattle where the primary interest was in animal breeding.
They reported that variation in the composition of gains (fat, lean or
bone) and in the maintenance requirements prevents gross feed
efficiency from being a precise measure of energy conversion rate.
Karlsson (1979) also pointed that due to the strong genetic
correlation between growth rate and mature size, fast growing animals
will on average be less mature and therefore leaner at a given
liveweight thus giving rise to better feed efficiency. The composition
of growth regarding fat and other tissues also affects feed efficiency,
as the feed energy required for a unit weight gain of feed is
considerably larger than for other tissues (Orskov and McDonald, 1970;
Fuller and Webster, 1977; Geay, 1984 and Brelin and Martinsson, 1986).

It is therefore apparent from the reports of several workers above
that gross feed efficiency is an imprecise measure bf feed efficiency

both in dairy and beef cattle. As such more accurate alternative

11

measures of efficiency should be defined. Buttazzoni and Mao (1988)
suggested use of net energy efficiency for dairy cows. This was defined
as the ratio of energy contained in milk over the portion of energy
intake used to produce it above maintenance requirements. They pointed
that net efficiency is a better biological indicator of a cow's
productivity than gross efficiency because it measures the efficiency
Of energy required for maintenance and for changes in body reserve
status. Buttazzoni and Mao (1988) estimated heritability values of 0.32
and 0.49 under single and multiple trait analysis respectively for net
energy efficiency for milk yield(NEL). The genetic correlation between
milk yield and NEL was 0.56. Koch et a1 (1963) suggested that the most
useful criterion for evaluating efficiency of feed use in beef animals
may be the amount of edible product produced for a given energy rather
than the fraction of energy in the feed which was converted to animal
tissue.

Residual energy intake (REI) has also been suggested as an
appropriate measure of feed efficiency (Koch et a1 1963, Brelin and
Brannang, 1982; Luiting, 1987; Korver,1988 and Jensen, 1989). REI is
defined as actual energy intake minus predicted energy requirements in
a production period and is thus independent of production. As such a
lower REI would indicate a better efficiency since most of the energy
would be partitioned into desired uses of energy. Korver (1988) pointed
that REI reflects differences in animals in utilizing net energy for
maintenance and production. REI has been reported moderately
heritable. Koch et a1 (1963) found a heritability value of .28 in

young bulls and Korver (1987) estimated a value of .25 in heifers from

12

AI bulls. In poultry values ranged from .25 to .40 (Luiting, 1987).

In order to improve produCtive efficiency genetically, it is
therefore of paramount importance to clearly specify the concept of
feed efficiency, partly to obtain a biologically more uniform measure
and partly because growth rate and milk production are already taken
into consideration in other ways in the breeding process. The
biological components of efficiency include feed intake, digestion and
absorption, utilization of metabolizable energy for production and
maintenance.

3.3 Feed Intake

In the first part of lactation nutrient intake especially of
energy and protein, does not meet requirements of a high-yielding cow.
Negative energy balance can be reduced by increasing density of the
diet through a higher proportion of concentrates (Korver, 1988).

Gravert (1985) (Table 2) showed results of 32 sets of monozygous
twins with 96 lactations. Cows were fed a mixed ration of roughage and
concentrates ad libitum. Heritability of energy intake during the
initial 20 weeks of lactation was 0.16 and its genetic correlation with
milk production was only 0.12. Results suggested that selection on
milk yield would not automatically increase feed intake of dairy cows
in the first part of lactation. Hooven et al., (1972) found the
correlation for 31-60 days lactation of 0.52 and heritability was 0.24
and found an estimate of 0.86 in the second part of 1actation.(Table
2). These results clearly reflect the differences between different
parts of lactation.

Performance testing of AI bulls provides the possibility for

13

selection on feed intake in a breeding program. Korver et al. (1987)
reported on the results of 202 young heifers 24 sires fed roughage ad
libitum. They found heritability for roughage intake (dry matter per
day) of 0.55 and for intake per unit metabolic weight of 0.17. The
phenotypic variation was 0.66 kg DM/day, respectively. Corresponding
results on 127 lactating heifers fed a roughage ration fed ad libitum
supplemented with a fixed amount of concentrate was 0.8 kg DM/day. The
genetic relationships between feed intake in young AI bulls and feed
intake of female progeny is not yet known.

Table 2. Genetic relationships (including heritabilities, underlined)

between feed intake, feed efficiency and milk yield.
(Hooven et al., 1972).

 

 

FCM GFE FI

31-60 days lactation

FCM yield (FCM) 0,43 0.89 O.52(O.12)a

Gross Feed Efficiency (GFE) 0.44 0.07

Feed Intake (FI) 0.24(0.16)a
121-150 days lactation

FCM 0,56 0.93 0.86

GFE Qlié 0.61

FI 9,26

 

aGravert (1985)

Several studies have focused on between breed variation in feed
intake and feed conversion. Oldenbrook (1984) observed in experiments
. with Holstein Friesian (HF), Dutch Friesian (DF) and Dutch Red and

White (DRW) cows, that the largest differences in dry matter intake was

14

between HF and DRW (on average 8%) in favor of HF. In his further
studies when Jerseys were involved, Oldenbrook (1986) showed a
significant breed-ration interaction when comparing Friesians and
Jerseys on a completely mixed high and low-concentrate diet. Jerseys
were favorable on a low concentrate ration and Friesians on the high
concentrate ration. Richardson et a1. (1971) also reported that
efficiency on a lactation basis showed a significant genotype-ration
interaction which might be the result of the level of concentrates
since the experimental Jersey heifers were fed concentrates according
to milk production. Korver (1982) and Korver (1988) showed results of
a comparison of Friesians under feeding strategies with a fixed
concentrate level or concentrate fed according to milk yield with
roughage ad libitum in both cases. The coefficient of variation was 9
and 15%, respectively. These results indicate more fully expression of
genetic differences when diets are altered according to expected
requirements. Feeding levels therefore account for the main part of
the differences in production between herds. These studies also make
apparent the importance of genotype by feeding level and that feeding
by strategy interaction represent scale effects.
3.4 Digestion and Absorption

Very few attempts have been made to determine the heritability of
digestibility within different types of cattle. Van Es (1961) cited by
Korver (1988) showed that little variation exists among cows in their
ability to digest a given diet. Grieve et al. (1976) and Custodio et
al. (1983) examined the relationship between estimated transmitting

ability for milk production and digestibility of dietary components in

15

Holstein cows. Both studies concluded that transmitting ability and
digestibility were not correlated when dairy cows were fed a mixed diet
ad libitum. Davey al., (1983) found similar results when he compared
two genetically different groups of Holstein cows.

In a literature review Warwick and Cobb (1976) reported
differences in dry matter digestibility between Bos taurus cattle and
808 indicus cattle, but the differences were too small and inconsistent
and of doubtful practical significance.

Walter (1986) examined the effect of varying the rate of energy
absorption as well as the amount of residual milk on milk energy
utilization. Using various half lives of energy absorption (0 to 1.5
days) very little effect was found on energy input and energy
utilization.

3.5 Partitioning of Energy to Useful Product

For growing young animals selected for meat production, there are
essentially two main uses of metabolizable energy namely growth and
maintenance. For lactating, cows there are typically four major uses of
energy which have been categorized: maintenance, lactation, pregnancy
and weight change. Energy utilization for each of these forms of
output will be reviewed.

3.5.1 Energy Requirements for Maintenance

Maintenance energy requirements consist of energy necessary for
basal metabolism, conduct of voluntary body activity and generation of
heat necessary to maintain body temperature (Milligan and Summers,
1986; Korver 1988). For the producing female maintenance requirements

constitute a major proportion of the energy requirements for cattle

16

production (Ferrel and Jenkins, 1985). Considerable variation exists
among animals in the maintenance requirement per unit metabolic body
size when comparisons are made across a range of species and ages.

Several investigations have been made to estimate energy
requirements for maintenance with either lactating or dry cows.
Hashizume et a1. (1965) found an estimate of 116 kcal ME/Rg3/4 for
lactating cows. Van Es and Nijkamp (1969) concluded that 96 to 111
kcal of ME/kg3/4 were required for the maintenance of a 500 kg
lactating cow. Moe et al. (1970) estimated maintenance requirements
from 350 energy balance trials with lactating cows to be 122.1 and
111.3 kcal/kg of metabolic body weight. Data reported by Ferrel et
a1 (1976) indicated the maintenance of pregnant and non pregnant
heifers to be similar. However data reported by Moe et a1 (1970)
indicated that the maintenance requirements of lactating Holstein cows
were considerably greater than the maintenance requirements for dry
cows.

Walter and Mac (1989) estimated the proportion of ME intake utilized
for maintenance by lactating Holstein cows. They reported negligible
differences in energy used for maintenance during negative or positive
energy balance ( 20.81 and 19.95% respectively). During the whole
lactation period, 20.11% of ME intake was used to meet energy
requirements for maintenance. Saama et al. (1990) did a similar study
using data from energy chamber experiments. On average, 25% was of ME
intake was used for maintenance of lactating Holstein cows.

Energy requirements for maintenance by lactating beef cows have

also been evaluated by several workers. Garret (1974) cited by Ferrel

17

and Jenkins (1985) obtained an estimate of 112 kcal ME/kg3/4 per day
for nonpregnant nonlacting cows. Similar estimates were obtained by
Klosterman et a1. (1968) for dry nonpregnant Hereford or Charolais
cows.

Neither Klosterman et al. (1968) nor Russel and Wright (1983)
observed differences in maintenance requirements between cow types but
reported that maintenance decreased with increased body condition of
beef cows. Ferrel and Jenkins (1985) reported data on a series of their
experiments with different breeds and breed crosses to evaluate the
influence of cow type on maintenance requirements during different
physiological states (Table 3). From these data Ferrel and Jenkins
concluded that the genetic potential for milk production was positively
related to maintenance energy requirements and physiological state of
the cow.

Numerous investigations have evaluated the influence of cattle
breed or breed cross on energy requirements for maintenance for
selected beef and dual purpose steers or bulls. Blaxter and Wainman
(1965) found lower basal metabolism for steers of beef breeds than for
dairy breeds.

Garret (1969) observed lower maintenance requirement for Hereford
steers than for Holstein steers. Frisch and Vercoe (1977) reported
that cattle of B05 indicus had a lower basal metabolism than cattle of
Bos taurus. Table 4 shows some of the results of estimates of
maintenance requirements for beef steers or bulls of different breeds.
These data indicate a substantial amount of difference in energy

requirements for maintenance among cattle germ plasm.

18

Table 3. Estimates of ME Required for Maintenance of Various Breeds
or Breed Crosses ( Ferrel and Jenkins, 1985).

 

 

Breed or
Breed Cross Physiological Maintengyze
State Kcal/Kg
Angus-Hereford nonpregnant nonlactating 130
Charolais X " 129
Jersey X " 145
Simmental X " 160
Angus nonpregnant lactating 149
Hereford " 141
Simmental " 166
Charolais " 165
Angus nonpregnant nonlactating 115
Hereford " 120
Simmental " 134
Angus pregnant lactating 151
Red Poll " 157
Brown Swiss " 156

 

3.5.1.1 Sources of Variation

Most of the estimates indicate that ME requirements for
maintenance vary among cow types and also suggest that variation in
maintenance exists beyond that associated with size or milk production
potential.

Energy requirements for maintenance may differ among animals
differing in genetic potential for production. Andersen (1980) and
Webster (1980) showed differences due to sex. Close (1982) reported
differences due to temperature in his studies with pigs. Blaxter and

Boyne (1982 cited by Ferrel and Jenkins 1975) also showed differences

19

due to season. Van Es (1980) and Geay (1984) reported variation in

maintenance energy requirements with liveweight of the animals. Geay

20

Table 4. Estimated Maintenance Requirements of Various Types of Cattle.

 

Breed Maintenance

 

Vercoe (1970)

Garrett (1971)

Chestnutt (1975)

Frisch & Vercoe (1976)

Vermorel et al (1976)

Andersen (1980)

Byers & Rompala (1980)

Van Es (1980)

Byers (1982)

Truscott et a1 (1983)

Brahman x British steers, 327 kg
Shorthorn x Hereford steers, 290 kg

Hereford steers, 240-410 kg
Holstein steers (DP), 230-390 kg

Angus steers, 220-500 kg

Hereford x Friesian steers (DP),
210-500 kg

Friesian steers (DP), 180-500 kg

Brahman x British steers, 230 kg
Africander x British Steers, 230 kg
Hereford x shorthorn steers, 232 kg

Charolais bulls, 16mo.
Friesian bulls (DP), l6mo

Simmental x bulls, 300-500 kg
Charolais X bulls, 300-550 kg
Hereford X bulls, 300-550 kg
Black & white X danish bulls, 320-560 kg
Angus x Danish bulls, 320-560 kg
Charolais x Danish bulls, 320-560 kg
Brown Swiss x Danish bulls (DP),

320-560 kg
Red Danish x Danish Bulls, 320-560 kg

Charolais Steers, 260-544 kg
Simmental x Angus steers, 190-402 kg

Hereford x Friesian steers (DP), 250 kg

" , 450 kg
Hereford x Friesian bulls , ” 250 kg
" " , " 450 kg

Red Angus steers, 219-453 kg
Simmental Steers,

Hereford steers, 141-487 kg
Friesian steers (DP), 166-568 kg

102°
112°

106°
115°

142°

130°
161°

73. 5b
85. 7b
84.3°

100°
113°

114°
109°
102°
94°
95°
99°

92°
93°

161°
122°

124°
117°
145°
137°

115°
115°

145°
153°

 

akcal 75
bs/ks/yg

ckcal/kg' 73

DP - Dual Purpose

21

(1980) observed that ME requirements for maintenance declined by 3.0
Kcal/kg3/4 for each increase in 100kg body weight with Charolais bulls,
by 10.5 Kcal/kg3/4 with Friesian bulls.

Body composition differences among breeds, sexes or ages of cattle
have led many investigators to suggest variation in body composition to
be the primary source of variation in fasting or maintenance energy
expenditure. Graham et al., (1967), Graham et a1. (1974) and Ferrel et
al., (1979) showed maintenance expenditure to be highly correlated with
body lean or protein mass and lowly correlated with body fat. Pullar
and Webster (1974) found that lean rats had a higher fasting heat
production than genetically obese rats. However, other data indicate
that body composition may not be the predominant factor in determining
maintenance requirements. Garret (1980) indicated no differences in
maintenance of steers and heifers although steers contained greater
amounts of lean tissue than heifers at similar weights. Different body
composition and fasting heat production values for genetically lean,
obese and contemporary Hampshire x Large white pigs were observed among
the lines of pigs (Tess et al., 1984). However adjustment of the
fasting heat production data to equal body size and composition did not
remove differences among the lines.

3.5.2 Energy Utilization for Growth

The energy requirements for growth are generally thought to be
depended on the composition of growth (Webster, 1980 Garret, 1980
Andersen et al., 1980 and Geay, 1984). In his study Garret (1980)
determined fasting heat production in trials involving 708 steers and

341 heifers. The energy value of gain in mJ/kg of the British breed

22

steers (23.4) was 11% higher than for the heifers (28.1) and 35% lower
than a small sample of Charolais steers (14.7). Although he did not
study the composition of gain he pointed that the Charolais steers had
gained more protein than fat since protein is produced with less
efficiency than fat in growing ruminants.

Byers and Rompala (1980) measured composition of empty body growth
at intervals during growth of eight Charolais and eight Angus x
Simmental steers (AxS). For the AxS steers efficiency of energy use for
growth was 62.0% and was 44.8% for the Charolais steers. The increased
proportion of protein in the gain of Charolais steers was probably a
factor in the lower efficiency of energy use for gain of Charolais when
compared with AxS steers. They also observed that within each group of
animals the efficiency of use of energy for growth was positively
related to the fraction of body gain as fat. Byers and Rompala further
on reported that increasing rate of gain from 1.0 to 1.5 kg/day with
either Charolais or AxS steers resulted in very little additional
protein gain but rates of fat deposition increased rapidly with
increasing increasing rate of gain from 1.0 to 1.5 kg/day. These
findings seem to suggest that efficiency of energy utilization for
protein deposition is lower than that of fat. This hypothesis has been
supported by many workers (Orskov and Mcdonald, 1970; Pullar and
Webster, 1977; Webster, 1977 and Geay, 1984).

In their study with lean and congenitally obese rats, Pullar and
Webster (1977) observed values of 0.45 and 0.75 for energy efficiencies
of protein and fat deposition respectively. Orskov and McDonald (1970),

in their study with lambs, found ME requirements for protein and fat

23

deposition to be 16.25 kcal/g and 11.44 kcal/g respectively. Geay
(1984), based on data from 52 experiments in his review of literature
gave preferred values of 0.20 and 0.75 for protein and fat accretion
respectively on energy basis.

Energetic efficiency of growth has been found to vary with diet.
Geay and Robelin (1980) examined the influence of energy content of the
diet on energy utilization for growth in bulls and heifers. Diets used
in this study were long grass hay or dehydrated lucerne with two
different proportions of concentrates. It was observed through
slaughter techniques that for the same ME intake, the daily gain of
liveweight protein and energy was lowest for diets with least
metabolizability but the decrease was less in bulls than heifers. These
observations were attributed to the efficiency of ME utilization for
fat deposition that drops when crude fiber content of the diet
increases or when its metabolizability decreases.

Orskov and McDonald (1970) used barley and soybean meal diets with
varied crude protein concentrations of approximately 10, 12.5, 15.0,
17.5 and 20.0%. Three feeding levels 100, 85 and 70% of estimated
maximum voluntary feed intake were used. The total deposition of
protein was not significantly affected by feeding level but rose
significantly with increasing protein level in the feed. Total
deposition of fat decreased with increasing protein concentrations in
the feed. They also observed that total fat deposition decreased with
feeding level. Menke (1980) proposed a curvilinear function for the
description of the relationship between metabolizable energy intake and

retention of energy as protein which permits the calculation of the

24

maximum potential energy retention in protein deposition at different
feeding levels. Menke (1980) also pointed that a function for
prediction of protein must include the effects of diet composition
(protein:energy ratio and amino acid pattern) and feed intake as well
as effects of endogenous factors of age, sex and breed of the animal.
Tyrell and Moe (1980) performed 59 energy balance measures on eight
Hereford heifers during the fattening period. They found that partial
efficiency of use of metabolizable energy for tissue gain was not
affected by diet.

Garret (1970) found that sex had a significant effect on the energy
required for growth . A high quantity of energy was required per unit
weight of gain in the heifers as compared to the steers. Their results

at different ages of the animals are shown on the Table below.

Table 5. Composition of gain of ad lib fed steers and heifers (Garret,
1970)

 

 

Period(days) 0-98 98-196 196-294

Sex Steers Heifers Steers Heifers Steers Heifers
Fat% 35.30 47.10 48.50 55.60 60.00 69.70
Protein% 14.50 12.20 11.90 10.50 9.60 7.70
NE mJ/kg 4.12 5.12 5.24 5.83 6.21 7.02

 

Some estimates of energy requirements for growth have been
reported. Tyrrel et al (1974) found that six Hereford heifers required
an average of 1.95 Mcal to deposit one Mcal of tissue for gain. For

each additional one Mcal in daily rate of growth they observed 95% of

25

it was retained as fat while only 5% as protein. Jensen (1989) using

data from Holstein-Friesian and Brown Swiss growing young bulls found
estimates of 9.26 mJ/kg of gain for bulls from 14 days to 200 kg and

23.0 mJ/kg of bulls between 200 kg and slaughter weight.

From these observation it is true that fat deposition increases
with age and the reverse is true for protein. Also as fat increases the
net energy required for a kg of gain increases.

In summary growth is a confusing process to evaluate because
everything is changing at once. However, from the above studies it is
apparent that relative growth rate declines throughout from birth to
maturity. The proportion of fat relative to protein deposited in the
growing animal increases progressively as the animal proceeds to
maturity. The amount of food energy required to maintain energy balance
increases throughout growth. All these factors combine to reduce
throughout growth the net efficiency with which food is converted to
body tissue and reduce even more the efficiency with which it is
converted to protein or lean tissue.

3.5.3 Energy Utilization for Weight Change in Support of Lactation

The catabolism and metabolism of tissue in support of lactation is
different from growth for it involves primarily fat tissue with little
or no protein in many circumstances (Walter, 1986). The basis of
liveweight change in lactating animals has been documented by Moe et a1
(1971). Intense selection of dairy cattle for high milk has resulted in
a situation in which the genetic ability to produce milk during early
lactation exceeds the ability of the animal to ingest sufficient feed

to meet requirements for energy (Moe et al., 1971). Because production

26

of milk during early lactation has a high priority in the dairy cow,
production of milk may continue high despite insufficient energy
intake. When this situation exists the cow must draw upon body tissue
reserves to provide the energy which is lacking in the diet. This
situation will end as soon as body energy reserves have been exhausted
thus resulting in body weight loss. However to re-establish the animal
to the same condition as at parturition, the animal must be allowed
sufficient feed intake for body energy deposition either during late
lactation or during the dry period.

Flatt et a1. (1970) described a cow Lorna which was given a diet
supplying over one half of the maintenance energy needs. The cow lost
10 to 20 Mcal body tissue daily while producing 27 to 35 Mcal (85 to
110 lbs) milk daily. In late lactation, the cow deposited large amounts
of body tissue (15.2 to 18.8 Mcal) daily while still lactating 15.0 to
22 lbs of milk daily. The same trend of body tissue mobilization in
early lactation was for replacement later in lactation was also
observed in cows between 6 and 10 weeks post-partum. On average Flatt
et a1. (1970) observed that cows in their study had an average body
tissue loss of 6.9 Meal/day in mid lactation (24 weeks). The effects of
stage of lactation were highly significant.

Composition of energy value of the body tissue gain or loss by
cattle are variable. Energy equivalence value derived from various
studies with cows range from 6.3 to 7.9 Mcal for body tissue loss (Reid
and Robb, 1971). It appears that body tissue gain or loss could range
from as much as 100% water to 90% fat (Reid and Robb, 1971).

Mobilization of body tissue thus occurs in most cows during peak

27

periods of lactation.

Bauman and Curie (1980) reported that cows took approximately 16
weeks post-partum to consume enough energy each day to meet daily
requirements for milk production. They calculated that over the entire
first month of lactation one third of the energy in milk is from
mobilization of body reserves. Broster et a1 (1975) conducted a study
on feed utilization by lactating dairy cows for a period of the first
24 weeks of lactation. They observed that the animals lost weight in
early lactation and started regaining the weight after peak period.
They also did regression of daily milk yield (Table 6). The regression
coefficient was large during early lactation and smaller in mid-
lactation. Thus the first six weeks of lactation appear to be most

important for tissue mobilization toward milk production.

Table 6. Regression of liveweight change on milk yield (kg/day)
measured at various stages of lactation (Broster et al., 1975).

 

 

Weeks of Regression

lactation coefficient SE
1-6 -0.122 0.0189
7-12 -0.042 0.0109
11-16 -0.062 0.0160
15-20 -0.052 0.0148
19-24 -0.040 0.0132

 

Several workers have estimate partial energy requirements for
weight change in lactating cows. Moe et al (1971) using 350 energy

balance trials with lactating dairy cow estimated a value of 1.339 Mcal

28

of ME for kg of body tissue gain. They calculated an overall efficiency
value of 75% for utilization of ME for weight change in lactation.
They also estimated a value of 0.54 as the efficiency of conversion of
body tissue to milk. Van Es (1976) estimated overall efficiency of
tissue deposition in late lactation as 50%. Walter and Mao (1989)
estimated values of 4.461, 0.581 and 1.920 Mcal of net energy (NE)
required for a kg of weight change over periods of negative energy
balance, positive energy balance and over entire lactation
respectively.
3.5.4 Energy Utilization for Pregnancy

The efficiencies of supporting pregnancy in the animal have not
been widely researched. In general more emphasis has been placed on the
study of efficiencies for growth and lactation. However, the importance
of pregnancy cannot be overlooked. As pointed by Bauman and Curie
(1980) pregnancy imposes a substantial cost to the animal because total
requirements for nutrients at the end of pregnancy are 75% greater than
in non pregnant animal of the same weight.

As early as 1957 Jacobsen et a1. derived a function of the energy
content of fetal tissue as

V _ 7.24 * e0.01741:

where v is the rate of energy deposition (in kcal/day) on day t. These
workers reported that the support of pregnancy increased heat
production in the dam between 2 and 2.5 times. Although they did not
compute the efficiency of maintaining pregnancy their results seem to
suggest that maintenance energy requirements during pregnancy increase.

This is confirmed by Bauman and Curie (1980) who reported that out of

29

88 kcal/kg per day required for pregnancy, 63% was heat.

The apparent efficiency of utilization of metabolizable energy for
pregnancy is exceedingly low (10 to 25%) in sheep (Rattray et al.,
1974; Graham, 1964) and cattle (Moe et a1. ,1970, 1971, 1972). Moe et
al. (1970) estimated the ME required for gestation from the results of
97 balance trials carried out with pregnant non lactating cows. The
estimate required to maintain pregnancy ranged from 60 to 65 e'0174t
kcal on day t. These values corresponded to efficiencies of 11 to 12%.
Moe and Tyrell (1972) reasserted that the efficiency of use of ME for
pregnancy was low (<25%). Walter and Mao (1989) estimated the ME
requirements to support pregnancy using data from lactating cows.

Their results indicated that less than 1% of ME intake was need for
pregnancy. In sheep Rattray et al. (1974) estimated that the
efficiency was between 12 and 14%. This apparent low efficiency, they
reported stems from ignoring the sizable cost of maintenance of
products of conception.

3.5.5 Energy Utilization for Lactation

It is well established that ME is utilized with varying partial
efficiencies according to use for maintenance, lactation, growth and
fattening. Lactation along with growth and weight gain, has been the
form of production most investigated. Although the broad subject of
energy efficiency during lactation encompasses the efficiencies of
digestion and milk production as well as endocrine effects, this review
will focus on the production efficiency of milk.

Many factors have been reported to affect energy utilization for

lactation. The type of diet affects ME utilization as reported by

30

Garret and Johnson (1983). A major portion of this effect probably
relates to the particular pathway of metabolism taken by each specific
nutrient. The pattern of nutrients comprising ME and therefore the
number of transformations would vary with diet.

Kronfeld et a1 (1980) observed that when protected fat is fed to
dairy cows, increases are observed in milk fat and in the calculated
efficiency of ME utilization for milk energy. Bauman et al. (1984)
pointed that the apparent increase in efficiency reflects a very low
energy cost associated with the transfer of absorbed fatty acids to
milk fat as compared with the energy costs of de novo synthesis of milk
fatty acids.

Van Es and Nijkamp (1969) found that percentage of crude protein
in the diet had no effect on lactation efficiency nor did crude fiber
percentage. Broster et a1 (1980) in their study using heifers in either
first or second lactation and fed at either high or low levels of
energy, observed that the group fed a low level in early lactation had
the highest efficiency of conversion of energy intake into milk.
Contrary to these findings Grieve et a1. (1977) determined that energy
efficiency for lactation increased with feed intake. Hashizume et a1
(1965) reported greater energetic efficiency for lactation in cows fed
high levels of concentrates than those fed low levels.

The extent to which individual cows vary in the efficiency with
which they utilize ME for milk synthesis is more difficult to quantify
because few if any experiments have directly addressed this question.
In summarizing 332 energy balance trials Moe et al (1972) Observed that

97% of the variation in energy balance was associated with the

31

variation in ME intake, diet and metabolic size. This seem to suggest
that little variation exists among cows in the partial efficiency with
which ME is utilized for lactation. Trigg and Parr (1981) were unable
to detect any association between predicted genetic merit and the
partial efficiency with which cows utilized ME for the synthesis of
milk or body tissue.

Nutrient partitioning has been the major source of variation among
animals in the energetic efficiency of lactation (Bauman and Curie,
1980). Moe (1981) in reviewing the extensive energy balance trials at
the USDA energy laboratory concluded that the major differences among
diets as well as among individual cows was in the amount consumed and
energy partition that is milk production or fattening rather than the
efficiency with which ME is used. This point was illustrated by Bauman
et a1 (1984) when they compared two cows Azalea and Bugle (Table 7).
Despite consuming equal amounts of the same diet, they exhibited marked
differences in nutrient partitioning during first 67 days of lactation.
Whereas Azalea averaged 12.3 kg/day of 3.5 % fat corrected milk (FCM)
and gained 39.1 kg of body weight, Bugle produced 26.3 kg of FCM/day
and lost 51.8 kg of body weight.

Similar differences in nutrient partitioning are seen in
genetically diverse lines. Animals of high genetic merit produce more
milk, have greater voluntary intakes and use more of their body
reserves in early lactation than those of low merit (Bryant and Trigg,
1981; Davey et al., 1983). The increase in productive efficiency that
occur in genetically superior cows are primarily due to dilution of

maintenance requirements.

32

Table 7. Example of animal differences in nutrient
partitioning (Bauman et al, 1984).

 

 

Variable Azalea Bugle
Initial body weight kg 517 519
Intake of diet ------ Equal ------
Liveweight change +39.1 -51.8
Mean daily milk kg 3.5% FCM 12.3 26.3

 

Estimates of energy required for milk production during positive
or negative energy balance have been computed (Moe et al., 1970; Walter
and Mao, 1989). These estimates differed between studies and slightly

with stage of lactation within studies (Table 8).

Table 8. Partial energy requirements for milk production (Moe et al
1970, Walter and Mao, 1989)

 

 

Physiological State Moe et al(l970) Walter & Mao(1989)
Mcal ME/kg SCM Mcal ME/kg SCM
Negative energy balance 1.512 0.706
Positive energy balance 1.576 0.741
Overall Lactation 1.552 0.706

 

Efficiencies of energy conversion to support lactation were also
quantified by several workers. Hashizume et a1. (1965) found the
efficiency of ME of diets containing 45 and 71% of concentrate consumed

in excess of maintenance was used with efficiencies of 74.0 and 68%

33

respectively for milk. Coppock et a1. (1964) used six mature lactating
nonpregnant cows to study the utilization of ME for milk production.
Using diets calculated to provide 50, 75 and 100% of estimated net
energy from alfalfa, efficiencies of ME in excess of maintenance were
65, 61, and 54% respectively for lactation. Van Es and Nijkamp (1969)
reported results of 41 balance trials with lactating cows. These
workers concluded that ME was used for milk production with an

efficiency of 54 to 58%.

4. Estimation of Residual Energy Intake in Growing Young Bulls
Using an Animal Model

34

35

4.1 ABSTRACT

Residual energy intake is defined as total net energy intake minus
the predicted energy requirements for maintenance and production in a
production period. The less energy intake left unused or the less REI,
the greater efficiency in using net energy intake. It is therefore a
good measure of energetic efficiency. REI was estimated for each of
650 growing young bulls from 31 Holstein-Friesian or Brown Swiss sires.
Animals were fed ad libitum diets consisting of 100%, 75%, 50% or 25%
expected energy from concentrates from 200 kg liveweight to slaughter.
The feeding experiment was conducted for a period of five years.
Average daily net energy intake (NEI), metabolic body weight and daily gain
(DC) in a production period on each of the bulls were analyzed. An
animal model for NEI containing fixed effects of year-treatment,
initial age of the animals at beginning of experiment and breed,
covariates of average DC and BWB/Q, random animal effects of sires and
bulls, and residual was used. Animal and residual effects were parts
of residual energy intake of a bull. Animal and residual variance
components were estimated by an Expectation Maximization algorithm of
Restricted Maximum Likelihood procedure. Partial net energy
requirements for growth and maintenance were 26.23 mJ/kg and 0.74
mJ/kg3/a, respectively, for the 650 bulls. The heritability estimate
for REI was a very low value of .14. The proportion of phenotypic

variation in NEI due to REI was 28%.

4.2 INTRODUCTION

Feed energy constitutes the largest item of expense in any cattle

36

production enterprise. One way of reducing these costs in the long term
is to breed for better feed efficiency (Brelin and Branning, 1982;
Brelin and Martinsson, 1986; Korver, 1987; Buttazzoni and Mao, 1988).
Before considering efficiency in a cattle breeding system, more
information about an appropriate measure of efficiency, the genetic
variation in efficiency and its relationships with other economically
important traits such as growth rate and carcass traits must be known.
Feed efficiency has been traditionally calculated as a ratio of
feed intake and weight gain in a given interval of growth (Koch et al.,
1963). Feed efficiency calculated this way has been found to have a
very high genetic correlation with growth rate, usually ranging from
.70 to .95 (Brelin and Branning, 1982). Therefore selection for feed
efficiency per se is considered needless as it is automatically
improved along with the selection for growth rate. However, feed
efficiency calculated this way is a very complex measurement including
a number of interacting factors (Koch et al., 1963; Korver, 1988).
Variation in the composition of gains (fat, lean or bone) and in
maintenance requirements prevents this measure from being a precise
estimate of energy conversion rate (Koch et al., 1963). The composition
of gain regarding fat and other tissues affects feed efficiency as the
energy required for a unit weight of fat is considered larger than for
other tissues (Webster, 1980; Geay, 1984). A high growth rate always
causes the proportion of feed energy necessary for maintenance to
decrease which automatically results in better feed efficiency (Brelin
and Martinsson, 1986). However, on the other hand the proportion of

feed energy often used for maintenance usually increases when an animal

37

gets larger which affects feed efficiency in the other direction (Geay,
1984). Due to strong genetic correlation between growth rate and mature
size (Karlsson, 1979), fast growing animals on average would be less
mature, therefore leaner, at a given liveweight, thus giving rise to
better feed efficiency.

In order to genetically improve utilization of feed energy the
concept of feed efficiency must be clearly specified because a
biologically more uniform measure is desirable and because growth rate
is already taken into consideration in the selection process. Koch et
a1. (1963), suggested that the most useful criterion for evaluating
efficiency of feed conversion in beef animals maybe the amount of
desired or marketable product produced for a given energy rather than
the fraction of energy in the feed which was converted to animal
tissue. Brelin and Branning (1982) measured feed efficiency as the
amount of feed energy which was required with ad libitum feeding of a
standardized diet to produce a carcass of the same weight in a given
period of time. This trait described as the average metabolic
efficiency of feed conversion for growing cattle from birth to
slaughter was termed true feed efficiency. True feed efficiency was
intended to express the ability of the animal to resorb feed energy and
to utilize it for maintenance and growth.

Residual energy intake (REI) defined as actual energy intake minus
predicted energy requirements in a production period has been suggested
as a good measure of energetic efficiency (Koch et al., 1963; Brelin
and Martinsson 1986 and Korver 1988). REI would reflect the differences

between individuals in utilizing metabolizable energy for maintenance

38

and production (Luiting, 1987 and Korver, 1988). If more energy an
animal takes is used for production and maintenance a lower REI would
thus indicate better efficiency. This approach of energetic efficiency
involves computation of partial energy requirements for production
(daily gain) holding maintenance constant or vice versa. However,
difficulties might appear since maintenance which is defined as a
function of metabolic body weight (BW3/4) will be highly confounded
with daily gain. The genetic nature of REI is, however, not well
understood and therefore needs to be investigated.

Using data from growing young bulls, the objectives of this study
were to (1) estimate partial net energy requirements for growth and
maintenance, (2) estimate residual energy intake for each animal and

(3) estimate the genetic parameters of REI.
4.3 MATERIALS AND METHODS

4.3.1 Experiment

A feeding experiment was carried out in Denmark using 650 bull
calves from a total of 31 sires of either Holstein-Friesian or Brown
Swiss. The experiment was conducted for a period of five years from
1978 to 1982. Bulls were introduced to the experimental diets at the
age of 28 days. From 28 days to 200 kg live weight, the feeding level
was restricted to 75% of expected ad libitum intake. From 200 kg
liveweight to slaughter weight the animals were randomly allocated to
four dietary treatments of 0%, 25%, 50%, 75% of expected energy from
roughages. The 0% diet was all concentrate and the other treatments

were fixed amounts of concentrates and roughages fed 3-4 times per day.

39

This feeding regime was to ensure feed availability at all times of the
day. In 1978 and 1979 concentrates were fed in fixed amounts based on
weight and roughages were fed ad libitum. In the last three years from
1980 to 1982 roughages and concentrates were fed as total mixed
rations. Energy composition of all the diets is shown in Appendix I.

The animals were randomly assigned to three slaughter weight
groups. Target slaughter weights were 340, 470 and 600 kg liveweight.

Calves were weighed biweekly. Age of the animals was also recorded
when they were weighed. Feed intake was recorded daily (sum of 4
intakes) and summed over a 2-week interval between weighings.
Weighbacks were taken twice a week. Thus feed intake was recorded as
the sum of all feed given in a two week period minus the weighbacks.
The energy content of roughages used was determined both in vitro via
chemical composition and using in vivo experiments with sheep reared
close to the maintenance level in order to obtain digestibility of the
diet. Net energy intake was computed according to the Danish Feed
evaluation system based on the Scandinavian feed units (SFU).
4.3.2 Data

A total of 9,798 2-week records of 650 young bulls were available
for analysis. There was a total of 20 year-treatment subclasses and 37
age classes. Each individual bull had 2-week repeated records and these
ranged from 4 to 25 records per animal. The number of records in the
year treatment subclasses ranged from 218 to 854. Tabulation of the
data is shown in Appendix 1.
4.3.3 Mbdel

To estimate the partial energy requirements for growth and

40

maintenance, and residual energy intake the following animal model was

used:

3 4

where:

yijk

3 4
swigk

b2

°13k

°13k

was the average daily net energy intake in production
period for a bull in the kth breed, in jth age

group, and ith year-treatment;

was the overall mean;

was the fixed effect of the ith year treatment with i -
l,2,...,20;

was the fixed effect of the jth age group with j -
l,2,...,12;

was the fixed effect of the kth breed with k - 1,2;

was the average daily gain of the mth bull in the
corresponding production period period of yijkmn;

was the partial energy requirement for growth;

was the average metabolic body weight of the mth bull in
the corresponding production period of yijkmn;

was the partial energy requirement for maintenance;

was the random animal effects of sires and bulls with
N(0,Ia§)

was the random residual with N(0,Iag).

After energy intake took account of the fixed classification effects

and the partial energy requirements for growth and maintenance, the

terms that were left namely aijk + eijk were collectively

41

estimates of residual energy intake (REI). Variance components
estimates for sire, bulls and residual error were estimated by the
restricted maximum likelihood (REML) approach utilizing the expectation
maximization (EM) algorithm as described by Jensen and Mao (1988). The
convergence criterion was set at 10"5 for the residual variance.
Appropriate relaxation factors were used to accelerate convergence.

To analyse data on repeated records a sire mode was used. This

model and the results from the model are show in Appendix II.

4.4 RESULTS AND DISCUSSION

Mean, SE, SD and CV for NEI, DC and 8w3/4 are shown in Table 9. The
CV for all the variables was high. Daily gain had the highest CV among
the animals and amongst all variables. There was a substantial
variation in average daily gain ranging from -1.71 kg to 3.79 kg. Total
NEI in a two week period also showed a high variation ranging from

296.85 Mcal to 1422.14 Mcal for different bulls.

Table 9. Mean, SE, SD, and CV fog pet energy intake (NEI), daily gain
(DC) and metabolic body weight(BW / ).

 

 

Mean SE SD CV%
NEI (mJ) 51.63 .38 9.72 18.82
ADC (kg) 1.24 .01 .19 15.36

8v3/4 (kg) 78.29 .38 9.81 21.34

 

42

4.4.1 Partial energy requirements for growth

The estimated partial net energy requirement for growth was 29.62
mJ/kg/day overall 650 bulls (Table 10). Jensen (1989) estimated partial
energy requirements for each of the same 650 bulls using a within bull
covariate model. The average of such requirement estimate was 23.0
mJ/kg/day which approximated closely our overall result, despite the
fact that his average daily estimate was for the entire period of 200
kg to slaughter.

Energy utilization for growth has been shown to depend on the
composition of body gain (Kielanowski, 1976; Pullar and Webster, 1977;
Thorbek, 1977 and Webster, 1980). In older animals, a larger proportion
of weight gain consists of fat (Andersen, 1984 and Geay, 1980). The
partial efficiencies of utilization of energy for protein and fat have
been reported to be different (Orskov and Mcdonald, 1970; Pullar and
Webster, 1977 and Geay, 1984). In their experiment with lean and
genetically obese rats, Pullar and Webster (1977) observed values of
.45 and .75 for efficiencies of protein and fat deposition,
respectively. Based on data from 52 experiments Geay (1984) in his
extensive literature review gave preferred values of .20 and .75 for
protein and fat accretion respectively on energy basis. The low
efficiency of metabolizable energy used for protein deposition
correspond to the expensive high rate of turnover. Since there is a
large difference in the partial energy efficiencies of protein and fat
deposition, the energy requirements for growth should therefore be
viewed in terms of body composition. Unfortunately for this study

information on composition of gain for each animal in each of the

43

weighing interval was not available.
4.4.2 Maintenance energy requirements

The partial net energy requirement for maintenance was 0.79 mU/kg
of metabolic body weight per day (Table 10). Again, Jensen (1989)
estimated partial net energy requirements for maintenance for each of
the same 650 using a within bull covariate model. The average estimate
obtained was 0.68 mJ/kg of metabolic body weight per day, a value that
closely approximated our result despite the whole production period
from 200 kg to slaughter he used in his study.

Maintenance requirements consists of the energy necessary to
maintain basal metabolism, conduct voluntary body activity and generate
heat necessary to maintain body temperature. Based on data from an
experiment with young bulls Andersen (1980) showed within breed
variation in maintenance requirements for beef bulls. He obtained
estimates of 0.541, 0.468, 0.355 mJ/kg3/4 for ad libitum feeding, 85%
and 70% of ad libitum intake respectively. Maintenance energy
requirements have been reported to depend on weight of the animals (Van
Es, 1980 and Geay ,1984). Van Es (1980) estimated requirements for
Hereford x Friesian bulls and found estimates of 124 Kcal/kg3/4 at
250 kg liveweight and 117 kea1/8w3/4 at 450 kg liveweight. Geay (1984)
also observed an apparent decrease in maintenance requirements as
liveweight increased. The metabolizable energy required for maintenance

declined by 10.5 Kcal/W 3/4 for each increase in 100 kg body weight.

44

Table 10. Partial net energy requirements for growth and maintenance.

 

DG (mJ/Kg/day) 26.23

13w3/4 (mJ/Rg3/4) 0 . 74

 

4.4.3 Residual Energy Intake

For residual energy intake, the estimated phenotypic SD was 2.72
mJ, additive genetic SD was 1.03 and heritability value was .14 (Table
11). This value is much lower than the reported literature values
(Andersen, 1980; Brelin and Brannang, 1982; Jensen, 1989). Andersen
(1980) using a small data set obtained a value of .31 and Brelin and
Brannang estimated a value of 0.28. Jensen (1989) used the same data
set we used and estimated a heritability value of 0.275. The model
used by Jensen (1989) contained some fixed factors that were different
from those in the model used in this study. The difference in the
heritability values may also be due to the fact that in his study
Jensen used a two stage model. REI for each bull obtained from a within
bull covariate model was used as a dependent variable in a sire model.
In this study REI was estimated simultaneously with the partial energy
requirements for production and maintenance.

The proportion of the phenotypic SD in total net energy intake due
to REI was 28%. This in conjunction with a low heritability seem to

suggest that most of the variation due to REI is due to environment.

45

Table 11. Estimates of additive genetic and phenotypic SD and
heritability values for REI.

 

Additive SD 1.03
Phenotypic SD 2.72
SD(REI)/SD(NEI) .28
Heritability .14

 

4.5 CONCLUSIONS

For growing young bulls in performance test, a much greater
proportion of net energy intake was used to meet the requirements for
growth as compared to that for maintenance. The energy requirement for
growth relate closely to composition of gain as documented in the
literature, however this study was unable to substantiate such
relationship due to the lack of data. The estimates for both growth and
maintenance partial net energy requirements from the animal model using
average daily NEI in growth period were consistent with those from an
intrabull covariate model used by Jensen (1989).

The phenotypic variation in total net energy intake due to REI was
a relatively small value of 28%, but heritability estimate for REI was only
0.14. This seems to suggest that variation in REI in growing young

bulls was largest due to causes other than additive genetic variation.

5. Estimation of Residual Energy Intake for Lactating Cows
Using an Animal Model

46

47

5.1 ABSTRACT

Residual energy intake (REI) is defined as the remaining energy
from total net energy intake (NEI) after all the energy uses for
production and maintenance have been accounted for. The idea of REI as
a measure of feed efficiency is that the greater the proportion of
energy intake that can be accounted for or the smaller the REI the more
efficient is the animal. REI was estimated for each of 247 Holstein
cows from 127 sires and 226 dams distributed in five herds across the
US. Cows were in four parity groups. Data consisted of daily milk
yield, net energy intake and dry matter intake, biweekly measures of
milk components and body weight measures taken at unequal intervals
throughout lactation. Average daily NEI in a lactation was used as a
dependent variable in a model that contained fixed effects of parity
and herd-season subclass; covariates of average daily solids corrected
milk and metabolic BW and weight change in a lactation; random animal
effects of cows, dams and sires; and random residual. From this model
REI was a sum of cow, dam, sire and error effects. Partial energy
requirements for SCM , maintenance,and weight change estimated for all
cows were 0.54, 0.15 and 1.52 Meal/kg, respectively. Heritability
value for REI was .016 with phenotypic SD being 2.455. The proportion

of the phenotypic SD in NEI due to REI was a high 68%.

5.2 INTRODUCTION

In dairy production, feed is the major part of production costs.

Improving a cow's efficiency of converting energy intake to produce

48

milk should be unimportant goal for the dairy industry. However,
genetic selection in dairy cattle has not considered feed intake or
feed efficiency. Such negligence is partly due to lack of information
about the genetic aspects of nutrient intake and utilization.

Gross feed efficiency in dairy cattle is defined as the ratio of
milk output over feed intake or its inverse. It has been found to be
influenced by diet, and other environmental factors, genetic ability
and physiological state of the cow to utilize nutrients for milk
(Bauman et al., 1984, Blake and Custodio, 1984). Heritability of gross
feed efficiency (GFE) has been reported to range from 0.4 to 0.6
(Hickman and Bowden, 1971). Blake and Custodio (1984) cited the genetic
correlation between milk yield and GFE being from 0.88 to 0.95, while
the phenotypic correlation ranged from 0.60 to 0.95. Because of the
high genetic correlation between GFE and milk yield, selection for GFE
has been considered as needless because it would be automatically
improved via selection for milk yield.

However, data used to compute all the genetic parameters for GFE
came from dairy cattle fed concentrates according to milk production
thus forcing a high correlation. The high phenotypic correlation could
be due to the decrease in maintenance energy as milk production
increases. GFE assumes that all feed consumed is utilized for milk
production and does not adjust for maintenance and other net energy
uses ( Meyer and Garrett, 1967, Buttazzoni and Mao, 1988, Walter and
Mao, 1989). Blake and Custodio (1984) pointed that milk production is
not only depended on feed intake but also on body tissue losses or

gains, a factor that is not considered in the computation of gross feed

49

efficiency, thus making gross efficiency an inaccurate measure of
productive efficiency.

Blake and Custodio (1984) suggested that feed efficiency for
lactating cows should be defined as dietary nutrients to milk after
adjusting for nutrient supplied by tissue catabolism. Mao, Walter and
Buttazzoni (1988, 1989) suggested net energy efficiency as a better
biological indicator of a cow's productive efficiency. They defined net
efficiency as the ratio of energy contained in milk over the portion of
energy used to produce it above net energy requirements for maintenance
and for changes in body tissue reserves. Net efficiency is thus a
better biological indicator of productivity than GFE.

Residual energy intake (REI) or residual efficiency is defined as
the remaining energy from total net energy intake (NEI) after all the
energy uses for production and maintenance have been accounted for
(Brelin and Brannanng, 1982, Koch et al., 1963, Korver, 1988, Luiting,
1987). The idea of REI as a measure of efficiency is that the greater
the proportion of energy intake that can be accounted for or the
smaller the REI the more efficient is the animal. REI thus reflects the
differences among animals in utilizing net energy for production and
maintenance. In laying hens heritability for REI ranged from .25 to .40
(Luiting, 1987) and in growing bulls the estimate was .28 (Koch et al.,
1963, Jensen, 1989).

The objectives of this study were using data from lactating cows
to 1) estimate the partial energy requirements for SCM, maintenance,
and weight change during lactation for all cows; 2) estimate REI for

each animal and; 3) estimate the genetic parameters for REI.

50

5.3 MATERIALS AND METHODS

5.3.1 Data

An experiment involving five herds distributed across the US
provided records on 247 cows distributed in four parity groups. Cows
were identified by 127 sires and 226 dams. In each of the five herds,
cows were assigned to three groups based on their projected production,
which was determined by the average milk production during the first
three weeks. Cows that produced less than 28 kg/day over the first 3
weeks were classified low producers, between 28 and 34 kg/day were
medium and those over 34 kg were high. All heifers were in one group
regardless of production. Total mixed rations were fed to all heifers
and cows with varying forage to concentrate ratio according to
production group and lactation. Ingredients for diets varied among
location. In each case, however, energy provided was 10% more than the
estimated cow's energy requirements according to NRC (1978).

Production data were recorded at regular intervals and included
daily milk yield, biweekly measures of milkfat, protein, lactose and
solids not fat. Feed intake was measured daily and included dry matter
intake (DMI) in kilograms and intake of net energy (NEI). Additionally
BW for each individual cow were taken throughout lactation according to
the following protocol: four measurements the first month, one
measurement every 2 months for the next 8 months and on each of days
300 and 301 of lactation. BW change for each cow during lactation was
computed as the difference between the mean of the last 2 and last 4 BW

measures. Based on the calving dates three seasons were identified as

51

October and November, December through February and March through
October. There were 14 herd-season subclasses.The lactation length of
each cow was at least 180 days. Tabulation of data is shown in
Appendix II.

From each lactation average daily NEI, solids corrected milk (SCM)
and BW3/4, and weight change were used for analysis.
5.3.2 Model

The following model was used to partition net energy intake into
energy requirements for production, maintenance, and BW during a
lactation for all cows, to estimate REI for individual cows and to
estimate genetic parameters for REI.

yijk - u + P1 + HSj + b1(SCMijk) + b2(wcijk) + b3(8wi§fi) + aijk

+ eijk

where:

yijk was the average daily NEI of a cow in a lactation;

p was the overall mean;
P1 was the fixed effect of parity with i - 1,2,3, 4;
H81 was the fixed effect of herd by season with j - l,2,...,l4;

SCMijk was the average daily SCM production as a covariate;

b1 was the partial energy requirement for milk production;
wcijk was weight change in a lactation as a covariate;

b2 was the partial energy requirement for weight change;
BWigfi was the average metabolic body weight as a covariate;
b3 was the partial energy requirement for maintenance;
aijk was the random animal effects with N(0,Ia§);

eijk was the random residual with N(0,Io§).

52

REI was the sum of animal and residual effects. Variance components
estimates for animal and residual effects were estimated by the
Restricted Maximum Likelihood (REML) utilizing the Expectation
Maximization algorithm as described by Jensen and Mao (12). Convergence
criterion was set at 10"5 for the residual variance. Appropriate

relaxation factors were used to accelerate convergence.
5.4 RESULTS AND DISCUSSION

The mean, SE and CV for NEI, SCM, BW3/4 and weight change are
shown in Table 12. Weight change had the highest CV among cows and
amongst all variables. Weight change varied substantially among cows.
Some cows lost as much as 60 kg while other cows gained as much as 360
kg. Based on CV values, SCM , NEI and BW3/4 each had a fair amount of

variation among lactating cows.

Table 12. Simple statistics of variables used in analysis.

 

 

Mean SE SD CV%.
NEI(Mcal) 29.06 0.23 3.64 12.52
SCM(kg) 23.04 0.24 3.74 16.22
8V3/°(kg) 123.73 0.61 9.57 7.74
Weight change(kg) 91.53 3.41 91.53 58.57

 

5.4.1 Partial energy requirements for SCM.
Partial regression coefficient for NEI on production (SCM) was

0.54 (Table 13). This value represent the partial energy requirements

53

for SCM. Using the same data Walter and Mao (1989) estimated partial
energy requirement for each of the 357 cows using within cow regression
models. The average requirement for SCM of all cows from a two stage
regression model was 0.34 Meal/kg an estimate that is slightly lower
than ours. Differences in models used in the two studies would
contribute to the discrepancy. However, both estimates were below the
values published by Moe et al. (1971). They estimated values of 1.54,
1.64 and 1.85 Meal/kg of SCM with diets containing 50%, 75% and 100% of
estimated net energy from roughages respectively.

Efficiency of net energy utilization for production depends on a
number of factors. Moe et al (1971) asserted that the amount of energy
required by a lactating cow depends upon the genetic potential of the
cow and the level of production desired. Davey et a1. (1983) pointed
out that animals of high genetic merit for production partition energy
differently than those of low genetic merit. Likewise, Custodio et al.
(1983) found that increased production of FCM corresponds with
increased efficiency for FCM with a high significant residual
correlation of .75 for the two traits. Hashizume et a1. (1965)
substantiated the effect of quality and quantity of ration in
influencing efficiency of utilization of energy for milk production.
Greater energetic efficiency was observed in lactating cows fed high
levels of concentrate than those fed low. Quantity of ration also
affects energy efficiency of milk production.

5.4.2 Maintenance Energy Requirements.
Partial net energy requirements for maintenance was estimated to

be 0.15 Mcal/kg. Again Walter and Mao (1989) estimated the same value

54

from a within cow two-stage regression model. Both estimates were
below the estimates of the NRC (1989) guidelines of 0.073 Mca/kg and
some of the estimates published earlier (Moe et al.,1970, 1971, Moe,
1981, Walter and Mao, 1989). The differences in maintenance requirement
between lactating cows can be due to a number of causes. Garret and
Johnson (1983), demonstrated the importance of diet and physiological
state on maintenance requirements. Because of the close relationship
between diet, physiological state and energy balance one might expect
the latter to affect maintenance requirements as well. Pregnancy may
increase maintenance requirements of lactating cows as reported by
Flatt et al (1969a).

5.4.3 Energy requirements for weight change

Partial energy requirements for weight change was 1.51 Meal/kg
(Table 13). Our value was lower than 1.92 Mcal/kg an estimate obtained
by Walter and Mao (1989). The differences between the two was small but
again could be due to the different models. Moe et al (1970) obtained a
value of 1.24 Mcal/kg for weight change gain.

The basis of weight change during lactation is due to the
metabolism and catabolism of tissue in support of lactation.
Mobilization of body tissue occurs in most cows during peak periods of
lactation. Some cows are capable of utilizing remarkable quantities of
body tissue for lactation during such periods of negative energy
balance. To re-establish itself to the same condition as at
parturition, the animal must eat more for body energy deposition either

during late lactation or during the dry period.

55

Table 13. PartiSIAnet energy requirements for weight change (WC),
maintenance (BW ) and SCM

 

WC(Mcal/kg) 1.51
BW3/4(Mca1/1<8) 0.15
SCM(Mcal/kg) 0.54

 

5.4.4 Residual Energy Intake

For residual energy intake (REI), the estimated phenotypic SD was
2.455, additive genetic SD was .315 and heritability was a low value of
.016 (Table 14). Literature has reported no results on REI on
lactating cows. However, in laying hens, Luiting (1987) reported a
heritability estimate of .25 and in growing young bulls heritability
was reported to be 0.28 (Koch et al., 1963, Jensen, 1989).

The proportion of the phenotypic SD due to REI was a high 68%.
This in conjunction with a low heritability value reflects that the

variation in REI of lactating cows may be largely due to environment.

Table 14. Estimates of additive genetic and phenotypic
standard deviation and heritability values for REI

 

Additive SD 0.315
Phenotypic SD 2.455
Heritability 0.016

REI(SD)/NEI(SD) 0.675

 

56

5.3 CONCLUSIONS

For the lactating cows, weight change had the highest net energy
requirement as compared to milk production (SCM) and maintenance on a
per unit basis. The high energy requirements for weight change in
lactating cows are mainly due to the high energy demands in late
lactation to cater for the tissue energy drain that occurs during
negative energy balance. However, on total production basis more energy
would still be needed for milk production. The partial net energy
requirement for SCM was lower as compared to literature values. The
estimate for maintenance was equal to that from a within cow regression
model used by Walter and Mao (1989).

The heritability value of REI was only .016. Selection for REI in
lactating cows would therefore not be fruitful. The proportion of the
phenotypic standard deviation due to REI was a high 68 %. This together
with a low heritability for REI indicates that variation in REI in

lactating cows is due to causes other than additive genetic effects.

6. SUMMARY

The total intake of metabolizable energy can be partitioned into
productive and nonproductive (maintenance) use of energy. These are
collectively known as the partial energy requirements. The remaining
energy after accounting for all the identifiable partial energy
requirements is called residual energy intake (REI). Therefore,

a small residual energy intake or a greater proportion of energy that
can be accounted for, would imply greater efficiency.
6.1 Growing Young Bulls

Partial energy requirements for growth and maintenance, and
residual energy intake were estimated for 650 bull calves from 31
Holstein-Friesian or Brown Swiss sires. Computation of partial energy
requirements and residual energy intake using an animal model was based on
average daily records in a production period for each young bull on
body weights and net energy intake.

Partitioning of energy intake showed that a greater proportion of
net energy intake was used to meet the requirements for growth as
compared to maintenance. However, energy requirements for growth are
more related to composition of gain.

REI was computed as sum of animal effects of sires and bulls
and error effects. The heritability value of REI low. The proportion
of the phenotypic SD due to total net energy intake was relatively
small . These results indicate the lack of additive genetic effects on

the variation of REI among bulls.

57

58

6.2 Lactating Cows

Data from Lilly Research Laboratories was used to estimate partial
energy requirements for production of solids corrected milk,
maintenance and weight change for lactating Holstein cows. The data
consisted of measurements on milk production, component percentages,
body weights and intake through complete lactations of 247 cows in 4
parity groups and from 127 sires and 226 dams distributed across five
herds. Cows were fed one of four rations determined by production
level. An animal model allowed partitioning of net energy intake into
partial energy requirements for production, maintenance, and weight
change and computation of REI for each animal as sum of cow, sire, dam
and error effects when average daily NEI was used as dependent
variable.

Results indicated that more energy calculated as Mcal/kg was
required for weight change in a lactation as compared to requirements
for SCM production or maintenance. Partitioning of net energy by
lactating cows is however dependent on a number of factors such as
amount and quality of ration and the physiological status of the
animal.

REI had a very low heritability value, but the proportion of the
phenotypic SD of NEI due to REI was high. As such variation in REI
among lactating cows is largely due to environmental effects such as
season or management. Incorporation of REI of as a measure of
productive efficiency for lactating cows may therefore not be fruitful.

In conclusion, this study examined REI which is a measure of net

energy efficiency. However, the relationship between gross energy

59

efficiency and net energy efficiency is unknown. If these two traits
are strongly and positively correlated then use of gross energy

efficiency would be recommended.

7 . APPENDICES

60

61

Appendix I.

Energy content of diets and tabulation
of data for Growing young bulls.

Table 1.1. Energy composition (mJ/kg) of the four diets used during
the entire experiment

 

Year Treatmenta Energy content(mJ/KG)

 

1978 .07
.70
.74

.29

bWNH
I-‘I-‘NO‘

1979 .08
.60
.60

.03

booms-t
NNU’O‘

1980 .14
.70
.19

.76

11>me
#UIUIO

1981 .24
.64
.76

.76

bump—-
I-‘I-‘Uwo‘

1982 .32
.05
.99

.20

J-‘wNi-I
vI-‘J-‘UICh

 

aTreatments 1,2 3, 4 - 100%, 75%, 50%, 25% of expected energy from
concentrates.

62

Table 1.2. Distribution of the records across the years and treatments.

 

 

 

Year Treatmenta Total
1 2 3 4
1978 382 489 519 490 1880
1979 464 414 484 557 1919
1980 426 471 580 510 1987
1981 607 854 218 234 1913
1982 518 541 529 511 2099
Total 9798

 

aTreatments 1,2,3,4 - 100%, 75%, 50%, 25% expected energy from
concentrates, respectively.

63

Table 1.3. Basic Statistics of net energy intakeafigEI), daily gain
(DG), body weight (BW), and metabolic weight (BW ) for different
year-treatment classes

 

 

Year Treatmenta Mean SD
NEI 1978 1 700.23 142.90 20.41
DG 1 1245.14 640.54 51.44
BW 1 367.11 108.26 29.50
8173/4 1 83.19 18.45 22.80
NEI 2 679.40 143.15 18.13
DG 2 1240.43 523.23 42.18
BW 2 363.75 100.84 27.72
8w3/4 2 82.70 17.21 20.81
NEI 3 619.55 107.43 17.34
DC 3 1228.05 442.38 36.02
BW 3 369.98 103.45 27.96
8w3/4 3 83.75 17.59 21.01
NEI 4 542.86 100.15 18.45
DC 4 1135.05 455.11 40.09
BW 4 361.84 99.49 27.49
8w3/4 4 82.39 16.95 20.57
NEI 1979 1 705.46 168.43 23.88
DC 1 1320.04 612.92 46.43
BW 1 369.81 110.08 29.76
8V3/4 1 83.64 18.72 22.39
NEI 2 700.76 153.28 33.95
DC 2 1256.73 469.51 37.86
BW 2 359.21 99.23 20.62
8w3/4 2 81.93 16.16 20.69
NEI 3 642.38 147.64 22.98
DG 3 1246.46 423.11 33.95
BW 3 366.44 105.99 28.92
8w3/4 3 83.11 18.06 21.73
NEI 4 574.45 128.73 22.41
DC 4 1143.24 407.87 35.68
BW 4 363.69 104.39 28.70
8w3/4 4 82.65 17.78 21.51

Table 1.3 (Cont'd.)

64

 

 

Year Treatmenta Mean SD CV
NEI 1980 1 713.32 161.85 22.69
DG 1 1293.43 557.57 43.11
BW 1 371.05 107.50 28.92
8w3/4 1 83.88 18.31 21.83
NEI 2 963.37 206.87 21.47
00 2 1269.34 505.93 39.86
BW 2 370.18 103.81 28.04
81:”4 2 83.78 17.68 21.10
NEI 3 929.42 205.91 22.16
DG 3 1134.36 481.92 42.50
BW 3 372.45 102.90 27.63
8w3/4 3 84.18 17.52 20.81
NEI 4 850.32 187.99 22.11
DC 4 1026.05 512.27 49.93
BW 4 372.04 102.52 27.56
8w3/4 4 84.11 17.46 20.76
NEI 1981 1 900.06 158.39 17.60
DC 1 1314.43 536.99 40.84
BW 1 365.49 104.76 28.66
WW4 1 82.96 17.85 21.52
NEI 2 809.77 144.72 17.87
DG 2 1020.74 490.32 48.04
BW 2 370.47 103.77 27.85
8w3/4 2 83.83 17.57 20.96
NEI 3 947.34 162.09 17.11
DG 3 1313.56 557.34 42.43
BW 3 357.79 103.34 27.50
8w3/4 3 84.75 17.54 20.69
NEI 4 817.78 136.41 16.68
DC 4 1033.36 45.78 43.81
BW 4 367.28 96.91 26.39
8w3/4 4 83.36 16.50 19.79
NEI 1982 1 720.36 138.53 19.23
DC 1 1219.80 562.86 46.14
BW 1 372.15 107.25 28.82
8w3/4 1 84.08 18.22 21.68

Table 1.3 (Cont'd.)

65

 

 

Year Treatmenta Mean SD CV
NEI 1982 2 747.40 183.97 23.58
DG 2 1152.49 433.04 37.58
8w 2 364.58 104.18 28.58
8143/4 2 82.81 17.77 21.36
NEI 3 780.39 183.97 23.59
00 3 1199.43 469.02 39.10
8w 3 362.42 107.79 29.74
8w3/4 3 82.39 18.32 22.24
NEI 4 730.39 172.29 23.59
DG 4 1261.53 438.09 34.73
8w 4 363.92 107.74 29.61
8113/4 4 82.65 18.33 22.18

 

aTreatments 1,2,3,4 - 100%,75%, 50%, 25% expected energy from
concentrates, respectively.

66

 

 

Table 1.4. Estimates of Year by Treatments subclasses
Year Treatment Estimate SE
1978 1 -1.99 1.61
1978 2 -3.36 1.60
1978 3 -7.86 1.60
1978 4 -11.96 1.56
1979 1 -2.00 1.63
1979 2 -1.86 1.61
1979 3 -6.37 1.60
1979 4 -9.86 1.57
1980 1 -2.17 1.60
1980 2 15.70 1.61
1980 3 13.68 1.57
1980 4 9.48 1.55
1981 1 12.53 1.62
1981 2 7.79 1.54
1981 3 14.13 1.67
1981 4 7.75 1.60
1982 1 0.98 1.65
1982 2 3.31 1.63
1982 3 4.81 1.63
1982 4 0.78 1.65

 

67

Table 1.5. Estimates of Initial age of young bulls at beginning of
experiment

 

 

Age (days) Estimate SE
140 -3.17 1.59
154 -2.67 1.31
168 -3.20 1.25
182 -2.15 1.21
196 -1.49 1.20
210 -1.30 1.19
224 -1.08 1.19
238 -0.97 1.21
252 1.14 1.27
266 -3.51 1.42
280 0.49 1.74
294 0.00 0.00

 

GROWING

Model

68

Appendix II

YOUNG BULLS: Sire model and results from the model

3 4
yijklmn ' P + T1 + A3 + “R + b1(DGijk1mn) + b2(BW1§k1mn) + 51

where:

yijklmn
p

T1

eijklmn

+ B(1)m + eijklmn

was total net energy intake in a 2-wk period

was the overall mean;

was the fixed effect of the ith year treatment with i -
l,2,...,20;

was the fixed effect of the jth age group with j -
l,2,...,37;

was the fixed effect of the kth breed with k - 1,2;

was the average daily gain of the mth bull in the
corresponding 2-wk period period of yijklmn;

was the partial energy requirement for growth;

was the average metabolic body weight of the mth bull in
the corresponding 2-wk period of yijklmn;

was the partial energy requirement for maintenance;

was the random animal effects of sires with 1- l,2...,31
and N(0,Ia§)

was the random residual with N(0,Iag)

69

Table 11.1. Basic Statistics of the variables analyzed by a sire model

 

 

Mean SE SD CV
NEI (mJ) 751.71 1.96 194.90 25.93
00 (kg) 1.20 0.01 0.51 42.13
8143/4 (kg) 83.29 0.18 17 77 21.34

 

Table 11.2. Partial net energy requirements for growth and maintenance.

 

DG (mJ/Kg/day) 29.00

Bw3/4 (mJ/Kg3/4) 0.79

 

Table 11.3. Estimates of additive genetic and phenotypic SD and
heritability values for REI.

 

Additive SD 4.09
Phenotypic SD 19.32
SD(REI)/SD(NE1) .42

Heritability .05

 

70

Appendix III.

Tabulation of data for Lactating Cows

Table 111.1. Distribution of cows in different parities

 

Parity

 

No. of cows

66

58

49

74

 

Table 111.2. Number of cows in different herd season subclasses

 

Herd Season

No. of cows

 

mUlU‘bwaWUJNNNl—‘H

WNF‘WNHWNHri-‘NH

25
10
27
26

5
19
20
17
36
17

4
13
16
12

 

Herds:

California

Michigan State University
North Carolina State
Penn. State

Illinois

Season:

1-
2-
3-

March-Sept.
0ct.-Nov
Dec.-Feb.

71

Table 111.3. Simple statistics of variables used in the analysis

 

 

by parity
Parity N Mean SE SD CV%.

NEI(Mcal) 1 66 28.96 0.49 3.96 13.69
SC§;£g) 1 66 22.54 0.42 3.41 15.15
BW (kg) 1 66 124.97 1.08 8.80 7.04
Weight change 1 66 48.31 5.95 48.30 46.48
NEI(Mcal) 2 58 29.03 0.52 4.00 13.78
sc§}zg) 2 58 22.73 0.51 3.93 17.30
BW (kg) 2 58 123.76 1.24 9.49 7.67
Weight change 2 58 88.46 6.72 51.20 57.89
NEI(Mca1) 3 49 28.92 0.47 3.29 11.39
SC§}Eg) 3 49 23.01 0.57 4.02 17.49
BW (kg) 3 49 121.52 1.34 9.41 7.74
Weight change(kg) 3 49 81.78 6.98 48.84 59.71
NEI(Mca1) 4 74 29.25 0.39 3.31 11.33
SC§;kg) 4 74 23.73 0.42 3.63 15.30
BW (kg) 4 74 124.05 1.20 10.31 8.31
Weight change(kg) 4 74 89.23 7.14 61.46 68.81

 

NEI - Net energy intake

72

 

 

Table 111.4. Estimates of the fixed effect of parity
Parity Estimate SE
1 0.70 0.51
2 0.84 0.51
3 0.94 0.54
4 0.00 0.00

 

Table 111.5. Estimates of the herd year-season subclasses

 

 

 

Herd Season Estimate SE
1 l -0.57 0.89
1 2 0.62 1.06
2 1 -2.32 0.86
2 2 -l.67 0.86
2 3 -1.52 1.34
3 1 -0.25 0.94
3 2 1.72 0.93
3 3 0.72 1.02
4 1 -4.17 0.89
4 2 -2.67 0.97
4 3 -2.21 1.50
5 l -1.09 1.02
5 2 -1.28 0.98
5 3 0.00 0.00
Herds: - California Season:l - March-Sept.
- Michigan State University 2 - Oct.-Nov
- North Carolina State 3 - Dec.-Feb.

Penn. State
Illinois

 

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