Embed Size (px)
Citation preview
UNIVERSITY OF CAPE COAST
A COMPARATIVE STUDY OF SOME PERFORMANCE CHARACTERISTICS OF
COBB AND ROSS BROILER STRAINS FED RATIONS WITH VARYING LEVELS
OF PALM KERNEL OIL RESIDUE (PKOR)
SAMUEL OFORI
2015
UNIVERSITY OF CAPE COAST
A COMPARATIVE STUDY OF SOME PERFORMANCE
CHARACTERISTICS OF COBB AND ROSS BROILER STRAINS FED
RATIONS WITH VARYING LEVELS OF PALM KERNEL OIL
RESIDUE (PKOR)
BY
SAMUEL OFORI
Thesis submitted to the Department of Animal Science of the School of
Agriculture, College of Agriculture and Natural Sciences, University of
Cape Coast, in partial fulfillment of the requirements for award of
Master of Philosophy Degree in Animal Science
DECEMBER, 2015
ii
Declaration
Candidate’s Declaration
I hereby declare that this thesis is the result of my own original work and that
no part of it has been presented for another degree in this university or
elsewhere.
Candidate’s Name: Samuel Ofori
Signature: …………………………….. Date: ……………….
Supervisors’ Declaration
We hereby declare that the preparation and presentation of the thesis were
supervised in accordance with the guidelines on supervision of thesis laid
down by the University of Cape Coast.
Principal Supervisor’s Name: Professor F. N. A. Odoi
Signature: ………………………. Date: ……………………….
Co-Supervisor’s Name: Professor O. Baffour-Awuah
Signature: ……………………….. Date: ……………………….
iii
Abstract
A comparative study was conducted on some performance
characteristics with 225 each of Cobb 500 and Ross 308 broiler chickens fed
three rations in which PKOR replaced wheat bran at 0% (control), 10% and
20% levels. There were 6 treatments (of 75 birds each) and 3 replicates (of 25
birds each), in a completely randomized designed 2x3 factorial experiment.
The trial used 3-week old broiler chicks over a 5 week period. The effects of
genotype, ration and their interactions on some growth parameters, carcass
traits, haematological and serological traits were assessed. The effects of
genotype and ration on most of the carcass, haematological and serological
traits evaluated were not significant (p>0.05). The growth traits evaluated
were also similar (p>0.05) for both Cobb 500 and Ross 308 birds. On the other
hand, there were significant (p<0.05) ration effects on some major growth
parameters. The control birds (0% PKOR) had significantly higher (p<0.05)
final live weights (2849g) compared with for birds on 10% (2654g) and 20%
(2644g) PKOR rations; values were similar (p>0.05) for latter 2 groups. This
trend and significance levels were reflected in other growth parameters such as
weight gain and growth rate. Feed cost/kg weight gain declined from the
control (GH¢4.29) through to birds fed rations containing 20% of PKOR
(GH¢3.59), although differences were not significant (p>0.05). The effects of
genotype × ration interaction on all performance parameters assessed were not
significant (p>0.05); implying that farmers can raise either Cobb 500 or Ross
308 on any of the three rations offered without any detrimental effects on
performance and would make savings/profit in feed cost/kg weight gain of
GH¢0.70 when PKOR is used in broiler ration up to 20% inclusion rate.
iv
Acknowledgements
Acknowledgement and thanks are due to the many people who
contributed to the development of my research and results presented in this
thesis. In particular, very warm thanks are given to my supervisors, Prof. O.
Baffour-Awuah, Prof. F.N.A. Odoi and Dr. Julius Hagan who assisted me in
diverse ways throughout my research. Their comments and guidance not only
encouraged me to work smarter and diligently, but also added considerably to
the initial and final versions of the text, making it more relevant to animal
production and livestock research.
Thanks are also due to my Head of Department, Prof. S.O. Apori, my
lecturers Dr. K.S. Awuma and Alhaji Ibrahim Adams for their constant
encouragement and contribution to the research work. Special thanks go to
Mr. Stephen Adu (chief laboratory technician) who assisted me in the
laboratory analysis of samples.
I should also like to thank the staff at the School of Agriculture
Teaching and Research Farm (i.e. Mr. Theophilus Yangtul and Mr. Edward
Kofi Akyea), who assisted me in data collection.
Furthermore, I thank my colleagues, Mr. Senyo Akorli and Mr.
Ebenezer Gyamera for their morale support and company during my entire
studentship years at the University of Cape Coast.
Finally, I should like to thank my dear wife, Mrs. Barbara Ofori, for
her encouragement, ‘open-handed generosity’, and prayer support (without my
even asking) during the entire graduate studies period.
v
Dedication
To my dear wife, Mrs. Barbara Brown Ofori, my mum, Mary Adubea,
and my sister, Mary Ofori
vi
TABLE OF CONTENTS
Declaration ii
Abstract iii
Acknowledgements iv
Dedication v
Table of Contents vi
List of Tables xii
List of Plates xiv
CHAPTER ONE: INTRODUCTION 1
Introduction 1
Background to the Study 1
Problem Statement 4
Significance/Justification 5
Conceptual Hypotheses 6
Objectives of the Study 8
CHAPTER TWO: LITERATURE REVIEW 10
Introduction 10
Categories of Phenotypic Traits in Farm Animals 10
Expression and Measurement of Phenotypic Traits 12
Genotype by Environment Interaction (G × E) 18
Methods for Estimating Magnitude of Interactions as Genetic
Correlation 21
Nutrient Requirement of Broiler Chickens 23
Diet: The Most Economically Important ‘Special Environment’ in
Poultry Production 26
vii
Some Common Feed Ingredients Used in Poultry Diets 26
Factors Influencing Poultry Farmers Choice of Feeds 31
Feed Quality 32
Farmer’s Technical Ability and Knowledge on Feed Processing
Methods 33
Long-Term Availability of Feeds 34
The Cost Price of Feed 34
Product Identification-What Exactly is PKOR 35
Use of Terminology 36
Production Methods of Palm Kernel Oil Residue 37
Solvent Extraction of Palm Kernel Oil 38
Mechanical Extraction of Palm Kernel Oil 38
Manual or Traditional Extraction of Palm Kernel Oil 39
Management/Handling of PKOR for Use as Poultry Feed 42
The Potential of PKOR 43
Nutritional Merits of PKOR and its Variants (PKC and PKM) 44
Crude Protein Content (CP) 45
Amino Acid Availability 46
Fat Content (EE) 47
Crude Fibre 48
Mineral Elements 49
PKOR as a Possible Maillard Reaction Product 50
Effects of Consuming Maillard Reaction Products 51
PKOR/PKC Feeding Trials in Poultry 52
PKOR/PKC Feeding Trials in Broiler Chickens 53
viii
Growth Response of Broiler Chickens to PKOR/PKM Based Diets 53
Carcass Characteristics of Broilers on PKC/PKOR Based-Diets 56
Haematological Response of Broiler Chickens to PKOR/PKC 58
Haematological Components and Their Functions 59
Effect of Genotype/Breed on Haematology 65
Serum Biochemical Profile 67
Effect of Genotype/Breed on Biochemical Profile of Chickens 70
Genotype–Environment Interactions in Broilers 73
Costs and Benefits of Using PKC/PKOR 76
CHAPTER THREE: METHODOLOGY 78
Introduction 78
Experimental Site 78
Phases and Duration of Experiments 78
Experimental Animals 79
Experiment Design 79
Housing 80
Management/Handling of PKOR for Use as Livestock Feed 80
Feeding 81
Analysed Proximate Composition of Experimental PKOR 82
Proximate Composition of Experimental Broiler Finisher Concentrate
and PKOR Used to Formulate the Rations 83
Composition of Experimental Rations (% of 100 Kg Weight) 83
Feed Analysis 85
Vaccination and Medication Schedule 85
Collection of Blood Samples 86
ix
Growth Performance and Carcass Data Collection 87
Feed Intake 87
Live Weight Gain 87
Feed Conversion Ratio (FCR) 87
Slaughtering of Birds for Carcass Traits 88
Dressing Percentage 88
Cost-Benefit Analysis of Feeding PKOR-Based Rations 89
Statistical Analyses 89
CHAPTER FOUR: RESULTS 90
Introduction 90
(1) Effect of PKOR-Based Rations on Performance (Growth
Parameters, Carcass Traits, Haematology and Serology) of Cobb 500
and Ross 308 Broiler Strains 90
(2) Influence of Genotype on Performance (Growth Parameters,
Carcass Traits, Haematology and Serology) of Cobb 500 and Ross 308
Broiler Strains 98
(3) Influence of Genotype x Ration Interaction on Performance
(Growth Parameters, Carcass Trait), Haematological and Serological
Traits in Cobb 500 and Ross 308 Broiler Strains 101
CHAPTER FIVE: DISCUSSION 102
Introduction 102
(1) Effect of PKOR-Based Rations on Performance (Growth
Parameters, Carcass Traits, Haematology and Serology) of Cobb 500
and Ross 308 Broiler Strains 102
Crude Protein 102
x
Metabolisable Energy (ME) 103
Body Weight 103
Feed Intake 106
Water Intake 107
FCR 107
Feed Costs 108
Warm Carcass Weight 109
Chilled Carcass Weight 110
Primal Cuts 111
Visceral Organs 112
Haematological Parameters 112
Serological Parameters 113
(2) Influence of Genotype on Performance (Growth Parameters,
Carcass Traits, Haematology and Serology) of Cobb 500 and Ross
308 Broiler Strains 115
Growth Traits 115
Carcass Traits 116
Haematological Parameters 117
Serum Biochemical Profile 118
(3) Influence of Genotype × Ration Interaction on Some Performance
(Growth, Carcass Traits, Haematology and Serology) Parameters of
Cobb 500 and Ross 308 Broiler Strains 120
CHAPTER SIX: SUMMARY, CONCLUSIONS AND
RECOMMENDATIONS 121
Introduction 121
xi
Summary 121
Effect of Ration on Performance 122
Effect of Genotype on Performance 123
Effect of Genotype-Ration Interaction on Performance 123
Conclusions 124
Recommendations 125
REFERENCES 126
APPENDICES 150
xii
List of Tables
Table Page
1 Dietary Nutrient Requirements of Broilers (90% DM) 24
2 Comparison of Nutrient Composition of PKC and PKOR 45
3 Percentage Amino Acid Composition of Some Commonly
Used Feed Ingredients in Ghana, Including PKC a Variant of
PKOR 46
4 Nutrient Composition of Wheat Bran and PKOR on DM Basis
49
5 Mineral Element Composition of PKC and PKOR 50
6 Range of Values for Haematological Parameters in Chicken
62
7 Effects of Dietary Levels of PKC (a Variant of PKOR) on
Haemtological Profile of Pullets 63
8 Normal Reference Values for Some Serum Electrolyte and
Biochemical Parameters for all Chickens 72
9 Cost and Benefit Analysis of Feeding Layers Varying Levels of
PKOR-Based Rations 76
10 Proximate Composition (on Dry Matter basis) of PKOR Used
in Experimental Rations 82
11 Proximate Composition (on DM Basis) of Individual
Ingredients Used in Experimental Rations 83
12 % Composition of Experimental Rations and their Proximate
Analysis 84
13 Vaccination and Medication Schedule Followed 85
xiii
14 Mean Values of Dietary Treatment Effect on Growth
Performance Parameters of Broiler Chickens (4-8 Weeks of
Age) 91
15 Mean Values of Dietary Treatment Effect on Carcass and
Organ Weights of Broiler Chickens (4-8 Weeks of Age) 93
16 Mean Values of Dietary Treatment Effect on Some
Haematological Traits in Broiler Chickens (4-8 Weeks) 95
17 Mean Values of Dietary Treatment Effect on Serological
Profile of Broiler Chickens (4-8 Weeks of Age) 97
18 Mean Values of Effect of Genotype on Growth Performance of
Broilers (4-8 Weeks of Age) 98
19 Mean Values of Effect of Genotype on Carcass Weights,
Dressing Percentages and Organ Weights of Broilers 99
20 Mean Values of Effect of Genotype on Blood Parameters of
Broiler Chickens as Influenced by Genotype 100
xiv
List of Plates
Plate Page
1 PKOR being poured from the Boiler 36
2 Roasting of Palm Kernels 41
3 Milling Roasted Palm Kernel into Paste 41
4 Heating Milled Paste 41
5 Skimming of Oil from Heated Paste 42
6 Sun Drying of PKOR on Concrete floor 43
7 A pen with 3-week old birds 80
1
CHAPTER ONE
INTRODUCTION
Introduction
This chapter provides an introduction to the research undertaken.
Among the topics discussed in the chapter are: background to the study,
problem statement, significance/justification, hypotheses, general and specific
objectives of the study.
Background to the Study
Growth performance in animals is influenced by genotype as well as
environmental factors, including nutrition (Cameron, 1997; Falconer &
Mackay, 1996). Thus, the expression of phenotypic traits of broiler chickens is
determined by both their genetic potential (genotype) and the environment to
which they are exposed; and sometimes, by an interaction between genotype
and environment (G × E).
The differential expression of genotype over environment is reflected
in the genotype x environment (G × E) interaction phenomenon. The term
genotype x environment interaction is most commonly used to describe
conditions where different genotypes (e.g. breeds, lines, strains, progeny
groups) respond differently to different environments (e.g. diet/feed/nutrition,
housing, location, season, production system, medication, sanitation),
according to Sheridan (1990). The differences of various genotypes in their
responses to different environments include changes in mean performance in
the measured traits of interest. The aspect of genotype x environment
interaction in broiler production which is best known to influence phenotypic
2
and economic performance is that of genotype x nutrition/diet interaction
(Razuki & Al–Rawi, 2007). Consequently, Hoste (2007) reported that in terms
of the future direction of genetics linked to nutrition, costs of feed will remain
a factor in the economics of production, and therefore the optimization of feed
utilization by birds will remain a priority to geneticists in making economic
decisions.
Over the last four decades, average daily body weight gain in broiler
chickens increased from about 22 g to more than 50 g, yielding live market
weights of more than 2500 g within a maximum age of 50 days; concurrently,
feed conversion ratio decreased from 4.1 to 1.7 (Arthur & Albers, 2003;
Razuki & Al–Rawi, 2007). The role of breeding in this tremendous increase in
growth rate has been documented by Havenstein, Ferket, Scheideler and
Larson (1994) and McKay, Barton, Koerhuis and McAdam (2000). Some of
the broiler breeds or genotypes with faster growth rate currently on the world
market are the Cobb 500 and Ross 308. However, while the genetic potential
of the bird is improved, nutritional or dietary management which influences
most metric traits, including blood parameters (haemtological and serological
profile which assesses the health status of birds) also has to adapt to these
changing demands; there is therefore a need to formulate or compound high
quality and well balanced, but least cost diets, that will enhance optimum
growth. The general goal of poultry genetics for the immediate future is to
breed chickens with the ability to perform well within a wide range of
nutritional planes or dietary levels (Cavero, Icken, Schmutz & Preisinger,
2011; Preisinger & Flock, 2000).
3
As indicated earlier, like phenotypic performance, the economic
performance of a broiler chicken enterprise depends on both genotype (breed)
and environment, most importantly diet. Fortunately, many poultry breeding
companies have been able to develop fast-growing genotypes or breeds such
as Cobb 500 and Ross 308 which can attain a market weight of about 2.5kg
within eight weeks (Arthur & Albers, 2003). All broiler genotypes,
irrespective of their genetic potential, will therefore require the right nutrition
from a well formulated or compounded diet. Diet seems to be the most
important variable input in commercial broiler chicken production under
intensive systems of management (Apantaku, Oluwalana & Adepegba, 2006).
Depending on the efficiency of feed utilization by broiler genotypes and the
ingredients used in formulating rations, diet forms about 70 to 80 percent of
the total cost of broiler meat production (Flake & Ashitey, 2008; Gyamera,
2010; Nyanu, 1999). This suggests that if the cost of diet is lowered, the profit
margin of broiler poultry farmers would likely increase, and vice–versa.
Additionally, lowered feed cost could also reduce the price of broiler chickens,
thereby increasing per capita consumption of animal protein sources which is
rated as low in Ghana, according to a report by Food and Agriculture
Organisation (FAO, 2004).
Generally, prices of conventional feeds in Ghana such as maize, wheat
bran and fish meal have been rising steadily in recent years; this has resulted
in unreliable supply and relatively high prices of locally produced broiler
chicken meat. This has led to the closing down of many small and medium
scale broiler poultry farms despite the availability and affordability of fast-
growing commercial broiler genotypes (breeds) over the last decade (Flake &
4
Ashitey, 2008; Yangtul, 2010). Reducing and regulating feed costs are
therefore critical to the sustainability and profitability of the broiler industry,
given that feed cost alone can account for as high as 80 percent of the variable
production costs (Flock, Ameli & Glodek, 1991; Gyamera, 2010).
With the ever increasing prices of some conventional feed ingredients
and their associated erratic supply, there is no doubt that, the use of other non–
conventional feedstuffs, and formulation of high quality balanced rations using
the least cost ration approach, will be an ideal avenue to reduce feed costs in
broiler chicken production (Nyanu, 1999). Hence, in recent times, many
animal nutritionists have focused on using crop residues and agro-industrial
by-products, which are low-cost vast feed resources very much under-utilized
in Ghana, to solve the problem of acute feed shortage and high feed cost. This
assertion confirms observations of Flake and Ashitey (2008) who reported that
farmers and feed manufacturers are switching to low-cost substitutes such as
palm kernel cake (PKC), palm kernel meal (PKM) and copra cake which are
by-products of agro-processing. Undeniably, palm kernel oil residue (PKOR; a
variant of PKC/PKM) which is a solid waste by-product of cottage industries
that extract oil from palm kernel, seems to have some potential as a low-cost
non-traditional feed ingredient for poultry, according to feeding trials on
broiler birds undertaken by Odoi, Adam, Awuma, Adu and Ayitey (2007) in
Ghana.
Problem Statement
The most significant decision of a broiler poultry farmer that would
influence the economic output of the farm is how to choose genotype/s
5
(breed/s) of birds which can attain market weight of 2.5-3.0kg within eight
weeks, on a well-balanced but least cost feed or dietary environment.
Efficiency of feed utilization is genotype-specific; but with the existence of
genotype x nutrition/diet interactions, it is difficult for a farmer to select the
most genetically superior broiler breed that can utilize readily available, low-
cost feedstuff for optimum growth (i.e. attaining market weight of 2.5-3.0kg
within 8 weeks) to guarantee maximum economic returns on investment. This
notwithstanding, even with the availability of affordable fast growing
genotypes (breeds) on the market, farmers cannot maximize their profit due to
unbearably high cost of conventional feed ingredients such as cereals,
legumes, fish and wheat bran.
Significance/Justification
Over the last few decades, poultry breeding and genetics have had
remarkable impact in producing fast-growing broiler genotypes well adapted
to changing global environments, particularly nutrition. Indeed, the goal of
any breeding programme is to develop new breeds/strains with better
performance, and test these under various environmental conditions (mainly
nutritional) to ascertain their potential to maximize overall profitability,
especially under commercial production (Flock et al., 1991). Thus, the
tremendous achievement of animal breeding in developing new broiler
genotypes or breeds for commercial production would not make any economic
sense if it does not result in increased profit margin in poultry enterprises. A
more practical way to realize higher profit with the use of fast growing broiler
6
breeds is for farmers to reduce their cost of production, especially in relation
to feed cost.
To be able to curtail the problem of unbearably high production costs,
local sources of more readily available and lower cost feedstuffs for poultry
rations must be considered. Partial or complete replacement of conventional
feedstuffs such as maize, fish meal and wheat bran in poultry diets will reduce
pressure on them, reduce their cost and increase their availability to small-
scale resource poor farmers (Yangtul, 2010). Several authors, including
McDonald, Edwards and Greenhalgh (1988) and Odoi et al. (2007) have
recommended the use of agro-industrial by-products, of which oil seed cakes
are major examples. One such agro-industrial by-product of the oil seed cake
group is palm kernel oil residue (PKOR) obtained from cottage industries
extracting edible oil from palm kernel (Abeka, 2007). The abundant supply of
this product, coupled with its relatively low cost and favourable nutrient
content, potentially makes it a good alternative feedstuff for poultry. It is
readily available in most parts of the country and is thus worth considering as
a very promising feed ingredient in poultry rations (Yangtul, 2010).
Conceptual Hypotheses
The major hypotheses for the study were that: The readily available,
relatively low-cost and favorable nutrient content of PKOR makes it a good
feedstuff which when included in broiler rations will reduce cost of chicken
production, without affecting growth performance, carcass characteristics, or
haematological and serological traits. Thus, the inclusion of PKOR in broiler
7
rations will not affect the full expression of the genetic potential and the
economics of production of Cobb 500 and Ross 308 broiler genotypes.
Based on the above, the study tested the following specific hypotheses:
1. H0: No significant differences exist in nutritional value among diets
with varying inclusion levels of PKOR.
H1: Significant differences exist in nutritional value among diets
with varying inclusion levels of PKOR.
2. H0: No significant differences exist in the growth, carcass,
haematological and serum biochemical characteristic of birds
fed varying levels of PKOR.
H1: Significant differences exist in the growth, carcass,
haematological and serum biochemical characteristic of birds
fed varying levels of PKOR.
3. H0: No significant differences exist between Cobb 500 and Ross
308 genotypes in terms of their growth performance, carcass
characteristics or haematological and serological traits due to
dietary effect.
H1: Significant differences exist between Cobb 500 and Ross 308
genotypes in terms of their growth performance, carcass,
haematological and serological traits due to dietary effect.
4. H0: There is no genotype x nutrition interaction in performance of
birds on different diets formulated with PKOR.
H1: There is genotype x nutrition interaction in performance of
birds on different diets formulated with PKOR.
8
5. H0: There is no cost-benefit in using PKOR in place of more
expensive traditional alternatives in formulating diets for
broilers.
H1: There is cost-benefit in using PKOR in place of more
expensive traditional alternatives in formulating diets for
broilers.
Objectives of the Study
General objective
The main objective of this study was to assess and compare
performance traits in two commercial broiler genotypes/breeds (Cobb 500 and
Ross 308) on PKOR-based diets in the Central Region of Ghana.
Specific objectives
The specific objectives of the study were:
i. To compare some growth performance characteristics (final live
weight, weight gain, growth rate, and feed conversion ratio) of
Cobb 500 and Ross 308 genotypes at different inclusion levels of
0%, 10% and 20% PKOR in their rations.
ii. To compare some carcass parameters (warm carcass weight,
warm dressing percentage, chilled carcass weight and chilled
dressing percentage) of the Cobb 500 and Ross 308 genotypes
fed varying inclusion levels of PKOR in their rations.
iii. To compare haematological profile and serological profile of the
Cobb 500 and Ross 308 genotypes fed varying levels of PKOR in
9
their rations and to compare the values to normal reference
ranges for chicken.
iv. To determine any possible genotype x ration interaction with
respect to the growth traits, carcass traits, haematological and
serological profile of Cobb 500 and Ross 308 genotypes fed
varying levels of PKOR in their diets, and determine ranking of
these genotypes.
v. To determine the effect of the different inclusion levels of PKOR
on final live weight and FCR and to determine the optimum
inclusion level of PKOR in rations for the two genotypes for
optimum growth based on final live weight (market weight of
2500-3000g at the 8th
week of age) and FCR.
vi. To calculate the feed costs/kg weight gain and savings on feed
cost of the Cobb 500 and Ross 308 broiler genotypes fed varying
levels of PKOR in rations.
10
CHAPTER TWO
LITERATURE REVIEW
Introduction
In this chapter, literatures which were found relevant to the subject
have been reviewed and discussed.
Categories of Phenotypic Traits in Farm Animals
Phenotypic traits are observable characteristics in living organisms and
are mainly influenced by the genetic constitution of the organisms and the
environmental factors to which they are exposed (Falconer & Mackay, 1996).
As reported in the FAO’s draft guidelines on phenotypic characterization of
animal genetic resources (FAO, 2011a), traits in farm animals are generally
grouped into those which show qualitative differences and those which show
quantitative differences.
In qualitative traits, the variation falls into a few clearly defined classes
described as Discontinuous Variation. This is usually due to the fact that such
traits are under the control of one or a few pairs of genes whose final
expression is not greatly influenced by external environmental factors. This
category of traits covers the external physical form, shape, colour and
appearance of animals. These traits are recorded as discrete or categorical
traits. Their discrete expression relates to the fact that they are determined by a
small set of genes. Some qualitative traits (e.g. colour of hair, coat/feather
colour, feather type, horn shape and ear shape) may have less direct relevance
to the production and service functions of farm animals (FAO, 2011a).
However, they may relate to some of their adaptive attributes. For instance,
11
according to FAO (2011a), colour of the skin and hair/coats, and shape of ears
and horns, are known to be relevant to the dissipation of excess body heat.
Length of tail or size of switch in cattle is important in areas where there are
many biting flies. Other traits may be relevant to the preferences or tastes of
livestock keepers and consumers (e.g. colour of hair/coat), and some are used
for animal identification in situations where permanent identification of
individual animals is otherwise impractical (FAO, 2011a).
Quantitative traits, on the other hand, show all manner of slight
gradations from small to larger (described as continuous variation) because of
the numerous genes (polygenes) that determine or influence their expression
(FAO, 2011a; Lynch & Walsh, 1998). This category of traits is much affected
by the environment especially nutrition as indicated by Falconer and Mackay
(1996) and FAO (2011a). Quantitative traits (metric characters) are measures
of the size and dimension of animals’ bodies or body parts including
haematological and serological traits and are more directly correlated to
production traits than qualitative traits. Most economic traits of importance
like birth weight, weaning weight, yearling weight, mature body weight, daily
weight gain (growth rate), feed efficiency, linear body measurement (i.e. body
length, heart girth, height at withers, width at hip, shank length, etc.), udder
size and shape, milk yield, carcass weight, dressing percentage, fleece weight,
egg weight, egg shell thickness, egg size/shape, etc are considered quantitative
(metric) traits and their study requires measurements with standard units due
to their continuous variation as reported by Falconer and Mackay (1996).
12
Expression and Measurement of Phenotypic Traits
Every quantitative or metric character has a value, expressible in the
metric unit by which the character is measured. Examples are milk
production: litres of milk/lactation (yield); feedlot gain: kg/day (growth); litter
size at birth, i.e. number of young born (reproduction). The value observed
when the character is measured on an individual in a population is the
phenotypic value of that individual (Lynch & Walsh, 1998). All observations
must clearly be based on measurements of phenotypic values. In order to
analyse the genetic properties of a population, the phenotypic value has to be
divided into components attributable to the influence of genotype and
environment. The genotype is the particular assemblage of genes (alleles)
possessed by the individual and known to control a particular trait, and the
environment is all the non-genetic factors that influence the phenotypic value
as stated by Lynch and Walsh (1998). The two components of value
associated with genotype and environment are the genotypic value and
environmental deviation.
Symbolically,
P = G + E;
Where P is the phenotypic value
G is the genotypic value, the detectable outward manifestations
of a specific genotype
E is the environment deviation, the combination of all non-
genetic factors that influence the phenotypic expression
13
The genotypic value is decomposed into additive (A), dominance (D) and
epistatic (I) values:
G = A + D + I
Where A accounts for the average effects of individual alleles, D for the
interaction between alleles at each locus (dominance), and I for the interaction
between alleles at different loci (epistasis) as reported by Falconer and
Mackay (1996). Thus G values from different types of relatives share different
amounts of these components, and it is these differences that allow for
inferences about the amount of variation contributed by each of these
components. The genotypic value accounts for genetic effect on the
phenotypic value of a trait. Genetic differences or differences in the genotypic
value of a quantitative (metric) trait within and between populations come as a
result of differences in the effect of genes controlling the trait. The effect of
genes on a trait will vary depending on the number or proportion or percentage
of genes. Thus, the gene frequency determines the genotypic value of the trait
under influence. However, since the genotypic value is a property of the
genotype (gene pair or allelic pair), the genotypic frequency; which is the
proportion or percentage of a particular genotype among individuals, also
determines the genotypic value (Falconer & Mackay, 1996)). In effect, the
genetic properties that cause genetic differences or differences in the
genotypic value of a metric trait between populations are the gene frequencies
and the genotype frequencies. In other words, the gene and genotype
frequencies are the primary sources of genetic difference or variation among
breeds (Falconer & Mackay, 1996; Lynch & Walsh, 1998). Quantitative traits
are known to be polygenic (affected by several genes), hence the larger the
14
number of genes contributing a unit quantity to the trait, the larger the
genotypic value and vice versa (Falconer & Mackay, 1996).
The three components (additive, dominance and epistasis) of the
genotypic value arise as a result of three different types/modes of gene action.
If a trait is controlled by additive gene action, it means that each allele has a
specific value that it contributes to the genotypic value and hence the final
phenotypic value. The effects of each allele are not affected by what other
allele is present at the same locus, nor by what alleles are present at the other
loci. If interaction deviations or effects are zero, the genes concerned are said
to act additively between loci. Thus, additive action may mean two different
things. That is, referred to genes at one locus means the absence of dominance
and referred to genes at different loci means the absence of epistasis. Note that
additivity does not imply equal effects of all alleles at a locus or all loci
affecting a trait (Falconer & Mackay, 1996). Additive gene action (which
produces additive value) is of much importance in quantitative genetics and
animal breeding in general because parents do not pass on their genotypes to
their progeny but rather their genes. Consequently, it is the additive value
(sometimes called the breeding value) of a gene for a particular trait that an
offspring inherits from parents which relates to the narrow sense heritability of
the trait. The breeding value or additive value depends on the gene frequency
in the population (Falconer & Mackay, 1996). The second mode of gene
action is the dominance, which is characterized by interactions between alleles
at the same locus. When genes act in a dominant fashion, the interaction
between alleles at one locus means that the diploid genotype at each locus
needs to be considered as a whole to determine the phenotypic effect. The
15
degree of dominance spans the entire range from complete dominance to over
dominance conferring respective values to the genotypic value and hence the
phenotypic value. Since the average effects of genes and the breeding values
of genotypes, depend on the gene frequency in the population, the dominant
deviations resulting from dominant gene action of alleles or genotypes within
the same locus are also dependent on the gene frequency as well as the
genotype frequency (Falconer & Mackay, 1996). The last gene action is
epistasis; an interaction between alleles at different loci, in which the
phenotype associated with a particular genotype, depends on what alleles are
present at another locus. A seemingly “favourable” allele and its genotype at
one locus may be “unfavourable” in a different genetic background and vice
versa. Loci may interact in pairs or in threes or higher numbers, and the
interactions may be of several different sorts. The deviation which arises from
epistatic interaction is not just a property of the interacting genotypes, but
depends also on the frequencies of genotypes in the population; and so on the
gene frequencies (Falconer & Mackay, 1996). The dominance and epistatic
values are often considered non-additive, such that the genotypic value
becomes the sum of additive and non-additive values.
The genetics of the inheritance of a quantitative character centres on
the study of its variation; for it is in terms of variation that primary genetic
questions are formulated. The basic idea in the study of variation is its
partitioning into components attributable to the genotypic value and the
environmental deviation. The relative magnitude of these components
determines the genetic properties of the population (Falconer & Mackay,
1996; Lander & Schork, 1994).
16
The amount of variation in phenotypic values of a given trait is
measured and expressed as the variance. The components into which the total
phenotypic variance is partitioned are the same as the components of its value.
The total variance is the phenotypic variance (VP) calculated from a set of
observed values; it is the sum of the separate components that is genotypic
variance (VG), which is the variance of genotypic values, and the
environmental variance (VE), the variance of environmental deviations
(Falconer & Mackay, 1996).
Symbolically:
VP = VG +VE
It implies:
VP = VA + VD + VI + VE
Where VA, VD and VI are variances due to additive gene actions, dominant
gene actions and epistatic gene actions respectively. Generally, epistatic
variances are smaller than the additive and dominance variances, even in the
presence of very strong epistasis much of the genetic variation is still loaded
into VA and VD. Further, the coefficients associated with epistatic variances
become smaller and smaller as higher-order epistatic interactions are
considered. The environmental variance may also be decomposed into the
special environmental variance (VES) due to special factors (nutrition,
medication, sanitation, and housing) and the general environmental variance
(VEG) due to general factors (climate and whether conditions) according to
Falconer and Mackay (1996).
Partitioning of the phenotypic variance into its components as shown
above allows us to estimate the relative importance of the various
determinants of the phenotype, in particular the role of heredity versus
17
environment. The relative importance of heredity in determining phenotypic
values is called the heritability of the character. There are, however, two
distinctly different meanings of ‘heredity’ and ‘heritability’, depending on
whether they refer to genotypic values or to breeding values. A character can
be ‘hereditary’ in the sense of being determined by the genotype or in the
sense of being transmitted from parents to offspring, and the extent to which it
is hereditary in the two senses may not be the same. The ratio VG/VP expresses
the extent to which individuals’ phenotypes are determined by the genotypes.
This is called the heritability in the broad sense or the degree of genetic
determination, and is more of theoretical interest than practical importance.
The ratio VA/VP expresses the extent to which phenotypes are determined by
the genes transmitted from parents. This is called the heritability in the narrow
sense, or simply the heritability (h2). The heritability (h
2), VA/VP determines
the degree of resemblance between relatives and is therefore of the greatest
importance in breeding programmes (Falconer & Mackay, 1996).
It follows from the above that the manifestation of phenotypic
expression of a trait can be altered by improving the genotypic value through
planned genetic improvement and through improving the environment i.e.
improved animal management, nutrition and health. Genetic improvement is
slow but its effect is permanent, thus should still be an important part of any
animal husbandry practice. This is because no matter how good animal
management system is put in place i.e. good nutrition, effective disease control
measures, adequate housing/shelter, etc. superior performance will not be
attained if animals are of poor genetic stock as indicated by Lander and
Schork (1994). More rapid improvement can be made in animals’ performance
18
through improved management practices. However, the level of performance
cannot be sustained if the management practices deteriorate. Good
management is necessary for the animals’ welfare and to derive maximum
benefit from the animals. Good animal management includes proper feeding,
maintenance of proper hygienic condition, proper medication, provision of
shelter, and in fact, the general supervision of animals (Falconer & Mackay,
1996; Lander & Schork, 1994).
Genotype by Environment Interaction (G × E)
The phenotypic expression of quantitative traits as observed in animals
is influenced by two main factors – genotype (G) and environment (E), and
sometimes an interaction between the genotype and the environment (G × E).
This is often expressed as phenotype (P) = genotype + environment +
genotype x environment interaction. Consequently, the basic phenotypic
performance model becomes P = G + E + G × E (Falconer & Mackay, 1996;
Cameron, 1997).
Genotype by environment interaction has been defined as the change in
relative performance of a trait expressed in two or more genotypes, when
measured in two or more environments (Falconer & Mackay, 1996). In other
words, genotype by environment interaction (G × E) occurs when
performances of different genotypes are not equally affected by different
environments (Falconer & Mackay, 1996). The ability of living things to alter
the phenotype in response to changes in the environment is known as
phenotypic plasticity or environmental sensitivity (Falconer & Mackay, 1996;
Hammami, Rekik & Gengler, 2009). When the same genotypes develop
19
different phenotypes in different environments, then there is G × E. Hammami
et al. (2009) reported that the existence of G × E could be explained by the
fact that some alleles may only be expressed in some specific environment,
consequently, gene regulation may change depending on the environment.
Favourable genes in some environments may become unfavourable under
other environmental conditions. When the differences between genotypes vary
between environments without changes in their ranking (position in a
hierarchy or scale) there is scaling effect (Hammami et al., 2009). However, if
the genotypes rank differently in different environment, the effect of G × E is
re-ranking of individuals (Hammami et al., 2009). G × E is of less importance
if only scaling effect is obtained because the best selected individuals in one
environment would still perform the best in other environments.
Mathematically, the contribution of G × E to phenotypic variance can
be expressed in the equation: VP = VG + VE + 2covGE + VGE where VP is the
phenotypic variance, VG is the genotypic variance, VE is the environment
variance, 2covGE is the covariance between genotype and environment, and
VGE is the interaction between genotype and environment variance (Falconer
& Mackay, 1996). Inclusion of G × E variance is important when estimating
the heritability of traits. It is known that a high G × E variance component will
result in a low heritability (Kang, 2002). Estimating the genetic correlation of
a trait between environments can help determine the G × E influence (Falconer
& Mackay, 1996). If the genetic correlation between traits is large, then there
is slight G × E effect. However, if the correlation is small, G × E may strongly
influence the performance. It is important to understand the potential
magnitude of G × E when a producer or a farmer is selecting animals for a
20
particular region. For best performance, if G × E is large, it is recommended, if
possible, to use the expected performance for the particular region in which
the animal will produce progeny, instead of where performance measures were
taken to estimate the genetic merit of the animal (Falconer & Mackay, 1996).
These specific predictions have not been fully developed for the animal
industry. The lack of this tool may compromise the producer’s or the farmer’s
ability to maximize their profit (Maricle, 2008).
In the presence of significant genotype by environment interactions (G
× E), the relative advantages of genotypes may differ from one environment to
the other. In some cases it is possible to adjust the environmental conditions to
the requirements for the desired genotype. However, in many cases such
adjustments are either not possible or not cost effective. Rather it becomes
necessary and even useful to choose specific genotypes for specific
environments (Mathur, 2003). The choice of appropriate genotypes and
selection for their further improvement depend upon the nature and magnitude
of the interactions. Therefore, the genotype by environment interactions (G ×
E) require additional considerations for selection and breeding programmes
and offer several opportunities for production of breeding stock specifically
suitable for the desired environmental conditions (Mathur, 2003).
In practical animal breeding and production, genotype may refer to
breeds, lines, strains or progeny groups (Cavero et al., 2011; Razuki & Al-
Rawi, 2007; Yalcin, Settar, Ozkan & Cahaner, 1997), as well as to specifically
differentiated genotypes with respect to major genes or markers, or individuals
such as sires whose progeny have been raised in more than one environment
(Mathur, 2003). The environment is made up of non-genetic factors
21
partitioned into special environment (e.g. diet/feed/nutrition, housing, location,
medication, sanitation and other factors under the control of the farmer or
breeder and which change from farm to farm even within the same locality)
and general environment (e.g. climate or weather conditions, season, etc) not
under the control of the farmer or breeder and which affect all the animals in a
given geographical location (Falconer & Mackay, 1996). The most
economically important environment in broiler chicken production is probably
nutrition or feed or diet (Razuki & Al-Rawi, 2007). Consequently, Hoste
(2007) reported that in terms of the future direction of genetics linked to
nutrition, costs of feed will remain a factor in the economics of production,
and therefore the optimization of feed utilization by birds will remain a
priority to geneticists in making economic decisions.
Methods for Estimating Magnitude of Interactions as Genetic Correlation
Mathur (2003) reported that the methods for estimating the genotype–
environment interactions mainly depend upon the kinds of genotypes and
environments studied in the biometrical sense. The environments can be a few
fixed effects (e.g. location, poultry houses, feeds, etc). The genotypes can be
some fixed effects (e.g. breeds, lines, genetic group) or several random effects
(e.g. sires, individuals, etc). If there are only a few genotypes, the main
interest is in changes in their average performance in reactions to different
environments revealed by genotypic means and interaction deviations. On the
other hand, if there are several genotypes the variance among them (genetic
variance) and interaction variance become relevant and the magnitude of
genotype–environment interactions can be estimated as a genetic correlation
22
between the expression of the genotypes in different environments. This
genetic correlation is expected to be 1 (one) if there are no interactions. The
greater the deviations from 1, the higher are the interactions. The methods for
estimating the magnitude of genotype–environment interactions, as genetic
correlations, have been described by Prabhakaran and Jain (1994) and Mathur
and Horst (1994a). This genetic correlation can either be estimated as an intra-
class correlation or as a product moment correlation between part breeding
values of the same individuals in different environments. The concept of intra-
class correlation is only relevant if there are several randomly chosen
genotypes. It cannot be used when there are only a few of them. The following
univariate factorial model is commonly used to describe the interactions as
stated by Mathur (2003):
Yijk = µ + Gi + Ej + Iik + eijk
Where Yijk is an observation of trait Y on the kth individual of the ith
genotype, jth environment and kth interaction, µ is the general mean, Gi is the
effect of the ith genotype, Ej the effect of the jth environment, Iij the
interaction between the ith genotype and the jth environment, and eijk the
residual effect. The genotypes and the residual effects are assumed to be
random, while the environments are either random or fixed effects.
In the case of few genotypes, the magnitude of interaction effects can
be estimated using least-squares procedure and the statistical significance of
interaction effects can be tested through an analysis of variance followed by an
F-test. If the genotypes are random effects, variance components can be
computed by equating the mean squares to the expectations or by other direct
procedures (maximum likelihood, restricted maximum likelihood, etc.). The
23
interaction variance may be expressed as fraction of genetic variance, sum of
genetic and interaction variance or total phenotypic variance to evaluate their
relative significance (Mathur, 2003).
Nutrient Requirement of Broiler Chickens
Broiler chickens have been bred to convert feed into meat quickly and
more efficiently so that they attain market weight of 2.5-3kg within eight
weeks (Arthur & Albers, 2003). The high rate of productivity of commercial
broiler chickens results in relatively high nutrient needs. Broiler poultry birds
require the presence of at least 38 dietary nutrients in appropriate
concentrations and balance (National Research Council [NRC], 1994). The
nutrient requirement figures published in Nutrient Requirements of Poultry
(NRC, 1994) are the most recent available and should be viewed as minimal
nutrient needs for poultry. These requirements assume that nutrients are in a
highly bioavailable form, and they do not include a margin of safety.
Consequently, adjustments should be made based on bioavailability of
nutrients in various feedstuffs. A margin of safety should be added based on
the length of time the diet will be stored before feeding, changes in rates of
feed intake due to environmental temperature or dietary energy content,
genetic strain, husbandry conditions (especially the level of sanitation), and
the presence of stressors such as diseases or mycotoxins (Chiba, 2014). There
should be sufficient crude protein in the diet of broiler chickens to ensure an
adequate supply of nonessential amino acids especially at the starter and
grower phases. The minimum crude protein (CP %) required by broiler chicks
(0-3 weeks) is 23%, (3-6 weeks) is 20% and (6-8 weeks) is 18%. The
24
metabolizable energy (ME) required by broiler birds form starter to finisher
within thermoneutral zone of 20-27.8oC is 3200kcal/kg. The fibre content of
broiler diet should not exceed 4% of the feed dry matter (NRC, 1994). Table
1 shows the nutrient requirement of broiler chickens from starter to finisher.
Table 1: Dietary Nutrient Requirements of Broilers (90% DM)
Week 0-3 3-6 6-8
Dietary ME Kcal/kg 3200 3200 3200
Protein and amino acids:
Crude protein % 23.00 20.00 18.00
Arginine % 1.25 1.10 1.00
Glycine + Serine % 1.25 1.14 0.97
Histidine % 0.35 0.32 0.27
Isoleucine % 0.80 0.73 0.62
Leucine % 1.20 1.09 0.93
Lysine % 1.10 1.00 0.85
Methionine % 0.50 0.38 0.32
Methionine + cystine % 0.90 0.72 0.60
Phenylalanine % 0.72 0.65 0.56
Phenylalanine + tyrosine % 1.34 1.22 1.04
Threonine % 0.80 0.74 0.68
Tryptophan % 0.20 0.18 0.16
Valine % 0.90 0.82 0.70
Linoleic acid: % 1.00 1.00 1.00
Macro minerals:
Calcium % 1.00 0.90 0.80
25
Chloride % 0.20 0.15 0.12
Magnesium mg 600 600 600
Nonphytate phosphorus % 0.45 0.35 0.30
Potassium % 0.30 0.30 0.30
Sodium % 0.20 0.15 0.12
Trace minerals:
Copper mg 8 8 8
Iodine mg 0.35 0.35 0.35
Iron mg 80 80 80
Manganese mg 60 60 60
Selenium mg 0.15 0.15 0.15
Zinc mg 40 40 40
Fat-soluble vitamins:
Vitamin A IU 1500 1500 1500
Vitamin D3 ICU 200 200 200
Vitamin E IU 10 10 10
Vitamin K mg 0.50 0.50 0.50
Water-soluble vitamins:
Vitamin B12 mg 0.01 0.01 0.007
Biotin mg 0.15 0.15 0.12
Choline mg 1300 1000 750
Folic acid mg 0.55 0.55 0.50
Niacin mg 35 30 25
Pantothenic acid mg 10 10 10
Pyridoxine mg 3.5 3.5 3.0
26
Riboflavin mg 3.6 3.6 3.0
Thiamin mg 1.80 1.80 1.80
Source: NRC (1994)
Diet: The Most Economically Important ‘Special Environment’ in Poultry
Production
In animal nutrition, diet is the sum of food formulated and
compounded from various feedstuffs or feed ingredients (from both animal
and plant sources) to be consumed by a farm animal for growth, development,
maintenance and production (Chiba, 2014). Diet formulation is a very
important aspect of animal production as the success of any animal production
enterprise depends, to a large extent, on proper nutrition and feeding based on
economical diets (Pond, Church & Pond, 1995). Consequently, the owner or
animal production practitioner should have a good knowledge of nutrition,
feeding, the physical and chemical characteristics of feedstuffs, and feedstuff
interactions and limitations, as well as the economics of production. Diet
formulation is a task that matches nutrient requirements of the animal with
combinations of various feed ingredients (Pond et al., 1995).
Some Common Feed Ingredients Used in Poultry Diets
In Ghana, the major feed ingredients of conventional poultry diets are
maize, wheat bran and commercial concentrate (Okanta, Aboe, Boa-
Amponsem, Dorward & Bryant, 2005; Gyamera 2010). Basically, a simple
diet given to poultry would contain 50% maize, 25% wheat bran and 25%
commercial concentrate (Flake & Ashitey, 2008; Okanta et al., 2005). Thus, a
27
simple poultry diet of 100Kg has 50kg of maize, 25kg of wheat bran and 25kg
of concentrate. However, this may not always be the case since farmers
usually use more than three feed ingredients in compounding poultry diets.
This notwithstanding, maize and wheat bran have always constituted
percentages of 40-60% and 20-30% respectively, of the rations, depending on
the number and type of feed ingredients used (Okanta et al., 2005).
Maize contains crude protein (CP) of about 8.5-12% and is a very good
source of energy (about 3350 kcal/kg ME) in poultry ration (NRC, 1994; Pond
et al., 1995). White and yellow maize have similar compositions except that
yellow maize has a much higher content of carotene and xanthophylls, vitamin
A precursors. Both white and yellow maize are fair sources of vitamin E but
low in the B vitamins and devoid of vitamin D. Maize has relatively high
content of Phosphorus but very deficient in Calcium (Pond et al., 1995).
Maize average yield registered by Ministry of Food and Agriculture,
Ghana in 2010 was 1.9 Mt/ha against an estimated achievable yield of around
2.5 to 4 Mt/ha (Ministry of Food and Agriculture-Statistics, Research and
Information Directorate [MoFA-SRID], 2011). There are substantial
opportunities for increased maize utilization for feed mills and the poultry feed
industry in general, following the government’s policy on reducing
importation of frozen chicken by 40% and supporting the local poultry
industry to produce broiler chickens to offset this demand deficit of chicken
meat. Currently, about 30% of maize supplies in the country go into the
poultry feed industry (MoFA-SRID, 2011). In 2008, the government granted
special import permits to import more than 26,000 metric tons of yellow corn
for the poultry feed industry. Over the years, limited supply of maize for feed
28
production led to constraints in the growth of the poultry industry, resulting in
significant growth in imports of poultry and other meats for consumption.
Estimated demand for maize for poultry feed was projected to grow from
73,000 metric tons in 2010 to 118,100 metric tons by 2015 (MoFA-SRID,
2011). Meanwhile, analysts say if Ghana had to grow its own chicken to
replace the current imports, it would need approximately 243,000 metric tons
of maize per year. This provides a huge opportunity for local grain farmers or
companies to move into commercialization to increase their production.
Wheat bran is a by-product of the dry milling of wheat (Triticum
aestivum L.) into flour. It is one of the major agro-industrial by-products used
in animal feeding. It consists of the outer layers (cuticle, pericarp and
seedcoat) combined with small amounts of starchy endosperm of the wheat
kernel. Other wheat processing industries that include a bran removal step may
also produce wheat bran as a separate by-product: pasta and semolina
production from durum wheat (Triticum durum Desf.), starch production and
ethanol production (Heuzé et al., 2013). Wheat bran contains 14-16% protein,
2627 kcal/kg metabolisable energy, 2.5-3.5% fat and 9.5-12% fiber as reported
by Pond et al. (1995). Wheat production occurs worldwide. However,
worldwide production figures are difficult to assess. Wheat production for
human consumption (total supply minus wheat produced for animal feeding,
seed or wasted) was estimated at 456 million tons in 2007 (FAO, 2011b).
Based on bran yield of 10-19 % of the wheat, wheat bran is estimated to range
from 45 to 90 million metric tons. The major producers are the main users of
milled wheat: China, India, United States of America, Russian Federation,
Pakistan, Turkey and France which together are responsible for about 75 % of
29
the production (FAO, 2011b). Currently, Ghana does not produce wheat and
so the wheat milled in Ghana is imported from other countries. Consequently,
wheat bran prices automatically increase when the local currency depreciates
against major international trading currencies.
Fish meal is also commonly used in rations for poultry. Fish meal is a
readily available source of animal protein, and its excellent nutrient values
also complement very well those of other feedstuffs (maize, wheat bran, etc),
provided that the fish meal has been properly processed (Scott & Dean, 1991;
Gyamera, 2010). Fish meal is commonly included at levels of 10-20% in
poultry diets (Esminger, 1992; McDonald et al., 1988). The inclusion rate
depends on the targeted crude protein content of the whole ration calculated
from the theoretical crude protein values of the individual ingredients used in
formulating and compounding the ration. Recently in Ghana, poultry farmers
are gradually moving away from the use of fish meal as source of protein, to
the use of amino acids; mainly, industrially manufactured amino acids such as
lysine and methionine (Donkoh & Atto-Kotoku, 2009). This is due to the poor
quality of locally processed fish meal and high cost of imported fish meal
resulting from the recent depreciation of the Cedi against major international
trading currencies.
Soybean meal is another emerging ingredient in poultry feed in Ghana.
Gyamera (2010) reported that soybean meal inclusion in poultry feed is low
due to high cost; the inclusion level ranges from as low as 10 percent to 18
percent of the total ration for layers and 15 to 25 percent of feed formulation
for broilers (Flake & Ashitey, 2008). Soybean meal is an important source of
dietary protein and energy for poultry throughout the world. However, because
30
much soybean is not grown in Ghana, the price is generally too high for it to
be used extensively in animal feeds. The raw soybean seeds contain a number
of natural anti-nutritional factors when fed to poultry, the most problematic
being trypsin (protease) inhibitors. Trypsin inhibitors disrupt protein digestion,
which results in decreased release of free amino acids. Their presence is
characterised by compensatory hypertrophy of the pancreas due to stimulation
of pancreatic secretions. Fortunately, the heat treatment done during
processing for oil is usually enough to destroy trypsin inhibitors and other
toxins such as lectins or haemagglutinins (Göhl, 1998). The growth depressant
effects of lectins are believed to be due primarily to their damaging impact on
intestinal enterocytes (Pustzai, Clarke, King & Stewart, 1979) and to appetite
depression (Liener, 1986). Moreover, Coon, Leske, Akavanhican and Cheng
(1990) reported that the oligosaccharides, raffinose and stachyose, in soybean
might be anti-nutritional factors. Soybean meal with added DL-methionine is
equivalent to fish meal in protein quality, and economic savings from the
replacement of fish meal can be up to 30% (Gyamera, 2010). Consequently,
poultry farmers and commercial feed producing companies in Ghana are now
increasing the use of locally processed high quality soybean meal in their
rations to reduce total feed cost. This achievement is made possible due to the
increased soybean cultivation in the northern part of the country and the
existence of processing companies such as Ghana Nuts Limited (Tachiman),
Vester Oil Mills Limited (Kumasi), United Edibles Limited (Kumasi) and
Dragon Soya Company Limited (Tema).
Generally, protein feedstuffs and other commercial concentrates
provided by feed companies are usually expensive, can vary considerably in
31
price, and price changes can occur unexpectedly (Flake and Ashitey, 2008;
Apantaku et al., 2006 and Salami, 1995). Advisedly, the use of other by-
products would improve the stability of the poultry production system.
Therefore, it is important to explore the possibilities of utilizing more of
locally available agro-industrial by-products if the nation’s poultry industry is
to be sustainable. Flake and Ashitey (2008) reported that present increases in
feed prices is causing feed manufacturers and many farmers to switch to low
cost substitutes such as palm kernel cake, groundnut cake and copra cake that
are by-products of agro-processing.
Factors Influencing Poultry Farmers Choice of Feeds
In commercial poultry production, feed is the most important variable
input factor (Apantaku et al., 2006). Consequently, it is important for poultry
farmers to familiarize themselves with the various types of feeds available, in
order to make reasonable and responsible decisions about what feeds to
include in their rations (Gyamera, 2010). In order to maximize production and
profit, according to Esminger (1992), farmers must choose feeds that are most
economical for the particular demands of the breed to be fed. Esminger (1992)
noted that “a high-energy, high-protein feed that is fed to low-producing
animals is unnecessarily expensive. Conversely, a low-cost but low-energy
feed that is fed to animals at a high production level will depress potential for
production and should be considered an expensive feed”. It is important,
therefore, that poultry farmers know and follow good feed choice/buying
practices. Likewise, farmers may consider the possibility of using commercial
feeds, all or in part, or not at all, as suggested by Gyamera (2010).
32
Esminger (1992) and Apantaku et al. (2006) identified about eight
factors that may influence farmers’ choice of feeds. These were the quality of
feed, technical ability and knowledge on feed processing methods, cost price
of feed, storage capabilities of the feed, transportation costs, long-term
availability of feeds, government regulations and origin of feeds (farms or
manufacturers).
Feed Quality
Feed quality refers to the amount and types of nutrients an animal can
derive from a particular feed (Chiba, 2014). Some feeds are more valuable
than others; hence, measures of their relative usefulness are important.
Although, Esminger (1992) noted that poultry farmers are not expected to
conduct experiments to evaluate the different feeds that they use, unless they
are very large operators, it is important for the farmers to have a working
knowledge of the value of different feeds from the standpoint of purchasing
and utilizing them.
It is important for farmers to know what constitutes feed quality and
how to recognize it, if they are to produce or buy superior feeds (Esminger,
1992). Farmers need to be familiar with the characteristics of feeds which
indicate high palatability and nutrient content; and if in doubt, observation of
the birds consuming the particular feed will inform them, as birds prefer and
thrive on high-quality feed (Gyamera, 2010). Farmers may use physical
evaluation, chemical analysis and/or biological tests to measure and compare
the quality or value of different feeds, as some feeds are more valuable than
others, and hence, their relative usefulness (Pond et al., 1995).
33
Farmer’s Technical Ability and Knowledge on Feed Processing Methods
The decision as to what type of feed to use will depend on the farmer’s
technical ability and knowledge on feed processing methods. Depending on
the production strategy, farmers may choose between self-compounded or
commercially-compounded feed, self cultivation of feed ingredients or
purchase of the ingredients, among other options (Gyamera, 2010). Whichever
programme is chosen must result in maximum returns for labour, management
capital and overall profitability. It is generally known that self-compounding is
cheaper but the choice for this greatly depends on the farmers’ technical
ability and knowledge on feed processing, and also the size of the farm. Large
farms will warrant the effort of self-compounding (Pond et al., 1995). In
choosing commercially compounded feeds, the farmer must recognize that
there are differences in commercial feeds. Efficient farmers will know how to
determine what constitutes the best in commercial feeds for their specific
needs (Gyamera, 2010). They will not rely solely on the appearance or aroma
of the feed, nor on the salesperson, but will strongly consider the reputation of
the manufacturer (i.e. conferring with other farmers who have used the
particular product before, and checking on whether or not the commercial feed
under consideration has a good record for meeting what it guarantees);
specific needs of the farm (i.e., whether the birds are broilers: - starter, grower
or finisher, or layers); and finally, feed laws (Pond et al., 1995). It is worth
noting that, farmers are conservatives when it comes to patronage of
commercial feeds and would not risk buying feeds that their fellow farmers
have not used and recommended to them, no matter how cheap it may sell.
34
Long-Term Availability of Feeds
Farmers’ choice of feed is highly influenced by the long-term
availability of the feed because of the negative effect of changing feeds on
production levels, especially in the case of laying birds. The unavailability or
irregular supply of a particular feed may compel farmers to move away from it
and find an alternative feed which they can get to buy at all times in order to
stabilize their production levels (Pond et al., 1995).
The Cost Price of Feed
This is a very important consideration, as feed prices vary widely.
Esminger (1992) recommended that for profitable production, feeds with
similar nutritive properties should be interchanged as price relationships
warrant. According to Salami (1995), choice of feed ingredients to formulate
feeds is always influenced by the cost of the ingredients and availability in a
locality. Furthermore, Salami (1995) stressed that increasing cost of feeding is
the greatest problem facing livestock farmers, especially poultry farmers,
because most layer and broiler farmers adopt intensive systems of
management.
In studying the causes of high cost of feed in animal production,
Adejumobi (1999) and Salami (1995) identified and reported the following as
the chief causes:
(a) Competition between humans and poultry for the same feed ingredients
e.g. cereal grains, legume grains, tubers and pulses.
35
(b) Importation of some conventional feed ingredients and imposition of
custom duties on them e.g. fish meal, dicalcium phosphate (DCP) and feed
grade synthetic amino acids.
(c) Irregular and erratic supply of certain feed ingredients due to their
seasonality e.g. cereals.
(d) Excessive use of the scarce and expensive conventional ingredients such as
cereal grains, soybean meal, fish meal and dicalcium phosphate (DCP) at the
expense of cheaper substitutes in the manufacture of compounded feeds.
(e) Inadequate local production of feed ingredients to meet local consumption
by humans and livestock e.g. cereals and legumes.
According to Gyamera (2010), it is for most reasons stated above that
there has been a strong advocacy for the use of agro-industrial by-products
such as PKOR in Ghana, in terms of its abundance; in most cases, PKOR can
be obtained free-of-charge and in few instances, very little amount of money is
offered for it. In this study, the use of PKOR; one of the cheapest, commonest
and high value feed resources in Ghana is considered.
Product Identification –What exactly is PKOR?
In manual or traditional palm kernel oil production, after all the oil has
been skimmed off from the boiling material in the drum or boiler, the residue
is allowed to settle for some days and then the excess water is drained off.
This leaves a semi-solid residue which is then scooped out of the boiler. This
is what is being referred to as palm kernel oil residue (PKOR). PKOR
obtained in this manner has been described by Odoi et al. (2007) to vary in
colour from brown to black, depending on the level of heat applied whilst
36
roasting the kernel or boiling the paste. Furthermore, it is fibrous in nature,
comes out in big wet lumps, and has a distinct pungent smell which
progressively reduces after drying for a period of time. Hutagalung (1981) and
McDonald et al. (1988) also observed that PKOR is bitter, gritty and highly
fibrous. It has been indicated by Odoi et al. (2007) that several of its
characteristics vary depending upon the method of oil extraction adopted. The
residue is thrown away (Plate 1), heaped at a spot close to the processing site,
producing a stench due to the oxidation of the lipids in it, polluting the land
and water bodies and threatening public health (Odoi et al., 2007).
Plate 1: PKOR being poured from the Boiler
Source: Yangtul (2010)
Use of Terminology
Generally, three names are used in literature to refer to the by-product
after palm kernel oil has been extracted. These are Palm Kernel Cake (PKC),
Palm Kernel Meal (PKM), and the more recently used Palm Kernel Oil
Residue (PKOR) by Odoi et al. (2007). The differences in name are
accounted for by the method of extraction and the residual oil content. PKM is
37
usually adopted for solvent extracted by-product which has about 0.5-3.0%
residual oil; and PKC for the mechanically extracted product with about 12%
oil. PKOR is the product described by Odoi et al. (2007) which is obtained
from cottage industry through manual extraction procedures and has oil
content of beyond 18%.
Odoi et al. (2007) were the first to use the term palm kernel oil residue
(PKOR) which has since been adopted by many other researchers and authors
to refer to the by-product of manual palm kernel oil extraction. In this review,
some attributes of PKOR, PKC and PKM will be compared and contrasted.
However, emphasis will be on PKOR, the by-product that will be used in this
study. It is abundant in the Cape Coast Municipality, as well as throughout the
entire country of Ghana (Yangtul, 2010).
Production Methods of Palm Kernel Oil Residue
Fundamentally, palm kernel oil residue (PKOR) and its two other
variants–palm kernel cake (PKC) and palm kernel meal (PKM) are derived
through three methods employed in the extraction of oil from palm kernel
(Yangtul, 2010). These are the manual or traditional (as in many cottage
industries in Ghana), mechanical (using heavy presses in oil mills) and the
solvent method, usually by the use of hexane (Odoi et al., 2007). Boateng et
al. (2008) identified the first two methods as the ones used in cottage palm
kernel oil extraction in Ghana, which result in the production of a variable by-
product called by several names–palm kernel waste, palm kernel chaff, palm
kernel oil residue, etc. The same name may even be used to refer to a similar
by-product quite different in texture and composition (Odoi et al., 2007).
38
Solvent Extraction of Palm Kernel Oil
This involves the use of solvents (such as hexane) in extracting oil
from palm kernel. The solvent extraction process can be divided into three
main operational units: kernel pre-treatment, oil extraction and solvent
recovery from the oil and meal (Yangtul, 2010). The method is ideal for high
capacity mills and is not recommended for small enterprises, because it is
expensive, in terms of maintenance and running cost. The solvent used in this
process usually percolates through the layers of the material being processed,
leading to extraction of the oil. The oil is recovered from the mixture by
distillation and the cake steam purified. The crude palm kernel oil is also
purified using a settling tank or by centrifuging (Odoi et al., 2007). The oil
obtained is bright yellow in colour, rich in lauric acid and has a nutty smell
and taste. The solvent-extracted PKC is much lower in residual oil (about 5%)
than in most traditional methods which tend to be generally inefficient and
usually leave large amounts of residual oil. As a result, solvent extracted PKC
has the advantage of longer shelf-life as it is less prone to rancidity as
compared to most traditionally and mechanically-extracted PKC. Its energy
value is however also lower compared with the residue from manually-
extracted PKC.
Mechanical Extraction of Palm Kernel Oil
The mechanical processes of palm kernel oil extraction can be grouped
into three basic steps: kernel pre-treatment, screw-pressing to extract oil and
oil clarification (Tang & Teoh, 1995). The method suits both small and large-
scale operations. It is more efficient than the cottage or the traditional method.
39
Consequently, the kernel by-product obtained has a lower residual fat and thus
less prone to rancidity. This lower residue fat property also increases its shelf-
life. It is observed that in Ghana, a few palm kernel processing mills
undertake some level of mechanical extraction, using heavy presses to squeeze
out the oil (Odoi et al., 2007).
Manual or Traditional Extraction of Palm Kernel Oil
Production of palm kernel oil in Ghana is primarily carried out by
women in rural or peri-urban localities, in several dispersed cottage industries.
The women work in small groups under very harsh environmental conditions,
extracting palm kernel oil for sale on site to market traders (Yangtul, 2010).
Currently, the kernel oil is the only product of commercial interest. The by-
product is dumped after oil extraction close to the production sites. This
creates nuisance to both processors and neighbouring residents, as it fouls the
air, soil and water bodies (Odoi et al., 2007).
The process of manual extraction of palm kernel oil in cottage
industries commences with the purchase of nuts, mainly during the raining
season when there is abundant supply of the palm kernels and prices are
lowest (Yangtul, 2010). The processors (women) move from house to house
in the oil palm processing towns buying and bulking the kernels for palm
kernel oil production. The nut processing and oil extraction consist of drying
of kernels, cracking, shell separation and oil extraction (Odoi et al., 2007).
Odoi et al., (2007) and Yangtul (2010), in describing the process stated
that the assembled or heaped kernels are usually sifted to remove as much
foreign materials (e.g. stones and chaff) as possible. Nuts are then spread out
40
in the sun to dry over a period of several days (7-14 days). This allows shells
to easily detach from the kernel during cracking. Cracking of the nuts is done
mechanically in a hammer mill, to obtain a mixture of kernels and shells. The
kernels are sifted from shells using the principle of varying densities; the
mixture of kernels and shells is poured into a viscous mixture of clay and
water in a barrel. The heavier shells sink while the lighter kernels float, and so
can be scooped out. Any mud on the kernels is washed off with water and the
kernels air-dried. Residues of shells, small stones and chaff remaining on the
kernels are removed by hand and through winnowing. From time to time,
shells at the bottom of the barrel are removed dried and used as fuel in the
processing of oil. Odoi et al. (2007) further indicated that the clean, dry
kernels are roasted/fried in large pots to which a little oil has been added; this
is done with continuous stirring for about 2 hours or until kernels are dark
brown in colour. After roasting, the kernels are drained of excess oil and
again spread out to air-dry. The kernels are next milled into a paste using a
motorized hammer mill. The paste is poured out into barrels containing water
in a ratio of 3 parts water to 4 parts paste. The mixture is further heated at a
temperature of 100-120oC for about 30 minutes, with continuous stirring. This
forces the oil to float to the top which is then skimmed off. Heating and
skimming off of oil goes on for some time (over a period of 3-4 days, but not
continuously). After this period, cold water is added to the mixture and
allowed to cool over a few days, without any further heating. Any residual oil
left in the mixture floats and is also skimmed off. All the oil recovered from
the mixture is further boiled to get rid of any residual water. Plates 2-5 show
stages in the manual extraction of oil from palm kernels.
41
Some Stages in the Manual Extraction Process
Plate 2: Roasting of Palm Kernels
Source: Yangtul (2010)
Plate 3: Milling Roasted Palm Kernel into Paste
Source: Yangtul (2010)
Plate 4: Heating Milled Paste
Source: Yangtul (2010)
42
Plate 5: Skimming of Oil from Heated Paste
Source: Yangtul (2010)
Management/Handling of PKOR for Use as Poultry Feed
Fresh PKOR contains more than 50% moisture; to curtail deterioration
of the product, the high moisture content should be removed as quickly as
possible. Odoi et al. (2007) therefore advised that for successful use of PKOR
as animal feed, preservation methods such as sterilization with heat and
elimination of as much water as possible from the material to prevent
microbial growth in storage are necessary to stabilize the product. Since
aflatoxin production and rancidity are favoured by high moist conditions,
rapid drying after production, and maintaining low levels of moisture during
storage, are important requirements to slow down chemical deterioration that
will promote rancidity and thus shorten the shelf-life of PKOR. A fast and
thorough drying is known to be a simple and cheap method to eliminate
moisture and also deactivate enzymes, to prevent the product from going
rancid (Odoi et al., 2007).
An important observation made by Odoi et al., (2007) and Yangtul
(2010) was that fresh PKOR is prone to spoilage because all the factors
43
necessary for mould (aflatoxin) production are readily present in the
environment where it is produced. The high moisture content (>50%), high
residual oil (>13%), coupled with the high environmental temperatures during
the dry season (when it is mostly produced), favour aflatoxin production and
rancidity. Hence, it is very important to quickly dry the product after oil
extraction. To achieve an effective drying, and consequently a good feedstuff
(PKOR), the fresh large wet lumps should be broken up, spread out and dried
in the sun over a period of 5–7 days depending on the intensity of the sunlight
(Odoi et al., 2007; Yangtul, 2010). The product should be spread out on iron
or aluminum sheets, or on a concrete surface (Plate 6) to speed up the drying
process. In the process of drying, any foreign material still present should be
removed by hand or sieved out. The product should also be protected from
rain and overnight dew. It is recommended that the final product should be
stored in sacks until ready for use.
Plate 6: Sun Drying of PKOR on Concrete Floor
Source: Field Data, 2012
The Potential of PKOR
PKOR is free from aflatoxin when properly dried and stored and thus
safe for animal feeding. It is also free from any toxic chemicals, heavy metals
44
pesticides and dioxins. When properly dried, microorganisms and mould
growth are discouraged; this optimizes intake and growth or productive
performance in poultry and livestock (Yangtul, 2010).
PKOR when treated and stored well is highly palatable to animals,
particularly poultry (Yangtul, 2010). Its low content of unsaturated fatty acids
also reduces rancidity problem when dried and stored properly. Interestingly,
recent findings have also demonstrated that inclusion of PKC and its variants
in poultry diets improve the health and immunity of the birds (Sundu, Kumar
& Dingle, 2006)
PKOR is abundant in most parts of Ghana. Places (mainly urban and
semi-urban communities) where it is abundant happen to coincide with areas
where more poultry are raised. It is also produced throughout the year and this
guarantees its supply and availability for use as a feed ingredient for poultry
and other animals.
PKOR is likely to prove cost-effective and so a practical ingredient to
be utilized in ration formulations for various livestock species. It is virtually
free as at now, and is therefore more economical under local dietary and
management systems compared to non-PKOR based diets.
Nutritional Merits of PKOR and its Variants (PKC and PKM)
Although, PKOR supplies both protein and energy, it is looked upon
more as a source of protein than energy (Chin, 1995; McDonald et al., 1988).
PKOR also contains good levels of most major and minor minerals. The levels
of some of the nutrients might vary widely, depending on the method of
extraction and the source of the palm kernel from which the PKOR is
45
obtained. The data presented in Table 2 compare the proximate analysis of
PKOR and PKC from two studies.
Table 2: Comparison of Nutrient Composition of PKC and PKOR
Fraction Sundu et al., 2006 (PKC) Odoi et al., 2007
(PKOR)
Dry matter, % 94 54.56
Crude protein, % DM 14-21 18.94
Crude fibre, % DM 21-23 17.32
Lipid, % DM 8-17 13.05
Ash, % DM 3-6 3.67
MJME/kg DM - 16.23
The moisture content of PKOR is very high, accounting for almost 50
% of the fresh product; however, the levels of other nutrients present in the
two products are very similar (Table 2).
Crude Protein Content (CP)
The CP content of PKC ranges from 7.7 to 18.7%, depending on
processing methods and the degree of impurities such as shell content
(Jalaludin, 1996). Chin (1995) observed that CP levels of solvent- extracted
PKC were more constant, (ranges between 15.0 and 15.3%) than that of the
expeller-pressed PKC (which ranges between 14.6 and 16.0%). However,
Odoi et al. (2007) reported crude protein content of up to 18.9% for PKOR
and 15.6% for the mechanically pressed PKC from samples collected in Cape
Coast, Ghana.
46
Amino Acid Availability
Reports by Yeong et al. (1983) and Hutagalung et al. (1982) suggest
that the amino acid composition of palm kernel cake (PKC), a variant of
PKOR is rather low (Table 3). However, the availability of the amino acids in
PKC has been rated by other authors to be very high. For example, Nwokolo,
Bragg and Kitts (1976) had indicated that all the essential amino acids were
available in excess of 85%, except for valine which was only 68.4% available.
Table 3: Percentage Amino Acid Composition of Some Commonly Used
Feed Ingredients in Ghana, Including PKC a Variant of PKOR
Amino Acid % PKC1 PKC
2 Maize
3 Wheatbran
3 SBM
3
Arginine 2.18 2.40 0.46 1.02 4.24
Cystine 0.20 - 0.21 0.34 0.95
Glycine 0.82 0.84 0.32 0.80 1.05
Histidine 0.29 0.34 0.24 0.39 1.36
Isoleucine 0.62 0.61 0.29 0.54 22.55
Leucine 1.11 1.14 1.03 0.98 4.63
Lysine 0.59 0.61 0.27 0.66 3.19
Methionine 0.30 0.34 0.10 0.22 0.77
Phenylalanine 0.73 0.74 0.41 0.79 2.85
Serine 0.69 0.77 0.38 0.64 2.75
Threonine 0.55 0.60 0.28 0.48 2.09
Tryptophan 0.17 0.19 - - -
Tyrosine 0.38 0.47 0.34 0.46 2.10
PKC1 – Source: Yeong et al. (1983), PKC
2 – Source: Hutagalung et al. (1982)
3 – Source: Donkoh and Atto-Kotoku, (2009)
47
Additionally, comparing the amino acid composition in PKC to some
commonly used ingredients in livestock and poultry rations in Ghana, it could
be seen from Table 3 that PKC (a variant of PKOR) compares well with wheat
bran, which is also viewed sometimes as a medium grade protein source, and
is used often to increase the crude fibre content of compounded feeds. PKC is
superior to wheat bran in all the essential amino acids except cystine, histidine,
lysine and phenylalanine. PKC is superior to maize in all amino acids but
inferior to soya bean meal. This observation suggests that, all other factors
being held constant, PKC or PKOR can conveniently replace wheat bran in
most compounded poultry feeds in Ghana.
Fat Content (EE)
The ether extract (EE) or fat content for PKC ranges from 0.5–3% (in
solvent-extracted cake) to 5–12% (in the expeller-pressed cake) (Chin, 1995).
However, Odoi et al. (2007) reported oil content of up to about 23% in the
PKC obtained from a source using a mechanical method, and 13% for the
locally extracted PKOR in Ghana. The high fat content makes PKC and
PKOR good energy sources (Hishamuddin, 2001); however the oil is mostly
saturated (Hutagalung, 1982). Studies by Rhule (1996) have shown that this
property is somehow beneficial as palm kernel cake with high level of residual
fat induced higher average daily gain, better feed conversion efficiency, with
reduced leanness, in pigs, and improved the performance of laying hens, as
reported by SenkÖylü et al. (2004).
On the other hand, the high oil or fat content of PKOR makes it highly
prone to rancidity in storage and rejected by animals when they are fed with it.
48
Generally, fats go rancid when they undergo changes in storage that leads to
production of unpleasant tastes, odours and eventually spoilage. Fats and oils
begin to decompose the moment they are isolated from their natural
environment. Rancidity results when the carbon-carbon bonds in the
polyunsaturated fatty acids are broken down due to atmospheric oxidation.
The process is accelerated by exposure to heat, light and moisture; Rancidity
leads to the emission of unpleasant odours, thus PKOR has both an unpleasant
taste and flavour. This altered taste and smell are due to aromatic products
from break down of unsaturated fatty acid in the residual oil contained within
the PKOR (Wardlaw, 1991). It is also observed that the greater the degree of
unsaturation of the fat, the greater it is liable to oxidative rancidity. Though
palm kernel oil is high in saturated fatty acids, there is also a considerable
proportion of unsaturated fatty acids present, making it liable to rancidity.
PKOR therefore has a sour taste as well as a very strong and pungent smell,
upon exposure to light, heat and moisture (Hartley, 1977). These changes in
taste, colour and odour as a result of oxidative rancidity contribute to make
fats less palatable and even unsafe for feeding to farm animals. It is also noted
that even though rancid feeds are potentially toxic, their unpleasant odour and
taste generally discourage animals from eating them. Rancidity therefore tends
to reduce feed intake drastically, thus adversely affecting performance and
growth in animals (Tung, Cook, Wyatt & Hamitton, 1975).
Crude Fibre
Another property of PKOR that is worth mentioning is the moderate
fibre content. Odoi et al., (2007) reported crude fibre content of 17.32% for
49
PKOR. Meanwhile, the fibre content of PKC (a variant of PKOR) of
Malaysian origin was reported to be as low as 3.9 % on dry matter basis
(FAO, 1998). The moderate fibre content of PKOR makes it suitable for
replacing wheat bran in poultry rations. Table 4 compares the proximate
values of wheat bran and PKOR as the latter is used to substitute the former in
poultry diets (Odoi et al, 2007; Yangtul, 2010).
Table 4: Nutrient Composition of Wheat Bran and PKOR on DM Basis
Nutrient 1Wheat bran
2PKOR
MEMJ 6.86 16.23
CP% 15.82 18.94
Fat% 3.8 13.05
CF% 9.6 17.32
Ash% - 3.67
Sources: 1(Okai, Olympio, Bonsu & Sam, 1994) and
2Odoi et al., 2007
Mineral Elements
PKOR is very rich in both macro and micro nutrients. Table 5 shows
the mineral composition of PKC from different authors (Yeong et al., 1983;
Chin, 1991) and PKOR from field data. Although marked differences might
exist in the levels of other nutrients, especially crude protein and fat content,
the values obtained for the mineral elements are fairly stable and similar for
the by-product obtained from different extraction methods.
50
Table 5: Mineral Element Composition of PKC and PKOR
Mineral 1PKC
2PKC
3PKOR
Calcium, % 0.29 0.25 0.38
Phosphorus, % 0.79 0.52 0.78
Magnesium, % 0.27 0.16 0.30
Iron, mgkg-1
4.05 4.05 5.05
Copper, mgkg-1
28.5 28.5 29.30
Zinc, mgkg-1
77.0 77.0 58.60
Manganese, mgkg-1
225.0 225.0 -
Sources: 1Yeong et al. (1983),
2Chin (1991) and
3Field Data (2012)
The high phosphorus to calcium ratio in the PKC and its variant PKOR
makes them good choices for poultry feed. Phosphorus and calcium elements
are critical nutrients in the feed, not only as the major elements forming the
mineral basis of bones, but also as the key minerals required in biochemical
energy transformation in all body cells (Hishamuddin, 2001).
PKOR as a Possible Maillard Reaction Product
The processing method (roasting the kernels at temperature of 140-
180oC and heating the paste at temperatures of 100-120
oC; researcher’s
personal field data, 2012) exposes PKOR to possible Maillard reaction,
resulting in its characteristic aroma and brown to dark brown colour. Maillard
reaction is a non-enzymatic interaction between reducing sugar and amino
acid, peptide or protein, resulting in a variety of by-products, intermediates
and brown products (melanoidins), which contribute markedly to aroma, taste
and colour, as well as the antioxidant potential of stored and processed foods
(Manzocco, Calligaris, Mastrocola, Nicoli & Lerici, 2011). This reaction was
first described by Louis Camille Maillard in 1912 when he observed the
51
formation of brown pigments during heating of glucose and Lysine. The
nutritional value of feed ingredients may be reduced during storage and
processing (Friedman, 1996). This is likely a consequence of a combination of
heat and humidity that leads to the Maillard reaction, which starts with the
condensation between an amino group (NH2) of an amino acid or protein and a
carbonyl group (C=O) of a reducing sugar. Lysine is an essential amino acid
that has a ɛ-amino group that easily condenses with the carbonyl group of a
reducing sugar (Nursten, 2005). When the Maillard reaction occurs, Lysine
availability is reduced (Pahm, Pedersen & Stein, 2008; Boucher, Pedersen,
Stein & Schwab, 2009). During amino acid analysis, however, Lysine is
partially recovered leading to an overestimation of the available Lysine.
Because of this overestimation, standard amino acid analysis procedures may
not be adequate to determine the amount of available Lysine in feed
ingredients that have been heat processed. Therefore, it is believed that
analysis of reactive Lysine is more accurate than standard Lysine analysis
(Boucher et al., 2009). Some of the factors affecting the rate of Maillard
reactions products formation are temperature, pH, type of substrate, and water
activity. Each of these factors may affect the kinetics of the reactions in
specific ways.
Effects of Consuming Maillard Reaction Products
The consumption of Maillard Reaction Products (MRPs) has increased
in recent decades and there is evidence that these substances are absorbed and
may participate in pathological processes such as, cataracts, diabetes,
degenerative diseases, atherosclerosis and chronic renal failure. The
52
consumption of diets rich in MRPs negatively affects protein digestibility
(Friedman, 1996).
The method of processing PKOR makes it a possible Maillard reaction
product and hence its protein or amino acid availability and digestibility may
be negatively affected. Lysine and Methionine are essential amino acids that
are likely to be reduced during the processing of PKOR as a Maillard reaction
product. Consequently, it is advisable to use PKOR in combination with non-
Maillard reaction product feed ingredients rich in lysine and methionine such
as soya bean meal, fish meal, blood meal and bone meal to supplement the
lysine, methionine and other essential amino acid content of the diet.
PKOR/PKC Feeding Trials in Poultry
A number of feeding trials carried out in poultry led to the observation
that there is a wide variation in the optimum inclusion level of PKOR and its
close variants (PKC and PKM) in poultry rations. The reasons assigned for
these variations are mainly due to the origin and differences in the oil and shell
content of the PKOR/PKC used. However, it is said that the different
recommendations from studies may not be due to the inclusion level of these
feedstuffs in the diet per se but to an imbalance in a number of nutrients,
particularly amino acids (Yangtul, 2010). The imbalance or reduced amino
acid nature of PKC/PKM/PKOR might be as a result of varying degrees of
Maillard reactions which occur during processing of these products. Hence,
PKOR/PKC should be used in combination with feedstuffs high in essential
amino acids or with industrially manufactured amino acids such as Lysine and
methionine.
53
PKOR/PKC Feeding Trials in Broiler Chickens
It has been observed that broilers can tolerate up to 20% PKOR in their
diets without adverse effect on their growth performance and feed efficiency
(Yeong et al., 1983). A feed conversion ratio of 1:0.48 was reported for
broilers fed palm kernel cake (PKC) at 35 days of age (Onifade & Babatunde,
1998).
In Ghana, Osei and Amo (1987) investigated effects of PKC at
different inclusion rates of 0%, 5%, 7.5%, 10%, 12.5%, and 15% in broiler
chickens at Akropong Farms, Kumasi. In their study, it was concluded that
there were no significant differences (p>0.05) between treatments, with
regards to feed consumption, weight gain and feed conversion efficiency.
Odoi et al, (2007) substituted wheat bran with PKOR at 0%, 5%, 10%,
and 15% in a grower-finisher broiler diet. The outcome was that there was an
increase in body weight gain and average feed consumption up to the 10%
level of inclusion.
Growth Response of Broiler Chickens to PKOR/PKC/PKM Based Diets
PKOR and its close variants (PKC and PKM) are known to influence
the growth performance of broiler chickens at different inclusion rates.
According to Soltan (2009), inclusion of PKC at 5%, 10%, 15% and
20% of broiler chickens diet showed non significant (p>0.05) reduction in
their final body weight (FBW) by about 3.9%, 5.6%, 10.3% and 8.7%
respectively when compared with a control, indicating that FBW was
negatively related to the dietary inclusion levels of PKC. Soltan (2009) further
stated that the reduction of FBW with increasing PKC inclusion levels in
54
broiler chick diets may be attributed to the lower nutrient digestibility with
PKC inclusion; this explanation is supported by Sundu and Dingle (2003) who
had earlier reported that during processing, PKC may also undergo Maillard
reaction (the reaction of mannose with amino groups leading to the formation
of a brown complex) due to extreme heat applied in the processes before and
during oil extraction, which may adversely affect the digestibility. Also the
results are in agreement with Ojewola and Ozuo (2006) who reported that
birds fed on diets containing 10%, 15% and 20% of PKC instead of soybean
meal, had depressed body weights when compared with the control. Ezieshi
and Olomu (2008) indicated that PKM (mechanically extracted) non
significantly depressed broiler chick weight while other PKM types highly
depressed FBW. In contrast, Okeudo, Eboh Ndidi, IZugbekwe and Akanno
(2005) stated that average body weight of broilers was approximately 2 kg in
each dietary group at the 8th
weeks of age, and was not significantly affected
by inclusion of PKC up to 30% of the diets. These differences may be related
to the degree of Maillard reaction in the different PKM used by the authors
since all reactions up to the formation of Amadori compounds at the initial
stage of Maillard reaction are reversible, to release amino acids for utilization
by the chicks. Also, Okeudo, Onyike, Okoli and Chielo (2006) reported that
the final live weights of broilers fed the 0%, 15% and 30% PKC diets were
similar (approximately 1.9–2.0 kg) and were significantly (P<0.05) higher
than the live weights of broilers reared on 45% PKC diet (1.5 kg). Further
report by Egenuka, Opara, Okoli and Okeudo (2013) who studied the effect of
different dietary levels (0%, 20% and 40%) of PKC on the growth of chickens
55
indicated that there were significant (P < 0.05) increases in the final live
weight of the growers with increases in the level of palm kernel cake.
PKC inclusion at 5%, 10%, 15% and 20% non significantly (P>0.05)
reduced daily body weight gain (DBG) by about 3.7%, 5.6%, 9.5% and 9.1%
respectively when compared with a control (Soltan, 2009). This result is
contrary to that of Shakila et al. (2012) who evaluated the effect of PKM (at
levels 0, 2.5, 5.0, 7.5 and 10.0 %, either alone or in combination with enzymes
(0.05%)) on the performance of broilers and reported that the inclusion of
PKM, with or without enzymes, did not have any significant effect on the
body weight gain compared to the control. Their finding is in conformity with
the findings of Onwudike (1986), Osei and Amo (1987), and Ezieshi and
Olomu (2004). Further, the body weight gains were apparently improved in
broilers fed the PKM diets together with enzymes. The moderate improvement
in weight gain might be due to improved fibre digestibility by the exogenous
enzymes which is in consonance with the findings of Ojewola et al. (2003).
Regarding daily feed intakes (DFI), Soltan (2009) further reported that
PKC inclusion at 5% and 10% non-significantly increased DFI by about 2.5%
and 8.1% respectively, when compared with control, while the higher PKC
levels increased (P<0.05) DFI by about 13.5% and 11,7%. The slightly lower
DFI with 20% PKC inclusion, compared with DFI by broiler chicks fed on
diets containing 15% PKC, may be due to the diet becoming unpalatable. On
the other hand PKC inclusion (at 5%, 10%, 15% and 20%) in broiler chick
diets reduced feed conversion ratio (FCR) by about 5.8%, 16.5%, 25.6% and
23.0% respectively, when compared with the control. Higher DFI by broiler
56
chicks fed on diets containing PKC may be attributed to the lower
metabolizable energy content of that diet; the higher DFI with depression of
FCR are in agreement with those obtained by Ojewola and Ozuo (2006) who
observed that broilers fed 15% PKC in their diet exhibited higher feed intake
and feed to gain ratio when compared with controls and other broiler chick
groups fed on lower levels of PKC. Also, Ezieshi and Olomu (2008) reported
that there was higher DFI by broiler chick fed on mechanical pressed palm
kernel. However, the data Soltan (2009) are in contrast with Ezieshi and
Olomu (2004) who observed that no significant differences in DFI between
broiler finishers fed 0%, 34% and 44.95% PKC diets. Also, Okeudo et al
(2006) recorded that average DFI was similar across the different dietary
inclusion levels of PKC (0, 15, 30 and 45%) for broiler chickens during the
finisher period. PKC inclusion in broiler diets at 5, 10, 15 or 20% reduce both
protein efficiency ratio (PER) and efficiency of energy utilization (EEU) by
about (4.4%, 11.1%, 18.5% and 17.3%) and (4.5%, 11.7%, 20.5% and 16.7%)
respectively, when compared with control. There were indications that DBG
and PER were negatively related to the dietary inclusion levels of PKC
(Soltan, 2009).
Carcass Characteristics of Broilers on PKC/PKOR Based-Diets
Soltan (2009) observed that PKC dietary inclusion at different levels
had no effect on dressing percent and liver relative weight when compared
with the control; this is similar to what has been reported by Shakila et al.
(2012). While, gizzard size was non significantly increased (P>0.05) with 5%
57
inclusion of PKC, it was highly increased (P<0.05) by about 34.6%, 37.6%
and 45.8% with 10%, 15% and 20% PKC addition in broiler diets respectively
when compared with control. This is in accordance with Okeudo et al. (2005)
who stated that gizzard size was significantly increased by the inclusion of
PKC in the broiler diet. Moreover, the higher gizzard size with inclusion of
PKC in broiler diets may be related to the higher dietary fiber content (Deaton,
Kubena, Reece & Lott, 1977; Onwudike, 1986). A well-functioning gizzard
should be large and muscular to grind especially fibrous feed and able to retain
feed components. This, in turn, results in better regulation of digestive
processes, leading to improved digestibility of nutrients (Hetland & Svihus,
2007).
In regards to immune organs, PKC inclusion at 5% of broiler diets non
significantly increased spleen relative weight by about 18% while,
significantly (P<0.050) improved spleen relative weight with PKC addition at
10%, 15% and 20% by about 36.4%, 27.3% and 45.5% respectively when
compared with control (Soltan, 2009). In a study conducted to evaluate the
effects of Palm Kernel Meal (PKM) replacing maize at inclusion rate of 0%,
10%, 20%, 30% and 40% in broiler diets supplemented with or without
enzyme (Maxigarin®) as replacement for Maize in broiler diets, Esuga,
Sekoni, Omage and Bawa (2008) reported that the weight of the heart in the
control diet was similar to the weight of the heart of all enzyme (Maxigrain®)
supplemented diets at all levels of PKM inclusion. However, the heart weight
of birds in all PKM diets without the enzyme supplementation was
significantly (p<0.001) higher than birds with enzyme supplementation at all
levels of PKM inclusion. Also, the weight of liver in the control was similar to
58
10% and 20% diets with Maxigrain® and 10% without Maxigrain®. At all
levels of PKM inclusion, the liver weights in Maxigrain® supplemented diet
were significantly (P<0.001) lower than those in the unsupplemented diets
where the size of the liver was observed to increase with increasing levels of
PKM without Maxigrain® supplementation. Furthermore, Esuga et al. (2008)
reported that the weight of kidney was similar among control, 10, 20 and 40%
PKM diets with Maxigrain® and 10% PKM without Maxigrain® but
significantly (P<0.001) lower than the other treatments. The size of the kidney
was observed to increase with increasing levels of PKM in the
unsupplemented diets compared with the Maxigrain® supplemented diets. The
increase in the weight of visceral organs (heart, liver and kidney) of birds with
increasing levels of PKM in the unsupplemented diets compared with the
Maxigrain® supplemented diets could be attributed to accumulation of
oxidative products (high levels of aldehyde and other oxidized metabolites)
due probably to the high oil content of the PKM that was used (Cherian,
Wolfe & Sim, 1996; Wang et al., 1997). Higher weight of visceral organs may
be signs of abnormality and effect of high oil feeds (Cherian et al., 1996).
Haematological Response of Broiler Chickens to PKOR/PKC
Haematology refers to the study of the numbers and morphology of the
cellular elements of the blood – the red cells (erythrocytes), white cells
(leucocytes), and the platelets (thrombocytes), and the use of these results in
the diagnosis and monitoring of disease (Merck Manual, 2012). The blood
transports or conveys nutrients and materials to different parts of the body.
Therefore, whatever affects the blood (e.g. drugs, pathogenic organisms or
59
nutrients) will certainly affect the entire body positively or adversely in terms
of health, growth, maintenance and reproduction (Olabanji, Farinu, Akinlade
& Ojebiyi, 2007). A readily available and fast means of assessing clinical and
nutritional health status of animals on feeding trials may be the use of blood
analysis, because ingestion of dietary components has measurable effects on
blood composition (Church, Judd, Young, Kebay & Kim, 1984; Maxwell,
Robertson, Spences & McCongrouodala, 1990). This may be considered as
appropriate measure of long term nutritional status of animals (Olabanji et al.,
2007). According to Togun and Oseni (2005), haematological studies have
been found useful for disease prognosis, and for therapeutic and feed stress
monitoring. Adamu, Thomas, Iseh, Fatihumi and Esieno (2006) observed that
nutrition had significant effect on haematological values. Togun et al., (2007)
reported that when haematological values fall within the normal range
reported for the animal, it is an indication that diets do not show any adverse
effect on haematological parameters; but when the values fall below the
normal range, it is an indication of anaemia. Low values for haematological
parameters, as reported by (Bawala, Akpan, Ogunnowo, Fasae & Sogunle,
2007), could be due to the harmful effects of high dietary contents.
Haematological Components and Their Functions
Blood, which is a vital special circulatory tissue, is composed of cells
suspended in a fluid intercellular substance (plasma) with the major function
of maintaining homeostasis (Isaac, Abah, Akpan & Ekaette, 2013).
Haematological components, which consist of red blood cells (RBC), white
blood cells (WBC) or leucocytes, mean corpuscular volume (MCV), mean
60
corpuscular haemoglobin (MCH) and mean corpuscular haemoglobin
concentration (MCHC) are valuable in monitoring feed toxicity especially
with feed constituents that affect the blood as well as the health status of farm
animals (Oyawoye & Ogunkunle, 2004; Etim, Enyenihi, Williams, Udo &
Offiong, 2013).
Red Blood Cells: Red blood cells (erythrocytes) serve as a carrier of
haemoglobin. It is this haemoglobin that reacts with oxygen carried in the
blood to form oxyhaemoglobin during respiration (Johnston & Morris, 1996;
Chineke, Ologun & Ikeobi, 2006). According to Isaac et al. (2013), red blood
cells are involved in the transport of oxygen and carbon dioxide in the body.
Thus, a reduced red blood cell count implies a reduction in the level of oxygen
that would be carried to the tissues as well as the level of carbon dioxide
returned to the lungs (Ugwuene, 2011; Isaac et al., 2013). Packed Cell Volume
(PCV) which is also known as haematocrit (Ht or Hct) or erythrocyte volume
fraction (EVF), is the percentage (%) of red blood cells in blood (Purves,
Sadava, Orians & Heller, 2003). According to Isaac et al. (2013) Packed Cell
Volume is involved in the transport of oxygen and absorbed nutrients.
Increased Packed Cell Volume shows a better transportation and thus results
in an increased primary and secondary polycythemia. Haemoglobin (Hb) is the
iron-containing oxygen-transport metalloprotein in the red blood cells of all
vertebrates (Maton et al., 1993) with the exception of the fish family,
channichthyldae (Sidell & Brien, 2006), as well as tissues of invertebrates.
Haemoglobin has the physiological function of transporting oxygen to tissues
of the animal for oxidation of ingested food so as to release energy for the
other body functions as well as transport carbon dioxide out of the body of
61
animals (Ugwuene, 2011; Soetan, Akinrinde, & Ajibade, 2013; Isaac et al.,
2013).
Packed Cell Volume, haemoglobin and mean corpuscular haemoglobin
are major indices for evaluating circulatory erythrocytes, and are significant in
the diagnosis of anaemia and also serve as useful indices of the bone marrow
capacity to produce red blood cells in mammals (Awodi et al., 2005; Chineke
et al., 2006; Peters, Gunn, Imumorin, Agaviezor & Ikeobi, 2011).
Furthermore, Chineke et al., (2006) posited that high Packed Cell Volume
(PCV) reading indicated either an increase in number of Red Blood Cells
(RBCs) or reduction in circulating plasma volume. Mean corpuscular
haemoglobin and mean corpuscular haemoglobin concentration indicate blood
level conditions. A low level is an indication of anaemia (Aster, 2004).
White Blood Cells: The major functions of the white blood cell and its
differentials are to fight infections, defend the body by phagocytocis against
invasion by foreign organisms, and to produce or at least transport and
distribute antibodies in immune response. Thus, animals with low white blood
cells are exposed to high risk of disease infection, while those with high
counts are capable of generating antibodies in the process of phagocytocis and
have high degree of resistance to diseases (Soetan et al., 2013) and enhanced
adaptability to local environmental and disease prevalent conditions
(Okunlola, Olorunisomo, Aderinola, Agboola & Omole, 2012; Iwuji &
Herbert, 2012; Isaac et al., 2013).
Blood Platelets: Blood platelets are implicated in blood clotting. Low
platelet concentration suggests that the process of clot-formation (blood
62
clotting) will be prolonged resulting in excessive loss of blood in the case of
injury (Merck Manual, 2012).
It has also been established that certain haematological factors can be
associated with certain production traits (Mmereole, 2008). For example, high
Packed Cell Volume (PCV) and hemoglobin (Hb) contents are associated with
high feed conversion efficiency (Mitruka & Rawnsley, 1977), while high
percentages of white blood cells (WBC), especially lymphocytes, are
associated with the ability of the chicken to perform well under very stressful
conditions. Consequently, the effects of any feed ingredient on the
haematological indices of chickens are of immense assistance in deciding
whether or not such a feed ingredient should be used as poultry feed stuff.
Table 6 shows the normal range of values for haematological parameters of
chicken.
Table 6: Normal Range of Values for Haematological Parameters in
Chicken
Parameter Units Normal Ranges
PCV % 22.0-35.0
Hb g/dl 7.0-13.0
RBC X106/μl 2.5-3.5
WBC X103/μl 1.2-3.0
MCV
MCH
MCHC
Fl
Pg
%
90-140
33.0-47.0
26.0-35.0
Source: Reference values of Jain (1993)
63
The table 7 shows the effects of dietary levels of PKC on the
haematological profile of pullets, as reported by Egenuka et al., (2013)
Table 7: Effects of Dietary Levels of PKC (a Variant of PKOR) on
Haemtological Profile of Pullets
Parameter 0% PKC 20%PKC 40%PKC SEM
Haemoglobin (g/100ml) 7.80 5.78 7.58 0.56
PCV (%) 23.00 17.00 22.00 1.73
RBC (x106/µl) 3.96 3.36 3.76 0.20
MCV (fl) 20.00 17.00 21.00 0.82
MCH (pg) 197.26 170.69 201.47 6.96
MCHC (%) 19.00 16.50 19.50 0.91
Total WBC (x103/µl) 4.20 4.60 4.50 0.29
Heterophils (%) 39.00 45.50 35.00 5.20
Lymphocytes (%) 58.00 52.00 61.00 4.65
Eosinophils (%) 3.00 2.50 4.00 0.87
Source: Egenuka et al., (2013)
From table 7, hemoglobin (Hb) values of 7.80 and 7.58g/100 ml,
recorded in the 0% and 40% PKC groups are within the normal range (7.0 –
18.6 g/100 ml) stated by Mitruka and Rawnsley (1977) for chickens; but the
5.78g/100 ml recorded by the 20% PKC group was a little lower, though the
differences were not significant (P > 0.05).
64
The values for packed cell volume (PCV) ranged from 17 - 23 %
(Table 7). These values were lower than 24.9 – 45.2 % stated for healthy
chickens by Mitruka and Rawnsley (1977), for Nigerian indigenous chickens
and 30 - 40% for laying hens (Esonu et al., 2006).
The red blood cell (RBC) counts from the results of Egenuka et al.
(2013) ranged from 3.36 - 3.96 (x106/μl). The values for mean corpuscular
volume (MCV), mean corpusscular hemoglobin (MCH) and mean corpuscular
hemoglobin concentration (MCHC) were 17 - 21 fl, 170.69 - 201.47 pg and
16.5 – 19.5 % respectively. These values differed from those reported for
laying hens by Olorede and Longe (2000). Their figures for RBC, MCV,
MCH and MCHC were 1.66 – 1.80 (x106/µl), 169.09 - 172.00 fl, 46.9 - 49.1
pg and 29.0 - 29.2 % respectively. The differences may be attributed to age as
they worked with older laying pullets while the birds used in the study of
Egenuka et al. (2013) were pre-laying pullets of 18 weeks of age.
The values of Egenuka et al. (2013) for WBC ranging from 4.2 - 4.6
(x103/l) were lower while the values for RBC, 3.36 - 3.96 (x10
6/μl),
heterophils (35.0 - 45.5 %), lymphocytes (52.0 - 61.0 %) and eosinophils (2.5
– 4.0 %) were within the normal range for a healthy chicken (Mitruka &
Rawnsley, 1977). However, Esonu et al. (2006) reported a WBC count of 8.0 -
9.3 (x 103/l) and a lower lymphocyte count of 41.0 – 46.0 % for laying hens.
The differences may be due to the differing ages of the birds. Evidence
suggests that hematological test results may vary depending on age, handling
conditions, the immediate environmental conditions and many other factors
(Fischbach & Dunning, 2004). Hematological values were slightly lower in
the 20% PKC group than the 0% and 40% PKC groups, except for the total
65
WBC and heterophils levels that were slightly higher. The MCHC of the 20%
PKC group was lower than that of the 0% and 40% PKC groups. Nicoll et al.
(2001) made a similar observation as they reported that MCHC was decreased
under high WBC conditions. The hemoglobin and RBC values obtained in the
study of Nicoll et al. (2001) indicate that the birds were healthy and not
anaemic.
Ugwuene (2011) also conducted a trial on the replacement value of
dietary palm kernel meal for maize on the haematology of Nigerian local
broiler turkeys. Six treatment diets in which palm kernel meal replaced maize
at 0, 20, 40, 60, 80 and 100 percent were formulated. There was no definite
trend in the haematological values for the birds with increase in the level of
replacement of maize with PKM. However, in most of the haematological
indices, turkeys fed 40 and 60 percent PKM diets performed well as those on
0% PKM diet. Consequently, Ugwuene (2011) recommended that PKM can
replace maize up to 60 percent in the diets of turkey without adverse effects on
haematology of the animals.
Effect of Genotype/Breed on Haematology
In a study on haematological parameters of rabbit breeds and their
crosses in humid tropics, Chineke et al. (2006), reported that genotype
influence on PCV, WBC, MCH and ESR; RBC, HBC and MCHC values were
identical in all genotypes, pointing to similar cellular haemoglobin content in
blood samples obtained. In a study conducted by Peters et al. (2011) on
variation in haematological parameters of Nigerian native chickens; normal-
feathered birds had higher mean values compared to frizzled feather and naked
66
neck genotype. Peters et al. (2011) observed some strain differences which
were consistent with Agaie and Uko (1998), Islam et al. (2004) and Chineke et
al. (2006) strengthening the argument for interest of genetic differences.
According to Peters et al. (2011), sufficient genetic variation therefore exists
for haematological parameters among Nigerian native chickens that may
represent indicator traits for study.
Isaac et al. (2013) in a study on haematological properties of different
breeds of rabbit (Chinchilla, New Zealand White and Dutch) reported that
Chinchilla had the highest value for WBC, lymphocytes, monocytes, RBC,
Hb, PCV and MCV. New Zealand White had the highest value in MCHC and
MCH while Dutch had the highest values in neutrophils, eosinophils,
basophils and platelets. Schalm, Jain and Carroll (1975) reported significant
breed differences in haematological values for New Zealand White and Wild
Jack rabbit. Ologunowa et al. (2000) however, reported no significant breed
effect on the blood parameters of rabbits, in their study in Nigeria. Durai,
Maruthai, Arumugam and Venugopal (2012) conducted a study on
haematological profile and erythrocyte indices of different breeds of chicken
and observed variation in results which was suggested to be due to differences
in breeds. Durai et al. (2012) further documented that the significant
differences in haematological profile and erythrocyte indices among the
different breeds of chicken can be considered as reference values and may
serve as a guide to assess the state of health in the monitored birds. Ekiz and
Yalcintan (2013) in a study on haematological parameters in goat kids
reported that breed had significant effect on PCV. Schalm et al. (1975) stated
67
that haematological studies of farm animals either showed significant or no
significant breed effect.
Serum Biochemical profile
Serum biochemical profiling has been used in several species of farm
animals to monitor herd/flock health status and to detect subclinical disease
(disease which is not severe enough to present readily observable symptoms)
according to Jain (1993). Glucose, cholesterol, calcium, total protein, alkaline
phosphates, uric acid, sodium, potassium, chloride levels are diagnostic values
for diabetes mellitus, liver disease, hypoparathyroidism, chronic hepatopathy
and liver disease, gout, kidney disease, chronic diarrhoea and dehydration,
respectively (Islam et al., 2004). Managing abnormalities in birds requires an
understanding of how diseases change the biochemical function of the blood
system. Because the clinical signs of illness in birds are frequently subtle,
clinical chemistry is necessary to evaluate cellular changes (Ritchie, Harrison
& Harrison, 1994). Islam et al. (2004) affirmed that the comparison of blood
chemistry profile with nutrient intake might indicate the need for adjustment
of certain nutrients upward or downward for different population groups. In
effect, for proper management, breeding, feeding, prevention and treatment of
diseases, it is desirable to know the normal physiological values in chickens in
order to attribute changes in these values to possible factors (Islam et al.,
2004).
Total plasma proteins are a common parameter utilized to estimate the
avian body condition. It is generally known that blood plasma proteins play
key roles in the maintenance of colloid osmotic pressure, as a rapid substitute
68
for indispensable amino acids, assuring glucose through gluconeogenesis, in
transport of minerals and hormones, in forming enzymes and the immune
system in the organism (Shakila et al., 2012). Therefore, blood plasma
proteins have an exceptional significance in homeostasis maintenance.
Moreover, albumin, one of the main serum proteins, serves as the most
favourable source of amino acids for synthesis of tissue proteins in the period
of quick somatic growth of birds, especially under feed restricted conditions
(Yaman, Kita & Okumura, 2000; Filipowiæ, Stojeviæ, Milinkoviæ-tur, Ljubiæ
& Zdelar-tukM, 2007). According to Shakila et al. (2012) the total serum
protein (4.68-4.89mg/dl) in broilers fed with PKM at different levels (0, 2.5,
5.0, 7.5 and 10.0 %) was not significantly affected with or without enzymes
supplementation and it might be ascribed to adequate protein levels in the diets
that were able to support normal reserve of the proteins in the body as
explained by Adesehinwa (2007). This finding was in consonance with the
observations made by Olorede, Onifade, Okpara and Babatunde (1996) in
broilers and further agrees with the total protein reference range 3.0 - 4.9mg/dl
(Clinical Diagnostic Division, 1990).
Alkaline phosphatase (ALP) is mainly produced by intestinal mucosa,
liver, bone, kidney, and placenta among which the intestinal ALP does not
contribute much to ALP serum levels. Szabó et al. (2005) reported that
reduced activity of ALP may be an indication of slow down of bone growth.
Higher serum levels of ALP are observed when there is increased osteoblastic
activity, involving formation and mineralization of bone associated with
increased skeletal growth (Lumeij, 1997). The serum alkaline phosphatase was
not significantly affected by PKM inclusion up to 10% in diets indicating that
69
PKM can safely be incorporated in the broiler diets without any adverse
effects on liver function (Shakila et al., 2012).
Glucose values are important in predicting diabetes mellitus
conditions and function of insulin in animals; higher glucose above normal
may indicate the presence of diabetes and very low level may indicate low
energy. Glucose, as the end product of carbohydrate digestion, indicates the
level of energy content of a given feed sample and hence the energy available
to the animal. Egenuka et al. (2013) reported that glucose level was slightly
higher in 40% PKC group than in the 0% and 20% PKC groups with glucose
values of 234.50 mg/dl, 195.00 mg/dl and 137.50 respectively. The high
glucose level may be linked to the slightly higher energy of the feed consumed
by the birds in 40% group. However, the glucose value recorded for the 40%
was within the normal range 197-299mg/dl (Clinical Diagnostic Division,
1990), where as those of the 0% and 20% were slightly lower than normal,
indicating lower energy levels of the feed given to the birds in these groups.
Cholesterol and triglyceride values are usually positively correlated
and are used to assess the function of the heart and cardiovascular diseases.
The findings of Egenuka et al. (2013) again indicated that their values for
cholesterol (120 – 156 mg/dl) were not significantly different for varying
levels of PKC and were within the normal range of 129 – 297 mg/dl
documented by Clinical Diagnostic Division (1990) except the 0% PKC (120
mg/dl) which was slightly lower than normal due probably to the low oil/fat
content of the feed.
Measures of serum urea, creatinine, calcium, sodium, potassium and
chloride are used to check whether or not the kidneys are functioning properly.
70
The kidneys process creatinine which is a waste product. So elevations could
indicate a problem with kidney function. Too much calcium in a bloodstream
could indicate kidney problems; overly active thyroid or parathyroid glands;
problems with the pancreas; or a deficiency of vitamin D, whereas normal
values of calcium is an indication that the integrity of the kidney is maintained
as reported by Ibrahim, Aliyu, Wada and Hassan (2012). Normal chloride
levels also mean that there is no dehydration, kidney disorders, or adrenal
gland dysfunction. The normal nerve impulses and muscle contractions are
also regulated by mineral sodium.
Egenuka et al. (2013) reported 10.0 mg/dl Calcium for both 0% PKC
and 20% PKC, whiles 12.0 mg/dl was recorded for 40% PKC. The higher
calcium level recorded by the 40% PKC group may be a result of the slightly
higher calcium level of the feed consumed by this group. However, all the
values obtained were within the reference range provided by Clinical
Diagnostic Division (1990), suggesting that there were no kidney problems
when birds were fed up to 40% PKC in diet.
Effect of Genotype/Breed on Biochemical Profile of Chickens
Ibrahim et al. (2012) studied the
effect of sex and genotype on blood serum electrolytes and biochemical
profile of five Nigerian indigenous chicken genotypes (dwarf, Fulani ecotypes,
naked neck, frizzle and normal feathered) and reported significant genotype
effect. The results revealed serum levels of sodium, potassium, chloride, uric
acid, glucose, total protein, creatinin, albumin and globulin were
145.23±27.18 mmol/L, 8.05±2.39 mmol/L, 106.33±11.27 mmol/L, 3.57±1.47
71
mg/μL, 44.87±17.57 mg/dL, 72.20±8.42 g/L, 74.50±12.98 μmol/L,
38.30±4.84 g/L and 33.30±5.95 g/L respectively. The serum chloride,
potassium, globulin, glucose and uric acid were significantly (p<0.05)
different across the genotypes. Serum chloride and glucose were higher in
dwarf chickens, whilst potassium was higher in neck chickens and higher
globulin levels were observed in frizzle chickens compared to the other four
genotypes. Serum chloride levels were significantly (p<0.05) different, with
the dwarf chicken having the highest value (112.67±3.77 mmol/L) and the
naked neck recording the least (96.67±10.99 mmol/L). The serum chloride
level was not different from the reference range 108-124 mmol/L (Clinical
Diagnostic Division, 1990).
Sodium is present in the extracellular fluid and is primarily responsible
for determining the value of the extracellular fluid and its osmotic pressure.
Serum sodium levels in the Nigerian indigenous chicken did not differ
significantly among genotypes and the values obtained were within the normal
range of serum sodium in mature birds of 139-155 mmol/L (Clinical
Diagnostic Division, 1990). Mary-Priya and Gomathy (2008) reported that
blood glucose increases until maturity and then subsequently decrease
gradually throughout the bird’s life. Across the genotype, a significant
difference (p<0.05) was observed. However, the values recorded were lower
than the normal range of 197-299mg/dL (Clinical Diagnostic Division, 1990),
probably due to age. Dwarf chickens had a greater glucose level of 62.84±6.68
mg/dL compared to frizzle (41.00±6.16 mg/dL), Fulani ecotype (42.00±2.03
mg/dL), naked neck (36.50±2.25 mg/dL) and normal feathered birds
(42.00±5.42 mg/dL). The higher glucose level in dwarf compared with the
72
other breeds is an indication of breed difference due to differences in genetic
composition. The findings of Ibrahim et al. (2012) is however, in contrast with
that of Ladokun et al. (2008) who reported no significant (p>0.05) differences
in total protein, albumin, urea, glucose and cholesterol levels between naked-
neck and normally feathered genotypes of Nigerian indigenous chickens in a
sub humid tropical environment. Meanwhile, the report of Ladokun et al.
(2008) is in consonance with the reports of earlier workers (Mitruka &
Rawnsley, 1977; Clubb & Schubot, 1991; El-Safty, Ali & Fathi, 2006).
The Table 8 shows normal reference values for some serum electrolyte
and biochemical parameters for all chickens.
Table 8: Normal Reference Values for Some Serum Electrolyte and
Biochemical Parameters for all Chickens
Parameter/unit Reference ranges
Potassium (mmol/l) 1.7 – 4.2
Sodium (mmol/l) 139 – 155
Chloride (mmol/l) 108 – 124
Total protein (mg/dl) 3.0 – 4.9
Glucose (mg/dl) 197 – 299
ALP (U/l) 10 – 106
Uric acid (mg/dl) 1.9 – 12.5
Calcium (mg/dl) 8.1 – 12
Cholesterol (mg/dl) 129 – 297
Source: Clinical Diagnostic Division (1990)
73
Genotype–Environment Interactions in Broilers
Broilers are marketed at an early age compared with layers; therefore,
they have a shorter period of exposure to the environment and less chance of
genotype–environment interactions. However, there is evidence of significant
genotype–environment interactions in broilers, especially with respect to
environmental conditions such as heat stress and nutrition (Mathur, 2003).
Genotype–environment interactions in broilers with respect to heat
stress have been investigated and these have given evidence of significant
interactions of the naked-neck, frizzle and dwarf genes with ambient
temperature (Cahaner, 1990; Cahaner, Deeb & Gutman, 1993; Deeb &
Cahaner, 1999, 2001; Petek, Yalcin, Turkmut, Ozkan & Cahanar, 1999; Yunis
& Cahaner, 1999). Deeb and Cahaner (2001) studied the effects of normal
(25°C) and high (30°C) ambient temperature on broiler progeny of hens from
a sire line and two dam lines, differing in growth rate and meat yield, carrying
the naked-neck (Na) gene. The advantage of the Nana genotype was much
more pronounced at high ambient temperature in broilers, with genetically
higher growth rate and breast meat yield.
Petek et al. (1999) studied the effect of genotype–environment
interactions on the performance of commercial broilers in western Turkey.
The genotypes were 29 sires and natural climatic conditions in spring and
summer were considered as environments. The interaction was evaluated as
correlations between sire breeding values in summer with those estimated
from their spring offspring. The genotypes that gained more weight in the
spring gained less weight under the hot conditions of summer. The correlation
between the two seasons for weight gain from 0 to 4 weeks of age was 0.26,
74
i.e. significantly lower than 1. It was even negative, though not significantly
lower than 0, for weight gain from 4 to 7 weeks of age and body weight at 7
weeks of age. The analysis of variance revealed highly significant genotype-
season interaction effects on all traits. They also observed that this variation
was somewhat related to growth potential.
Interactions between broiler genotypes and heat stress have also been
investigated in several tropical locations. Singh, Choudhuri, Chandra, Malik
and Singh (1998) compared the performance of naked neck (Nana) and
normal (nana) broilers in winter and summer in India. The naked-neck broilers
were superior to normal broilers in terms of growth rate, feed efficiency,
dressing percentage and liveability in both seasons, but the difference between
the two genotypes was higher in summer than in winter. The results show that
the naked-neck genotypes were more suitable for the tropical climatic
conditions and their superiority was greater with increasing heat stress. The
results observed in layers also apply to the broiler populations, though the
magnitude of the interactions again depends upon the differences between the
environmental conditions.
Ali, Katule and Syrstad (2000) studied the importance of genotype–
environment interactions in broilers and layers in Tanzania. The genotypes
were four commercial broilers: White Plymouth Rock, Tanzania Local fowls
and crosses between White Plymouth Rock and Tanzania Local fowls. The
environments were two rearing systems: intensive and extensive management
systems from 10 to 18 weeks of age. The effect of interactions was evaluated
on live weight, body weight gain, feed intake, carcass weight and
gastrointestinal traits. The broilers had the highest live weight and fastest gain
75
on both systems, but performed much better under intensive than on extensive
management system. Tanzanian Local fowls had the lowest weight and
slowest gain. The live weight of the crosses was higher than the average of
their parents. The intensively reared fowls gained about four times more
weight than those on extensive rearing. Feed intake, carcass weight and
intestinal length followed the same trends as live weight. The genetic groups
ranked similarly in the two rearing systems, suggesting that genotype–
environment interactions were of little practical importance (Mathur, 2003).
An experiment to investigate the effect of heat stress on
haematological parameters was conducted by Pingel, Hailu, Dang, Al-
Mahrous and Lengerken (1995) on divergently selected lines from White Rock
fowls for plasma corticosterone concentration for three generations, and an
unselected control line, after heat stress (2 h at 40°C). Heat stress decreased
the leucocyte count in all groups. Antibody production against sheep
erythrocytes was similar in fowls of the three lines kept at normal and high
temperatures. However, the genotype–environmental temperature interactions
were not significant.
Genotype–nutrition interactions in broilers were reviewed by Leenstra
(1989). This review considered the effects of genotype × dietary composition
(protein and fat) and genotype (strains) × temperature interactions on broiler
performance. The interaction effects are especially important when the
genotypes differ in protein metabolism, body composition and protein
efficiency. There is a need for diets of specific composition for optimal
performance of the desired genotype according to Mathur (2003).
76
Costs and Benefits of Using PKC/PKOR
There is evidence that the use of PKC and its variant PKOR in feed
compounding has led to significant reduction in feed cost and increased the
financial benefit or profit margin of users. Yangtul (2010) replaced wheat bran
with PKOR and his cost-benefit analysis is shown in Table 9
Table 9: Cost and Benefit Analysis of Feeding Layers Varying Levels of
PKOR-Based Rations
Parameter Treatments
0% PKOR 5% PKOR 10% PKOR 15% PKOR
Feed cost/bird/day (gh¢) 0.060 0.058 0.057 0.056
Mean daily feed intake (g) 117.68 116.75 116.30 116.03
Number of days on feed 84 84 84 84
Number of birds on diet 24 24 24 24
Total feed cost (GH¢) 120.96 116.93 114.91 108.06
Mean daily egg prod. 20.38 20.76 20.36 19.30
Total egg weight (kg) 96.8 99.9 97.7 95.1
Feed cost/kg egg (GH¢) 1.25 1.17 1.18 1.14
Price/crate of eggs (GH¢) 4.59 4.59 4.59 4.59
Value of eggs (GH¢) 261.92 266.81 261.67 250.23
Net Revenue (GH¢) 140.96 149.88 146.76 142.17
Cost-Benefit ratio 0.46 0.44 0.44 0.43
Source: Yangtul (2010)
The cost-benefit ratio (CBR) was calculated as the total feed cost
divided by value of eggs for a particular diet.
Thus, CBR= Total feed cost (GH¢)/ Value of eggs (GH¢)
77
The CBR provides some basis for decision-making and as a rule of thumb, the
lower the CBR the higher the returns on a particular investment option. Feed
cost was significantly reduced by the inclusion of PKOR and translated to a
reduction in feed cost per kilogramme of egg produced. The control diet came
out the most expensive in terms of total feed cost, feed cost per kilogramme
egg produced, and gave the highest cost-benefit ratio of 0.46. The results also
showed that the 5% PKOR-based diet yielded the highest value for eggs
(GH¢266.81) and net revenue of GH¢149.88. However, the PKOR diet
treatments came out with similar cost-benefit ratios, with the 15% PKOR-
based diet recording the lowest cost-benefit ratio of 0.43. This implies that for
every GH¢1.00 revenue generated, GH¢0.43 was cost of feed and GH¢0.57 as
net revenue to the farmer (Yangtul, 2010). However, for the control diet, every
GH¢1.00 revenue generated, GH¢0.46 was feed cost and GH¢0.54 was net
revenue. The difference of GH¢0.03 between the GH¢0.57 net revenue of the
15% PKOR-based diet and the GH¢0.54 net revenue of the control (0%
PKOR) is the savings on feed cost.
Odoi et al. (2007) also reported significant reduction in feed cost when
they fed up 15% of PKOR to broiler finisher chickens. Similarly, Egenuka et
al. (2013) reported 295.91 Naira (N), 247.92 Naira (N) and 211.06 Naira (N)
as average feed cost/kg gain (N) for 0% PKC, 20% PKC and 40% PKC levels
respectively in the diet of pullets, indicating a significant (P < 0.05) reduction
in feed cost as PKC levels increased in the diet. The feed cost per kg live
weight gain was significantly less in broilers fed PKM with or without
enzymes compared to control (Shakila et al., 2012). A similar finding was
observed by Osei and Amo (1987).
78
CHAPTER THREE
METHODOLOGY
Introduction
The chapter explains in details the materials and methods used to
conduct the research. It covers experimental site, duration of experiment,
experimental animals, experimental design, experimental diet composition,
data collection procedure and data analysis.
Experimental Site
The experiments in this study were conducted on the Teaching and
Research Farm of the School of Agriculture, University of Cape Coast, Cape
Coast, Ghana. The experimental site is located in the south-western part of
Ghana, with annual temperature range of 24oC and 34
oC, and relative
humidity of between 50% and 85%. The area has a bi-modal rainfall pattern,
averaging annually 800mm to 1500mm. Throughout the study period, the
mean temperature recorded in the experimental room was 25.60C (range of
23oC to 26
oC), which was within the thermoneutral zone of the birds (20
oC-
27.8oC). Temperature measurements were taken at 9.00 am, 12.00 noon and
3.00 pm daily.
Phases and Duration of Experiments
The experiments were in three phases with each phase investigating
different category of performance parameters.
79
Phase 1 was a feeding trial which lasted for five (5) weeks, from July 7
to August 10, 2012, after the birds have been brooded on commercial diet for
three (3) weeks from June 15 to July 6, 2012.
Phase 2 was carcass evaluation which lasted for twenty-four hours,
from 9:00 am of 10th
August, 2012 to 9:00 am of 11th
of August, 2012.
Phase 3 was haematological and serological evaluation which lasted
for three hours (9:00 am-12:00 noon) on August 10, 2012.
Experimental Animals
Two hundred and fifty (250) Cobb 500 broiler day-old chicks and two
hundred and fifty (250) Ross 308 broiler day-old chicks were imported from
Holland through Agro Kamm Farms.
Experimental Design
Two groups of 250 day-old chicks each were brooded separately in a
brooder house with two compartments in order to avoid mixing up of the two
breeds or genotypes. After brooding for three weeks, 225 Cobb 500 and 225
Ross 308 broilers were selected and divided into six treatment groups, with 75
birds in each treatment; each treatment had three replicates of 25 birds each.
The experiment was a 3×2 factorial experiment with three replications, where
factor A represented feeding treatment with three levels (A1-A3) and factor B
represented two breeds or genotypes (B1 and B2). The Completely
Randomized Design (CRD) was used. The birds were randomly selected and
assigned to eighteen pens (with twenty-five birds in each pen). The broiler
birds in each treatment were fed on one of three diets containing 0%, 10% and
80
20% of PKOR, corresponding to treatments A1-A3 (CP levels of 19.00-
19.45%) respectively with the PKOR directly replacing wheat bran.
Housing
After brooding, the three-week old broiler chicks were housed in
eighteen deep litter pens with wood shavings as the litter. The floor
dimensions of each pen were 2.5m x 2.1m giving a standard floor space of
0.21m2
per bird. Each pen (plate 7) contained one galvanized aluminum feeder
of 15Kg capacity and one plastic water drinker of 11.2Kg capacity.
Plate 7: A pen with 3-week old birds
Management/Handling of PKOR for Use as Livestock Feed
Fresh PKOR was collected from a processing site at Abura, Cape
Coast (about 6Km away) and transported to the School of Agriculture
Teaching and Research Farm for stabilization and use in compounding the
poultry rations. It was sun-dried for a period of about 5 days at an average
daily temperature of 30oC. The fresh product was spread out on iron or
galvanized aluminum sheets, hot clean concrete surface and black polythene
sheet at a depth of 2.5-3cm, to speed up the drying process. In the process of
drying, lumps were broken up, foreign materials still present were removed
81
and constantly stirred by hand after every two hours. The dried PKOR was
stored in sacks and used for compounding the broiler finisher rations.
Feeding
Birds in each group were fed once daily at 7.00-8.00 am on one of
three experimental rations containing 0%, 10% and 20% of PKOR. Both feed
and water were supplied ad libitum. The birds fed under 24-hour lighting
regime.
82
Analysed Proximate Composition of Experimental PKOR
Table 10 shows the proximate values obtained from laboratory analysis
of the PKOR that was used to compound the experimental rations. Generally,
the values are similar to those obtained by Odoi et al. (2007)
Table 10: Proximate Composition (on Dry Matter basis) of PKOR Used in
Experimental Rations
Parameter Value
Dry matter (DM)% 92.32
CP% 16.65
CF% 13.25
EE% 12.43
Ash% 3.35
Ca% 0.38
P% 0.62
Mg% 0.30
Fe% 0.05
Cu% 2.93×10-3
Zn% 5.86×10-3
*ME (Kcal/Kg) 3293.87
*Calculated
83
Proximate Composition of Experimental Broiler Finisher Concentrate
and PKOR Used to Formulate the Rations
The chemical composition of the major feed ingredients used in
formulating the experimental ration are shown in Table 11
Table 11: Proximate Composition (on DM Basis) of Individual
Ingredients Used in Experimental Rations
Ingredient %CP %CF %EE %Ash Metabolisable
Energy (ME)
*Broiler Finisher
Concentrate
32 3 5 - 2480 Kcal/Kg
# PKOR 16.65 13.25 12.43 3.35 3293.9 Kcal/Kg
Key
* Label on Commercial Product
# From Chemical Analysis
Composition of Experimental Rations (% of 100 Kg Weight)
Table 12 shows the feed ingredient composition of the experimental
rations. With the exception of wheat bran and PKOR, which were varied, a
constant value was used for all other ingredients for all the treatments.
84
Table 12: % Composition of Experimental Rations and their Proximate
Analysis
Ingredient 0% PKOR
n=3
10% PKOR
n=3
20% PKOR
n=3
SED p-value
Maize 50.0 50.0 50.0
Wheat Bran 28.5 18.5 8.5
Fish Meal 12.0 12.0 12.0
Commercial Broiler
Concentrate
8.0 8.0 8.0
PKOR 0.0 10.0 20.0
Oyster Shells 1.0 1.0 1.0
Vitamin Premix 0.3 0.3 0.3
Salt 0.2 0.2 0.2
TOTAL 100.0 100.0 100.0
Proximate analysis
of diets
Dry Matter% 89.20a 92.98
b 93.95
b 0.55 0.001
Crude Protein (CP)% 19.01a 19.48
ab 20.25
b 0.25 0.007
Fat (EE)% 3.42a 3.73
a 4.69
b 0.13 0.001
Crude Fibre (CF)% 5.13a 6.03
b 6.53
c 0.12 0.001
Ash% 9.33a 10.36
ab 11.16
b 0.44 0.001
*ME (Kcal/Kg) 2,840.13a 2,920.86
b 2954.75
b 22.4 0.007
Means in a row with same letter superscripts are not significantly different (p>0.05)
*Calculated
85
Feed Analysis
Proximate composition of the experimental rations and PKOR were
carried out in the Nutrition Laboratory of the School of Agriculture according
to the methods of AOAC (2000). Metabolisable Energy (ME) values were
calculated according to the equation of NRC (1994) as:
ME (Kcal/Kg) = 35×Protein (%) + 85×Fat (%) + 35×NFE (%); whereas NFE,
% nitrogen-free extract = 100-(%moisture + %CP + %EE + % CF +
%Ash/Minerals). The proximate values and energy were subjected to one-way
analysis of variance (ANOVA) at probability level of 0.05 using the
generalized linear model of the Genstat Discovery Edition (VSNI, 2011) and
where significant (P < 0.05) differences existed, means were separated using
Least Significant Difference (LSD).
Vaccination and Medication Schedule
A tally chart was opened for the experimental birds from day old till
four weeks of age. Standard preventive and curative healthcare medication
was administered.
Table 13: Vaccination and Medication Schedule Followed
Date Medication Means of
administration
15/06/2012 Glucose In drinking water
20/06/2012 Coccidiosis vaccination In feed
29/06/2012 1st Newcastle vaccination In drinking water
08/07/2012 Gumboro vaccination In drinking water
13/07/2012 Fowl Pox vaccination In drinking water
86
Collection of Blood Samples
At the end of the eighth week, birds were subjected to 12 hours fasting
prior to blood samples collection. Three birds from each replicate were
randomly selected and thus a total of fifty-four birds from the three treatments
were picked up for blood samples to be taken to determine haematological and
serological profile of the birds. Blood was collected from the birds by
venipuncture of the wing vein using a sterile syringe and needle. Blood
samples (2 ml each) for haematological analysis were collected in ethylene
diamine tetra-acetic acid (EDTA) treated bottles while samples (3 ml each) for
serum biochemical analysis were collected in bottles without the
anticoagulant. The samples were quickly stored in an ice box, using icepacks
and transferred to the laboratory for analyses, within three hours post
sampling. An Automatic Fully Digital Hematology Analyzer was used for
haematological determination whereas Universal Clinical Auto Analyzer was
used for serological determination. The following important secondary
parameters were estimated from the primary haematological parameters: Mean
Corpuscular Volume (MCV), Mean Corpuscular Haemoglobin (MCH), and
Mean Corpuscular Haemoglobin Concentration (MCHC), with their respective
formulae (Etim et al., 2013 and Gernsten, 2006, 2009):
Where Hb and PCV are haemoglobin and packed cell volume respectively.
87
Growth Performance and Carcass Data Collection
Some growth and carcass parameters were assessed during and after
the eight week experimental period. These were:
Feed Intake
The experimental diet was weighed into a feeding trough in each
replicate pen in the morning (7:00am-8:00am) and the leftover feed weighed
out the following morning before new feed was offered. The difference
between feed offered and leftover feed gave the groups feed intake per day.
The total feed consumed was divided by the total number of birds in each pen
to obtain the feed intake per bird per day, for each replicate in a treatment.
Live Weight Gain
During the experimental period, birds were weighed weekly, using an
electronic balance. Weight gain was calculated as the difference between the
previous weight and the new weight.
Feed Conversion Ratio (FCR)
Feed conversion ratio is the ratio of feed intake to weight gain. In
principle, the lower the FCR, the better the feed is utilized and vice versa.
88
Slaughtering of Birds for Carcass Traits
At the end of the experiment, three birds were randomly selected from
each replicate. The birds were weighed after 12-hour feed withdrawal, and
tagged to differentiate them. They were then stuck with a sharp knife to cut the
jugular veins and allowed to bleed for about 60 seconds, after which they were
scalded in warm water (60oC). The feathers were plucked manually and head
and shanks removed. An incision was then made around the vent to remove
the viscera (internal organs). The viscera were separated into heart, liver,
kidney, spleen, and gizzard and weighed. Then the warm carcass weight was
taken. The dressed carcass was chilled for 24 hours and cold weight taken.
Primal cuttings (wings, back, breast, drumsticks and thigh muscles) were
made from the chilled carcass, weighed and recorded.
Dressing Percentage
The dressing percentage is the proportion of the live weight of the
broiler which is sold as meat, expressed as a percentage:
Carcass weight is the weight of carcass after removal of feathers, head with
neck where it joins the spine, shank with toes and all internal organs.
In general, dressing percentage increases with age. It is low in young
birds in which there is little muscle and fat. There is wide variation in dressing
percentages of broilers in developed countries and that of developing countries
depending on what these countries consider as edible viscera and inedible
viscera. In developed countries the dressing percentage range is 70-75%
(FAO, 1996; Lessler, Ranells & Choice, 2007).
89
Cost-Benefit Analysis of Feeding PKOR-Based Rations
The cost of ration was calculated using the prevailing market prices of
the individual feed ingredients used in formulating the rations. The cost of
PKOR was obtained by summing the cost of the fresh PKOR, cost of sacks,
cost of drying materials (example black rubbers), cost of transporting fresh
material from collection site to the experimental site, labour cost for drying
50kg of PKOR to moisture content of less than 8%. The ration cost/weight
gain (1000g) in GH¢ was obtained by multiplying the ration cost per bird by
the FCR as documented by Okeudo et al. (2006) and Bello, Oyawoye, Bogoro
and Dass (2011).
Statistical Analyses
Data were subjected to 2-way ANOVA analyses as outlined by the
Generalized Linear Model of the Genstat Discovery Edition (VSNI, 2011).
The level of significance was reported at (P<0.05). The following univariate
factorial statistical model of least squares procedure was used to test the fixed
effects of genotype and ration as well as their interactions, as documented by
Mathur (2003), Lwelamira (2012) and Ibrahim et al. (2012):
Yijk = µ + Gi + Rj + (GR)ij + eijk
Where Yijk is an observation of trait Y on the kth individual of the jth
genotype, µ is the general mean, Gi is the effect of the ith genotype, Rj the
effect of the jth ration, (GR)ij the interaction between the ith genotype and the
jth ration, and eijk the residual effect/error.
90
CHAPTER FOUR
RESULTS
Introduction
This chapter is devoted to the results that were obtained from the
research. The arrangement of the results is in three main thematic areas,
namely; (1) influence of dietary treatment (PKOR-based rations) on some
performance indices (growth parameters, carcass traits, haematology and
serology), (2) effect of genotype on some performance indices (growth
parameters, carcass traits, haematology and serology) and (3) influence of
genotype-by-ration interaction on some performance indices (growth
parameters, carcass traits, haematology and serology) of Cobb 500 and Ross
308 broiler strains.
(1) Effect of PKOR-Based Rations on Performance (Growth Parameters,
Carcass Traits, Haematology and Serology) of Cobb 500 and Ross 308
Broiler Strains
The effect of dietary treatments on growth performance of Cobb 500
and Ross 308 broiler chickens in this study is presented in the Table 14.
91
Table 14: Mean Values of Dietary Treatment Effect on Growth
Performance Parameters of Broiler Chickens (4-8 Weeks of Age)
Parameter 0%
PKOR
10%
PKOR
20%
PKOR
SED p-value
Initial weight
(g)/bird
612.60 616.40 618.90 17.40 0.935
Final weight (g)/bird 2849a 2654
b 2644
b 62.30 0.010
Weight gain (g)/bird 2236a 2037
b 2025
b 60.50 0.007
Growth rate
(g/day)/bird
65.13a 59.93
b 58.00
b 1.72 0.007
Total feed intake
(g)/bird
5984a 5698
ab 5416
b 174.40 0.022
FCR/bird 2.68a 2.80
b 2.68
a 0.04 0.040
Total water intake
(g)/bird
11715a 10862
ab 10186
b 567.20 0.042
Feed cost (GH¢)/kg 1.60 1.47 1.34 - -
Feeding cost/1000g
weight gain in GH¢
4.29 4.12 3.59 - -
Savings on feed cost
(GH¢)/weight
gain/bird
0.00 0.17 0.70 - -
Means in a row with same letter superscripts are not significantly different (p>0.05)
With reference to Table 14, there was no significant difference
(p>0.05) in the mean initial body weight of broilers in all treatments at the
commencement of the feeding trial. At the end of the trial, birds on 0% PKOR
had significantly (p<0.05) higher mean final live weight, weight gain and
92
growth rate than birds on 10% and 20% PKOR. Birds on 20% PKOR
consumed significantly (p<0.05) lower amount of feed and water than birds on
0% PKOR; but consumption levels were not different from birds on 10%
PKOR (p>0.05). The feed conversion ratio (FCR) of birds on 0% and 20%
PKOR were significantly lower (p<0.05) than for birds on 10% PKOR.
However, the water: feed intake ratio did not indicate significant differences
(p>0.05) among the treatments. The inclusion of PKOR at 10% and 20% led
to a reduction in feeding cost/kg weight gain of GH¢0.17 and GH¢0.70
respectively.
The influence of different dietary treatments on some carcass and
organ characteristics is shown in Table 15.
93
Table 15: Mean Values of Dietary Treatment Effect on Carcass and
Organ Weights of Broiler Chickens (4-8 Weeks of Age)
Trait/parameter 0%
PKOR
10%
PKOR
20%
PKOR
SED p-value
Warm carcass weight (g) 2254a 2027
b 2012
b 68.80 0.007
Warm dressing percentage
(%)
78.14 76.38 75.99 1.27 0.058
Chilled carcass weight (g) 2130 1994 1989 70.50 0.072
Chilled dressing
percentage (%)
77.73 75.62 75.48 1.28 0.184
Weights of primal cut (g)
Breast (g) 716.80 683.20 667.90 42.60 0.121
Thigh (g) 327.60 306.50 318.70 18.22 0.289
Drumstick (g) 285.50 269.3 279.90 15.86 0.290
Back (g) 553.20 497.50 475.30 38.10 0.060
Wing (g) 246.90 227.50 247.20 26.41 0.609
Weight of organs (g)
Heart (g) 12.02 12.37 12.10 0.38 0.639
Liver (g) 53.80 58.30 55.60 4.56 0.626
Kidney (g) 14.35 14.24 14.84 0.76 0.708
Spleen (g) 1.81 1.96 1.90 0.25 0.854
Gizzard (g) 59.63 57.24 57.71 1.81 0.403
Abdominal fat pad (g) 18.38 18.66 18.82 0.29 0.341
Means in a row with same letter superscripts are not significantly different (p>0.05)
94
From Table 15, birds on 0% PKOR dietary treatment recorded
significantly (p<0.05) higher warm carcass weight than birds fed 10% and
20% PKOR. On the contrary, birds on all three dietary treatments recorded
similar warm dressing percentage, chilled carcass weights and chilled dressing
percentages. Also, weight of the primal cuts assessed did not vary significantly
(p>0.05) among the dietary treatments. However, the trend observed was that
birds on the 0% PKOR ration recorded numerically higher weights compared
to birds on the 10% and 20% PKOR rations. The weights of visceral organs
(heart, liver, kidney, spleen and gizzard) did not vary significantly (p>0.05)
among the dietary treatments, neither did the values indicate any particular
trend. Furthermore, weights of the abdominal fat pad of birds on the three
dietary treatments were similar.
The influence of different dietary treatments on some haematological
parameters of birds evaluated in this study is presented in Table 16.
95
Table 16: Mean Values of Dietary Treatment Effect on Some Haematological Traits in Broiler Chickens (4-8 Weeks)
Trait/parameter 0%
PKOR
10%
PKOR
20%
PKOR
SED p-value Normal Reference
ranges (Jain, 1993)
Haemoglobin (g/dl) 10.57 10.92 10.78 0.42 0.710 7.0-13.0
PCV (%) 26.48 27.99 27.53 0.82 0.206 22.0-35.0
RBC (X106/µl) 2.22 2.28 2.35 0.14 0.644 2.5-3.5
WBC (X103/µl) 2.42
a 2.38
a 2.07
b 0.14 0.045 1.2-3.0
MCV (fl/cell) 119.70 121.20 118.30 5.12 0.848 90.0-140.0
MCH (pg/cell) 47.63 48.05 46.31 1.55 0.523 33.0-47.0
MCHC (%) 39.99 39.03 39.18 15.86 0.711 26.0-35.0
Means within a row with different superscript are significantly different (p<0.05)
96
From Table 16, all haematological parameters evaluated did not show
significant difference (p>0.05) across the treatment groups, except for the
WBC. The birds on 0% and 10% PKOR recorded significantly higher
(p<0.05) WBC counts than birds on 20% PKOR ration.
The influence of different dietary treatments on the serum biochemical
profile of birds evaluated in this study is presented in Table 17.
According to Table 17, the serum biochemical parameters measured
did not vary significantly (p>0.05) among the dietary treatments. With the
exception of glucose and total protein values which appeared to increase in
numerical terms as the amount of PKOR in the ration increased, values for the
other serological parameters did not show any particular trend.
97
Table 17: Mean Values of Dietary Treatment Effect on Serological Profile of Broiler Chickens (4-8 Weeks of Age)
Parameter 0%
PKOR
10%
PKOR
20%
PKOR
SED p-value Normal reference ranges (Clinical
Diagnostic Division, 1990)
Glucose (mg/dl) 172 176 188 27.90 0.836 197–299
Cholesterol (mg/dl) 116.80 113.60 107.40 6.99 0.417 129–297
Triglyceride (mg/dl) 112.30 75.90 100.20 15.76 0.103 –
Total protein (mg/dl) 4.14 3.79 3.76 0.26 0.297 3.0–4.9
ALP (IU/l) 81 93 80 6.70 0.836 10–106
Sodium (mmol/l) 141.90 139.10 159.00 12.28 0.254 139–155
Potassium (mmol/l) 5.80 12.20 11.80 4.53 0.319 1.7–4.2
Calcium (mmol/l) 9.78 10.12 10.51 0.60 0.489 8.1–12.0
Chloride (mmol/l) 114 190 119 60.50 0.405 108–124
98
(2) Influence of Genotype on Performance (Growth Parameters, Carcass
Traits, Haematology and Serology) of Cobb 500 and Ross 308 Broiler
Strains
The influence of genotype on the growth performance parameters that
were studied are presented in Table 18.
Table 18: Mean Values of Effect of Genotype on Growth Performance of
Broilers (4-8 Weeks of Age)
Trait/parameter Cobb 500 Ross 308 SED p-value
Initial weight (g) 609.90 622.30 14.21 0.39
Final weight (g) 2674 2757 50.80 0.13
Weight gain (g) 2065 2135 49.40 0.18
Growth rate (g/day) 58.99 61.02 1.40 0.17
Total feed intake (g) 5711 5687 142.40 0.87
FCR/bird 2.77 2.68 0.04 0.07
Total water intake (g) 11186 10657 463.30 0.28
From Table 18, all growth performance parameters evaluated did not
reveal any significant difference (p>0.05) between the two genotypes.
However, numerical values for final weight, weight gain and growth rate of
Ross 308 broilers appeared to be higher than that for the Cobb 500. On the
other hand, Cobb 500 broilers recorded numerically higher values for feed
intake and water intake than that for the Ross 308 broilers.
The effect of genotype on carcass weights, dressing percentages and
organ weights of broilers is shown in Table 19
99
Table 19: Mean Values of the Effect of Genotype on Carcass Weights,
Dressing Percentages and Organ Weights of Broilers
Trait/parameter Cobb 500 Ross 308 SED p-value
Warm carcass weight (g) 2070 2126 56.20 0.34
Warm dressing percentage (%) 77.41 77.11 1.04 0.70
Chilled carcass weight (g) 2010 2074 57.60 0.34
Chilled dressing percentage (%) 76.98 76.85 1.04 0.67
Weight of Primal Cuts (g)
Breast (g) 643.00 710.00 34.80 0.14
Thigh (g) 321.90 322.30 14.87 0.66
Drumstick (g) 282.50 278.70 12.95 0.66
Back (g) 519.00 516.00 27.80 0.51
Wing (g) 243.60 249.40 15.25 0.83
Weight of organs (g)
Heart (g) 11.92 12.41 0.309 0.14
Liver (g) 55.60 56.10 3.73 0.89
Kidney (g) 13.94 15.02 0.62 0.11
Spleen (g) 1.96 2.05 0.21 0.15
Gizzard (g) 56.19 59.55 1.48 0.09
Abdominal fat pad (g) 18.45 18.79 0.24 0.18
The carcass weights, dressing percentages, weights of primal cuts,
weights of viscera organs and abdominal fat pad did not vary significantly
(p>0.05) between the Cobb 500 and Ross 308 genotypes (Table 19).
100
The effect of genotype on some haematological and serum biochemical
parameters of birds evaluated in this study is presented in Table 20.
Table 20: Mean Values of Blood Parameters of Broiler Chickens as
Influenced by Genotype
Parameter/Profile Cobb
500
Ross
308
SED P-value 1Normal ranges
Haematological profile
Haemoglobin (g/dl)
10.42
11.09
0.35
0.07
7.0-13.0
PCV (%) 26.24a 28.42
b 0.67 0.01 22.0-35.0
RBC (106/µl) 2.18 2.38 0.11 0.10 2.5-3.5
WBC (103/µl) 2.30 2.28 0.11 0.90 1.2-3.0
MCV (fl/cell) 120.50 118.90 4.18 0.71 90.0-140.0
MCH (pg/cell) 47.75 46.91 1.27 0.53 33.0-47.0
MCHC (%)
Serum biochemical profile
Glucose (mg/dl)
Cholesterol (mg/dl)
Triglyceride (mg/dl)
Total protein (mg/dl)
alkaline phosphatase (IU/l)
Sodium (mmol/l)
Potassium (mmol/l)
Calcium (mmol/l)
Chloride (mmol/l)
39.75
196.00
116.60
94.60
3.86
95.00a
142.00
7.30
10.57
119.00
39.18
161.00
108.60
97.70
3.94
78.00b
151.30
12.60
9.70
163.00
1.00
22.80
5.71
12.87
0.21
6.90
10.03
3.70
0.49
49.40
0.50
0.15
0.19
0.81
0.71
0.01
0.37
0.17
0.10
0.39
26.0-35.0
2Normal ranges
197-299
129-297
-
3.0-4.9
10-106
139-155
1.7-4.2
8.1-12.0
108-124
1Reference ranges (Jain, 1993).
2Reference ranges (Clinical Diagnostic
Division, 1990)
101
From Table 20, Ross 308 broilers recorded significantly (p<0.05)
higher PCV values than Cobb 500 broilers. However, values of all the other
haematological parameters measured were similar for both genetic groups
(p>0.05). Also, Cobb 500 broilers recorded significantly (p<0.05) higher ALP
values than Ross 308 broilers. However, values for all the other serological
parameters measured were similar for the two genetic groups (p>0.05).
(3) Influence of Genotype x Ration Interaction on Growth Parameters,
Carcass Trait, Haematological and Serological Traits in Cobb 500 and
Ross 308 Broiler Strains
There was no significant (p>0.05) genotype x ration (environment)
interaction effect on growth, carcass traits, haematological and serum
biochemical parameters studied in broiler chickens and since mean values of
traits are not the focus in genotype x environment but rather the correlation or
strength of the interaction, there is no need to present tables of means of traits
if there is no significant differences in the least squares analysis of variance.
102
CHAPTER FIVE
DISCUSSION
Introduction
In this chapter, results of the research have been discussed. The
discussion has been arranged to match the order in which the results have been
presented in the chapter four. Thus, the findings are discussed under the
following headings; (1) influence of PKOR-based rations on performance
(growth parameters, carcass traits, haematology and serology) of Cobb 500
and Ross 308 broilers; (2) effect of genotype on performance (growth
parameters, carcass traits, haematology and serology) of Cobb 500 and Ross
308 broilers; (3) influence of genotype-by-ration interaction on performance
(growth parameters, carcass traits, haematology and serology) of Cobb 500
and Ross 308 broilers.
(1) Effect of PKOR-Based Rations on Performance (Growth Parameters,
Carcass Traits, Haematology and Serology) of Cobb 500 and Ross 308
Broilers Strains
Crude Protein
The data presented in Table 12 on the chemical composition of the
experimental rations indicated that the CP% for the 0%, 10% and 20% PKOR
rations were 19.01%, 19.48% and 20.25% respectively. The CP% content of
20% PKOR ration was significantly higher (p<0.05) than that of 0% PKOR
ration but not significantly different (p>0.05) from the 10% PKOR ration.
Thus, theoretically, birds on the 20% KPOR should have higher weight gain,
103
growth rate, live weight and FCR than birds on 0% PKOR but not different
from birds on the 10% PKOR. However, practically, this is not always the
case because of the differences in protein digestibility and utilisation due to
Maillard reaction (McDonald et al., 2002). The statistically higher CP% of the
20% PKOR ration was due to the replacement of wheat bran with 20kg of
PKOR, and the latter, which as a result of the prolonged heating under higher
temperatures (140-180oC) during processing might have undergone Maillard
reaction resulting in its characteristic brown colour. Consequently, the 20%
PKOR ration might have practically lower protein digestibility and utilisation
compared with the 0% PKOR ration.
Metabolisable Energy (ME)
The calculated ME (kcal/kg) of the 0% PKOR was significantly lower
(p<0.05) than that of the 10% and 20% PKOR rations. The ME (kcal/kg)
values obtained were 2840.13, 2920.86 and 2954.75 for the 0%, 10% and
20% PKOR rations respectively. According to Pond et al. (1995) and
McDonald et al. (2002), birds consume feed to meet their energy requirements
for maintenance and production. Consequently, birds would consume higher
amount of any feed of lower caloric content than feed with higher caloric
content. Birds were expected to consume more of the 0% PKOR ration than
10% and 20% PKOR rations; this trend was actually observed in this study.
Body Weight
At the end of the feeding trial, birds on 0% PKOR had significantly
(p<0.05) higher final live weight, weight gain and growth rate than birds on
104
10% and 20% PKOR (Table 14), indicating that these growth traits were
negatively related to the dietary inclusion levels of PKOR. The lower values
of these traits with increasing PKOR inclusion levels in broiler chicken rations
may be attributed to the lower nutrient digestibility with PKOR inclusion. This
explanation is supported by Sundu and Dingle (2003) who reported that
heating agro-industrial by-products at higher temperatures during processing
may cause feed products such as PKC and its variants such PKM to undergo
Maillard reaction (the reaction of reducing sugar with amino groups leading to
the formation of a characteristic brown complex) due to heat applied in the
process before and during oil extraction which adversely affects the
digestibility. Confirming this explanation further, McDonald et al. (2002)
reported that, Maillard reaction occurring as a result of prolonged heating of
feed products leads to formation of complex linkages within and between
peptide chains, some of which are resistant to hydrolysis by proteases, thereby
reducing the solubility, digestibility and utilisability of proteins in such feed
products. Another feed factor which might have contributed to the lower live
weight and its correlated traits of birds on the 20% PKOR ration compared
with the 0% is that of higher fibre content in the former, which is known to
reduce digestibility according to Pond et al. (1995). The results obtained in
this study are also in agreement with Ojewola and Ozuo (2006) who reported
that birds fed on diets containing 10%, 15% and 20% of PKC instead of
soybean meal had depressed the body weight when compared with control.
Furthermore, Soltan (2009) as well as Ezieshi and Olomu (2008) indicated that
the feeding of PKM (mechanically extracted) based diets, non significantly
depressed broiler chicks weight, while other PKM types (solvent extracted)
105
highly depressed final body weight of broiler birds. On the contrary, Okeudo
et al. (2006) reported that average body weight of broilers was approximately
2 kg in each dietary group at the 8th
week of age, and was not significantly
affected by inclusion of PKC up to 30% of the diets. Egenuka et al. (2013)
who studied the effect of different dietary levels (0%, 20% and 40%) of PKC
inclusion on the growth of chickens indicated that there were significant
(p<0.05) increases in the final live weight of the growers with increase in the
level of palm kernel cake in the ration. These differences in the findings from
feeding trials using PKOR and PKC/PKM may be related to the different level
of fibre and the varying degree of Maillard reaction in these products used by
the researchers, since all reactions up to the formation of Amadori compounds
at the initial stage of Maillard reaction are reversible to release amino acids for
utilisation by the chicks (Yaylayan & Huyghues-Despointes, 1994). It is
therefore advisable for agro-processing industries that produce
PKC/PKM/PKOR to reduce the amount of heat applied during processing as
well as the duration of heat exposure of the products in order to minimize the
degree of Maillard reaction in their by-products, which are eventually used as
animal feed ingredients. Also, farmers and feed manufacturing companies may
use PKOR/PKC/PKM in combination with feedstuffs high in essential amino
acids or with industrially manufactured amino acids such as Lysine (the main
essential amino acid mostly affected by Maillard reaction), and/or with
enzymes to improve the overall utilisation of these agro-industrial by-
products.
106
Feed Intake
Birds on 20% PKOR ration consumed significantly (p<0.05) lower
amount of feed than birds on 0% PKOR ration, but intakes were not different
from birds on 10% PKOR (Table 14). The lower feed intake by birds on the
20% PKOR compared with those on 0% PKOR may be due to the higher
energy content in the 20% PKOR ration than in the 0% (Table 12), as birds
will consume feed to meet their energy requirement (McDonald et al., 2002).
Thus, the higher the amount of energy in the feed, the lower the amount of
feed needed to meet the energy requirement, and vice versa. This lower feed
intake with increasing level of PKOR agrees with by Soltan (2009) and Onuh
et al. (2010) who reported reduction in feed intake of broilers with increasing
level of palm kernel cake in diets. This however, contradicts the findings of
Ojewola and Ozuo (2006) who observed that broiler fed 15% PKM in their
diet exhibited higher feed intake when compared with control and other broiler
chick groups fed on lower levels of PKM. Also, Ezieshi and Olomu (2008)
reported that there was higher daily feed intake by broiler chicks fed on
mechanically pressed palm kernel meal. Their findings were however, as a
result of the fact that the control diet had soyabean meal with higher energy
than the PKM diets. The implications of the current study is that, feed
consumption in broiler finisher chickens could be reduced to considerable
levels with up to 20% of PKOR in ration, especially after the 8th
week when
birds need to be kept a few more weeks due to the absence of ready market.
This will help reduce feeding cost as feed intake reduces.
107
Water Intake
The results of this study revealed that birds on 20% PKOR ration
consumed significantly (p<0.05) lower amounts of water than birds on 0%
PKOR ration but these were not different from birds on 10% PKOR (Table
14). Forbes (1998) reported that water requirements are related to feed
consumption; and under the same environmental temperature conditions,
water intake increases with increasing feed intake and decreases with
decreasing feed intake, thereby making the observation in this study logical.
This suggests that farmers should anticipate increased water consumption and
hence supply more water when they provide low energy diet to their birds, and
vice versa.
FCR
The feed conversion ratio (FCR) of birds on 0% and 20% PKOR
(Table 14) were significantly lower (p<0.05) than for birds on 10% PKOR;
implying that the ration without PKOR and ration with 20% PKOR had
similar and better utilisation levels. Feed conversion ratio (FCR) is a measure
of animal’s efficiency in converting feed mass into body mass. Some feed
factors that influence utilisation are digestibility and nutrient or energy
content. The better utilisation of the ration without the PKOR and ration with
the 20% PKOR may have resulted from higher digestibility with higher
nutrient absorption and higher energy content respectively, even though the
latter might have a lower digestibility. In effect, the higher energy content of
the 20% PKOR ration might compensate for nutrient losses due to lower
digestibility, giving it a feed conversion ratio similar to the control ration. The
108
FCR results from this study agrees with that of Egenuka et al. (2013) who
reported no significant difference in the FCR of broilers fed 0% PKC and 40%
PKC diets. This however, conflicts the report of Okeudo et al. (2006) who
observed that broilers fed 0% PKC diet had significantly lower FCR than
broilers fed 45% PKC diet; this might however be due to the fact that at the
45% inclusion of PKC, the digestibility of the diet was drastically reduced.
Comparing the results of 0% PKOR with the 10% PKOR ration of this study,
the higher FCR of the former could be attributed to poor digestibility and
lower nutrient utilisation relative to the latter. However, comparing the FCR of
the 0% and that of the 20%, the higher value for the former could be due to its
lower energy content relative to the latter; though both may have similar lower
digestibility. Consequently, despite the supposedly poor digestibility of the
20% PKOR ration its higher energy content might compensate for nutrient
losses due to lower digestibility, resulting in better conversion ability. Thus,
farmers may reduce feed intake and feeding cost with 20% of PKOR inclusion
and also achieve better feed conversion.
Feed Costs
One of the important economic motivations for the use of PKOR in
poultry rations is its potential to minimize feed cost when it replaces a
conventional feed ingredient of relatively higher price. The results of this
study showed that the inclusion of PKOR at 10% and 20% (with PKOR
directly replacing wheat bran) led to a reduction in feed cost/kg weight gain of
GH¢0.17 and GH¢0.70 respectively, confirming work by Odoi et al. (2007)
who also reported significant reduction in feed cost when up to 15% of PKOR
109
were fed to broiler finisher chickens. These reductions in feed cost per
kilogram weigth gain translates into substantial savings and increased profit
margin. The results are similar to that of Ezieshi and Olomu (2004) who
reported significant reductions in feed cost per weight gain with increasing
PKC inclusion rates. Likewise, Egenuka et al. (2013) reported 295.91 Naira
(N), 247.92 Naira (N) and 211.06 Naira (N) as average feed cost/kg gain (N)
for 0% PKC, 20% PKC and 40% PKC levels respectively in the diets of
pullets, indicating a significant (P < 0.05) reduction in feed cost as PKC levels
increased in the diet. Similarly, the feed cost per kg live weight gain was
significantly less in broilers fed PKM, with or without enzymes, compared to
control (Shakila et al., 2012). Berepubo, Mepba, Agboola and Onianwah
(1995) reported that the cost of feed decreased as the level of palm kernel cake
inclusion increased. Contrary to these results, Onuh, Ortserga and Okoh
(2010) reported no significant differences (p>0.05) in feed cost per unit weight
gain among birds fed the control diet and those fed the diets which contained
1:1 and 4:1 ratio of palm kernel cake and maize pap offal. Nevertheless, the
cost of feed intake per gain would depend on the comparative costs of the
different ingredients utilized and their inclusion levels. The results of this
study suggest that farmers can make substantial savings on feeding costs,
which translates into increased profit margin, when they replace wheat bran
with PKOR up to 20% inclusion rate in broiler finisher ration.
Warm Carcass Weight
From Table 15, birds on 0% PKOR recorded significantly (p<0.05)
higher warm carcass weight than birds fed 10% and 20% PKOR rations. The
110
differences could be attributed to the fact that birds on the 0% PKOR ration
had significantly higher weight gain and final live weight than those on the
10% and 20%, implying that body weight gain, final live weight and warm
carcass weight are correlated. The warm dressing percentages of birds were
similar for all treatment groups. The warm dressing percentage measures the
yield of the body muscle of the chicken, made ready to be cooked or frozen
(Kekeocha, 1995). The values obtained were 78.14%, 76.38% and 75.99% for
the 0%, 10% and 20% PKOR rations respectively. These values are slightly
above the reported average of 70-75% (FAO, 1996; Lessler et al., 2007), due
possibly to the fact that more of the nutrients derived from the rations fed to
the broilers were used to synthesize muscles rather than to develop such
unwanted parts as feathers, offal and viscera as explained by Palo, Sell,
Piquer, Vilaseca and Soto-Salanova. (1995). The non-significant difference in
the dressing percentage of the 0%, 10% and 20% PKOR rations observed in
this study agrees with work of Soltan (2009) who revealed that PKC dietary
inclusion at different levels had no effect on dressing percentage when
compared with the control, similar to what has been reported by Shakila et al.
(2012). Subsequently, farmers who wish to slaughter and sell their broilers on
warm carcass weight can make considerable financial gains by using up to
20% of PKOR in their broiler finisher ration.
Chilled Carcass Weight
The chilled carcass weight and chilled dressing percentage were
similar for all the treatment groups even though the trend in value for the
control ration birds was numerically higher than that for the rations with the
111
PKOR. Fresh meat is approximately 70 to 75 percent water, making carcasses
very susceptible to evaporative cooling loss in the first 24 hours of chilling,
with the losses ranging from 3 to 5 percent of the hot carcass weight
(Rentfrow, 2010). Carcasses with moderate fat cover will have good water-
holding capacity and less liable to cooler shrink. Knowing the effect of
chilling on the carcass is necessary to avoid misunderstandings between meat
processors and consumers, in terms of the price difference between warm and
chilled carcasses or the possible reduction in weight of paid hot carcass which
has to be chilled by the processor and later collected by the consumer/buyer.
From Table 15 the warm carcass weight of the control ration lost about 5.5%
weight after chilling for 24 hours whereas the 10% and 20% lost 1.63% and
1.14% weight respectively within the same chilling period; indicating that the
carcass of birds fed PKOR rations was more resistant to evaporative cooling
losses due probably to moderate intramuscular fat content of the meat.
Consequently, farmers and companies who wish to process live birds and add
value to the meat by chilling and selling chilled broiler meat would find
PKOR inclusion in the broiler rations profitable.
Primal Cuts
Carcass is sometimes sold in parts; in this study, the weights of various
parts (breast, thigh, drumstick, wing and back) cut from the chilled carcass
were determined but did not differ among the treatments. The inclusion of
PKOR did not have any effect on any of the parts assessed. The weight of the
abdominal fat pad was also similar across the treatments, implying the
112
considerable amount of fat (Table 12) in the 10% and 20% PKOR rations did
not lead to increased abdominal fat deposition.
Visceral Organs
The weight of visceral organs (heart, liver, spleen, kidney and gizzard)
did not vary significantly (p>0.05) across the dietary treatments; neither did
the values indicate any particular trend. PKOR is high in oil; and oxidized oil
raises the levels of aldehyde and other oxidized metabolites (Cherian et al.,
1996). According to Wang et al. (1997) the accumulation of oxidative
products may lead to increased weight of visceral organs, an indication of
abnormality. However, none of the organs showed signs of abnormality in the
present study as weight of organs were similar for all the dietary groups. The
results are consistent with that of Chinajariyawong and Muangkeow (2011)
who reported no significant difference in the relative weights of visceral
organs of broiler chickens fed palm kernel meal in rations up to 40%.
Subsequently, feeding PKOR in broiler rations up to 20% will not cause organ
abnormality and malfunction.
Haematological Parameters
The blood analysis showed non-significant effect of PKOR inclusion
for all haematological parameters (Table 16) considered, except for WBC
count. The birds on 0% and 10% PKOR recorded significantly higher WBC
count than birds on 20% PKOR ration. Bello et al. (2011) obtained similar
results with broilers fed varying levels (0%, 10%, 20%, 30% and 40%) of
PKM; and which were also in consonance with work by Egenuka et al., (2013)
113
who fed broilers with varying levels (0%, 20% and 40%) of PKC. A readily
available and fast means of assessing clinical and nutritional health status of
animals on feeding trials may be by the use of blood analysis, because
ingestion of dietary components has measurable effects on blood composition
(Church et al., 1984; Maxwell et al., 1990); and this may be considered or
used as appropriate measure of long term nutritional status (Olabanji et al.,
2007). According to Togun and Oseni (2005), haematological studies have
been found useful in disease prognosis, and for therapeutic and feed stress
monitoring. Adamu et al., (2006) observed that nutrition had significant effect
on haematological values. Togun et al., (2007) reported that when
haematological values fall within the normal range reported for the animal, it
is an indication that diets do not show any adverse effect on haematological
parameters; but when the values fall below the normal range, as reported by
(Bawala et al., 2007), this could be due to the harmful effects of high levels of
some dietary contents. However, all the haematological parameters assessed
were within the normal ranges (Table 6) reported for chickens by Jain (1993).
This observation shows that PKOR is not toxic or detrimental to broilers.
Serological Parameters
The serum biochemical parameters evaluated did not vary significantly
(p>0.05) among the treatments (Table 17). With the exception of glucose and
total protein which increased and decreased respectively in numerical value as
the amount of PKOR in the ration increased, values of the other serological
parameters did not show any particular trend. The non-significant increase and
decrease in the numerical value of glucose and protein respectively with
114
increasing PKOR inclusion indicated that increasing PKOR levels resulted in
increasing energy (in the form of glucose) and decreasing protein levels in the
blood serum. Egenuka et al. (2013) reported that glucose level was slightly
higher in 40% PKC group than in the 0% and 20% PKC group and linked it to
the slightly higher energy level of the feed consumed by the birds in 40%
group. According to Shakila et al. (2012), the total serum protein (4.68-
4.89mg/dl) in broilers fed with PKM at different levels (0, 2.5, 5.0, 7.5 and
10.0 %) was not significantly affected, with or without enzymes
supplementation, and it might be ascribed to adequate protein levels in the
diets that were able to support normal reserve of the proteins in the body as
supported by the work of Adesehinwa (2007). These findings were in
consonance with the observations of the current study. Cholesterol and
triglyceride values are usually positively correlated, and are used to assess the
function of the heart and cardiovascular diseases. The findings of Egenuka et
al. (2013) again indicated that their values for cholesterol were not
significantly different for varying levels of PKC; this is similar to the results
of the present study. Measures of calcium, sodium, potassium and chloride are
used to check whether or not the kidneys are functioning properly, and the
non-significant differences noticed among the control and the PKOR-based
rations in this study indicated that the inclusion of PKOR in the rations did not
affect the integrity of the kidney and its function, as reported in studies by
Ibrahim et al. (2012). The liver is one of the most important organs in the
body; in this study, alkaline phosphatase was the only parameter assessed for
liver function test. The results obtained did not vary significantly among the
different rations. Similarly, serum alkaline phosphatase was not significantly
115
(p>0.05) affected by PKM inclusion up to 10% in diets, indicating that PKM
and lits variants could safely be incorporated in broiler diets without any
adverse effects on liver function (Shakila et al., 2012). The results from this
study indicate that the inclusion of PKOR in broiler finisher rations would not
affect the integrity of liver and its function in broiler chickens.
(2) Influence of Genotype on Performance (Growth Parameters, Carcass
Traits, Haematology and Serology) of Cobb 500 and Ross 308 Broiler
Strains
Growth Traits
From Table 18, all growth performance parameters evaluated did not
reveal any significant difference (p>0.05) between the two genotypes. The
non-significant difference in the mean values of all growth traits (final weight,
weight gain, growth rate, FCR, water intake and water: feed ratio) might be
due to the fact that the two breeds have similar genetic composition. In other
wol8rds, Cobb 500 and Ross 308 genotypes have comparable genetic
constitution and potential for growth and hence any one of the two can serve
as a farmer’s choice. The results of all growth performance traits contradicted
the findings of Sterling, Pesti and Bakalli (2006) who observed significant
difference in the growth performance of Cobb 500 and Ross 308 with the
former breed recording better performance than the latter. Similarly,
Hristakieva, Mincheva, Oblakova, Lalev and Ivanova (2014) who reported
that Соbb 500 broiler genotypes attained a higher live weight, and were
116
heavier than Ross 308 genotypes by 6.29 % at 49 days of age (7 weeks). The
observations of the current study also disagreed with Mmereole and Udeh
(2009) who found that the local chicken by Barred Plymouth rock (G3) and
the Barred Plymouth rock (G4) groups were significantly (P<0.01) heavier
than the local chicken (G1) and the Barred Plymouth rock by local chicken
(G2) groups at the 1st, 4th and 8th weeks of age, respectively. Mmereole and
Udeh (2009) concluded that the G4 and G3 genotypes had superior genetic
potential for body weight than the G1 and G2 genotypes. The results of the
current study again contradicted the findings of Olawumi, Fajemilehin and
Fagbuaro (2012) who reported that both sexes of Marshal Broilers recorded
the highest live weight at 56 days of age (8 weeks) when compared with Arbor
Acre and Hubbard chickens, and described the former as having superior
genetic potentials for meat yield than the latter.
Carcass Traits
The carcass weights, dressing percentages, weights of primal cuts,
weights of visceral organs and abdominal fat pad did not vary significantly
(p>0.05) between the Cobb 500 and Ross 308 genotypes (Table 19). This
implies that the two genotypes had similar genetic composition. The
productive performance of three commercial broiler genotypes (Marshall,
Arbor Acres and Hubbard) reared in the savannah zone of Nigeria was
assessed by Olawumi and Fagbuaro (2011). In contrast to the results of this
study the authors reported that, as regards the carcass traits, Marshall genotype
had superior and higher (p<0.05) mean values in dressing out weight,
eviscerated weight, carcass weight, carcass percentage, breast muscle weight,
117
back muscle weight, thigh muscle weight, drumstick weight, neck weight and
wing weight when compared with Arbor Acres and Hubbard. Similarly, Musa,
Chen, Cheng, Li and Mekki (2006) and Ojedapo et al. (2008) also reported
significant effect of breed in all the carcass traits evaluated including
abdominal fat weight. However, Olawumi and Fagbuaro (2011) recorded
similar values in dressing out percentage and abdominal fat for the three
genotypes studied. Their results were similar to the findings of the present
study. Moreover, there was no significant difference between the Arbor Acres
and Hubbard genotypes for the carcass traits evaluated, indicating that these
two genotypes probably shared common genetic composition (Olawumi &
Fagbuaro, 2011), which is in consonance with the outcome of the current
study. The weight of visceral organs were also similar for both Cobb 500 and
Ross 308 genotypes in this study which agrees with the report of Olawumi &
Fagbuaro (2011) but contrary to the result of Taha, Abd El-Ghany and Sharaf
(2010) who reported significant effect of breed on these traits. The non-
significant difference in the weight of visceral organs in the present study
indicates that Cobb 500 and Ross 308 broiler genotypes shared comparable
genetic composition in respect of these organs and hence had similar organ
functions. Consequently, farmers can choose to buy either Cobb 500 or Ross
308 and raise them for the market as whole carcass or cut carcass parts.
Haematological Parameters
All haematological parameters were similar for both genetic groups,
except the PCV which was significantly (p<0.05) higher for Ross 308 than
Cobb 500 (Table 20). This observation suggests that Cobb and Ross genotypes
118
possessed similar genetic constitution for all the haematological parameters
evaluated except PCV (percentage of red blood cells in the blood). The higher
mean value of PCV recorded for Ross 308 points out that oxygen and nutrient
transport in Ross 308 was better than in Cobb 500. With the exception of the
results for the PCV, all haematological findings of the present work
contradicted the work by Peters et al. (2011) who reported a variation in
haematological parameters of Nigerian native chickens; normal-feathered
birds had higher mean values compared with frizzled feather and naked neck
genotype, which were consistent with Islam et al. (2004) and Chineke et al.
(2006) strengthening the argument for interest of genetic differences or
variations among Nigerian native chickens. Nevertheless, all the
haematological values of the current study were within the normal reference
ranges for chicken haematology (Table 6) as documented by Jain (1993),
except MCHC which was slightly and insignificantly above the normal range
(26.0-35.0%) in this study. Haematological studies have been found useful for
disease prognosis and for assessing general health status (Maxwell et al.,
1990; Togun and Oseni (2005). The results of the current study showed that
Cobb 500 and Ross 308 shared similar superior genetic composition which
conferred healthy status on the two genotypes as their haematological values
did not deviate from the normal ranges.
Serum Biochemical Profile
From Table 20 Cobb 500 broilers recorded significantly (p<0.05)
higher ALP than Ross 308 broilers. However, values of all the other
serological parameters were similar for the two genetic groups. This
119
observation likewise suggests that Cobb 500 and Ross 308 genotypes
possessed similar genetic composition for all the serological parameters
evaluated except ALP enzymes, implying that the higher mean value of ALP
recorded for Cobb 500 is an indication that those genes responsible for ALP
enzymes in Cobb 500 are more than those of the Ross 308. The results of the
present work are in consonance with that of Ladokun et al. (2008) who
reported no significant (p>0.05) differences in total protein, albumin, urea,
glucose and cholesterol levels between naked-neck and normally feathered
genotypes of Nigerian indigenous chickens in a sub humid tropical
environment, similar to the report of El-Safty et al. (2006). Generally, the
serum biochemical parameters obtained were within the reference ranges for
chickens (Table 8) provided by Clinical Diagnostic Division (1990). Serum
biochemical profile helps to assess the functions of major organs such as heart,
liver and kidney in the body of animals. Cholesterol and triglyceride values
are usually positively correlated and are used to assess the function of the
heart and cardiovascular disease status. The liver is one of the most important
organs in the body and in this study alkaline phosphatase is the only parameter
assessed for liver function test. Measures of calcium, sodium, potassium and
chloride are used to check whether or not the kidneys are functioning properly.
The normal serum biochemical values observed in the current study showed
that Cobb 500 and Ross 308 shared similar superior genetic composition
which ensured normal and healthy functioning of major organs in the two
genotypes.
120
(3) Influence of Genotype × Ration Interaction on Some Performance
(Growth, Carcass Traits, Haematology and Serology) Parameters of Cobb
500 and Ross 308 Broiler Strains.
There was no significant (p>0.05) genotype x ration (environment)
interaction effect on growth, carcass traits, haematological and serological
parameters in broilers. The results of this work implied that there was absence
of joint effect of breed and ration on birds’ performance; that is, the two
factors acted independently of each other as explained by Olawumi et al.,
(2012). The results of the current work are also in consonance with work by
Mmereole and Udeh (2009) who reported no significant genotype by diet
interaction on body weight and weight gain of the Nigerian local chicken and
its crosses with Barred Plymouth Rock. The absence of genotype x ration
interaction in the present study indicates that the nutritional environment of
the three rations (0%, 10% and 20% PKOR) similarly favoured gene
expression and regulation of traits. Hence the two genotypes did not differ in
ranking. The implication is that farmers can raise any of the two genotypes on
any of the three rations without detrimental effect on performance or
production provided nutritional composition of diets were adequate for
requirements of birds in that category.
121
CHAPTER SIX
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
Introduction
This chapter consists of three sections. The first section summarizes
the major results of the research carried out in connection with the use of
PKOR as broiler chicken feed ingredient. The second section draws
conclusions on the use of PKOR as broiler chicken feed ingredient and its
effects on production and profitability of farmers. The third section presents
recommendations for further studies.
Summary
The study was conducted to assess and compare the performance of
two commercial broiler genotypes (Cobb 500 and Ross 308) on three levels
PKOR-based rations (0%, 10% and 20%) in the Central Region of Ghana.
Specifically, it determined: (1) influence of PKOR-based rations on
performance (growth parameters, carcass traits, haematology and serology) of
Cobb 500 and Ross 308 broilers; (2) effect of genotype on performance
(growth parameters, carcass traits, haematology and serology) of Cobb 500
and Ross 308 broilers; (3) influence of genotype-by-ration interaction on
performance (growth parameters, carcass traits, haematology and serology) of
Cobb 500 and Ross 308 broiler strains.
The work revealed the following findings:
122
Effect of Ration on Performance
(1a) Growth Parameters: Control birds (0% PKOR) had significantly higher
(p<0.05) final live weights compared with lower but similar weights (p>0.05)
for birds on 10% and 20% PKOR rations. This trend and statistical level of
significance was reflected in other growth parameters such as weight gain and
growth rate. Trends observed for feed and water intake were also similar but
not significantly different (p>0.05) among treatments. FCR was however,
significantly lower (p<0.05) in control and 20% birds than birds on 10%
treatment ration. Water: feed ratio was not significantly different (p>0.05)
among treatments. Feed cost/kg weight gain declined progressively from
control (GH¢4.29) to birds fed rations containing 20% of PKOR (GH¢3.59),
although differences were not significant (p>0.05).
(1b) Carcass and Organ Weights: Birds on 0% PKOR dietary treatment
recorded significantly (p<0.05) higher warm carcass weight and warm
dressing percentage than birds fed 10% and 20% PKOR. On the contrary,
birds on all three dietary treatments recorded similar chilled carcass weights
and chilled dressing percentages. Also, weight of the primal cuts assessed did
not vary significantly (p>0.05) among the dietary treatments. The weights of
viscera organs (heart, liver, kidney, spleen and gizzard) did not vary
significantly (p>0.05) among the dietary treatments, neither did the values
indicate any particular trend. Furthermore, weights of the abdominal fat pad of
birds on the three dietary treatments were similar.
(1c) Haematological Parameters: All haematological parameters evaluated did
not show significant difference (p>0.05) across the treatment groups, except
123
for the WBC. The birds on 0% and 10% PKOR recorded significantly higher
(p<0.05) WBC counts than birds on 20% PKOR ration.
(1d) Serological Parameters: The serum biochemical parameters measured did
not vary significantly (p>0.05) among the dietary treatments.
Effect of Genotype on Performance
(2a) Growth Parameters: All growth performance parameters evaluated did not
reveal any significant difference (p>0.05) between Cobb 500 and Ross 308
broiler genotypes.
(2b) Carcass and Organ Weights: The carcass weights, dressing percentages,
weights of primal cuts, weights of viscera organs and abdominal fat pad did
not vary significantly (p>0.05) between the Cobb 500 and Ross 308 broiler
genotypes.
(2c) Haematological Profile: Ross 308 broilers recorded significantly (p<0.05)
higher PCV than Cobb 500 broilers. However, values of all the other
haematological parameters were similar for both genetic groups.
(2d) Serological Profile: Cobb 500 broilers recorded significantly (p<0.05)
higher ALP values than Ross 308 broilers. However, values for all the other
serological parameters were similar for the two genetic groups.
Effect of Genotype-Ration Interaction on Performance
(3) There was no significant (p>0.05) genotype-by-ration interaction effect on
performance (growth parameters, carcass traits, haematology and serology) of
Cobb 500 and Ross 308 broiler strains.
124
Conclusions
(1) Cobb 500 and Ross 308 breeds of broiler chickens recorded final live
weight (g) of 2674 and 2757; weight gain (g) of 2065 and 2135;
growth rate (g/day) of 58.99 and 61.02 and FCR of 2.77 and 2.68;
respectively. Thus, Cobb 500 and Ross 308 shared similar genetic
composition for all growth traits measured.
(2) Cobb 500 and Ross 308 breeds recorded warm carcass (g) of 2070 and
2126; warm dressing percentage (%) of 77.41 and 77.11; chilled
carcass (g) of 2010 and 2074; chilled dressing percentage (%) of 76.98
and 76.85 respectively. This implies that the two Cobb 500 and Ross
308 genotypes are genetically comparable for carcass traits.
(3) Haematological profile and serological profile were similar for both
genotypes and were also within normal reference ranges, indicating
that Cobb 500 and Ross 308 genotypes have comparable superiority in
terms of genetic composition; and hence, had normal organ functions
and good health status.
(4) There was no genotype x ration interaction effect with respect to the
growth traits, carcass traits, haematological and serological profile of
Cobb 500 and Ross 308 genotypes; and hence, the two genotypes did
not differ in ranking. The implication is that farmers can raise any of
the two genotypes on any of the three rations without detrimental
effect on performance or production provided nutritional composition
of diets were adequate for requirements of birds in that category.
(5) The 0%, 10% and 20% PKOR rations respectively recorded final live
weight (g)/bird of 2849, 2654 and 2644 and FCR of 2.68, 2.80 and
125
2.68. The results indicate that the optimum inclusion rate of PKOR out
of the three treatments for Cobb 500 and Ross 308 broiler genotypes
was the 20%, since the 20% ration gave the least FCR with final live
weight of 2644g which is within the 8th
week market weight of 2500-
3000g.
(6) The 0%, 10% and 20% PKOR rations recorded feed cost/kg weight
gain of GH¢4.29, GH¢4.12 and GH¢3.59 respectively. The results of
this study showed that the inclusion of PKOR at 10% and 20% (with
PKOR directly replacing wheat bran) led to a reduction or savings in
feeding cost/kg weight gain of GH¢0.17 and GH¢0.70 respectively.
These savings on feed cost would increase the profit margin of farmers
who use PKOR in rations for their Cobb 500 and Ross 308 broilers.
Recommendations
(1) The processing method of PKOR exposes it to possible Maillard
reaction. Hence agro-processing industries that produce
PKC/PKM/PKOR should reduce the amount of heat applied during
processing as well as the duration of heat exposure of the products
in order to minimize the degree of Maillard reaction in their by-
products, which are eventually used as animal feed ingredients.
(2) Further studies should be conducted to determine the effects of
PKOR diets at isonitrogenous and isocaloric levels on the sensory
characteristics of broiler chicken meat.
126
REFERENCES
Abeka, M.K. (2007). Suitability of stabilized Palm Kernel Oil Residue as
Feed Ingredient in Broiler Finisher Diets. B.Sc. Dissertatian,
School of Agriculture, University of Cape Coast
Adamu, S., Thomas, A., Iseh, N. M., Fatihumi, M. Y., & Esieno, K. A. N.
(2006). Normal values of haematology of Nigeria adopted albino
rats (Rattus norvegicus) in Zaria. Proc. of 31st Annual Conf. Of
Nig. Soc. for Anim. Prod. (NSAP). Abuja, Nigeria.
Adejumobi, E. O. (1999). Analysis of the movement of urban tool prices in
Nigeria. Nigeria Journal of Economics and Social Studies, 12(1),
133–140.
Adesehinwa, A. O. K. (2007). Utilization of palm kernel cake as a
replacement for maize in diets of growing pigs. Effects on
performance, serummetabolites, nutrient digestibility and cost of
feed conversion. Bulgarian Journal of Agricultural Science, 13:
693-600
Agaie, B. M., & Uko, O. J. (1998). Effect of season, sex, and species
difference on the packed cell volume (P.C.V.) of Guinea and
domestic fowls in Sokoto, Sokoto state of Nigeria. Nigerian
Veterinary Journal, 19, 95-99.
Ali, K.O., Katule, A.M., & Syrstad, O. (2000). Genotype–environment
interaction in growing chickens: comparison of four genetic groups
on two rearing systems under tropical conditions. Acta
Agriculturae Scandinavica 50, 65–71.
127
AOAC. (2000). Official methods of analysis .18th
edition. Association of
Official Analytical Chemists. Washington DC.
Apantaku, S.O., Oluwalana, O. A. & Adepegba, O. A. (2006). Poultry
farmers’ preference and use of commercial and self-compounded
feeds in Oyo area of Oyo State, Nigeria. Agriculture and Human
Values 23(2): 245-252.
Arthur, J. A. & Albers, G. A. A. (2003). Industrial perspective on problems
and issues associated with poultry breeding. In: W. M. Muir & S.
E. Aggrey, (Eds.), Poultry genetics breeding and biotechnology
(pp. 1-10). CAB International, Oxon, UK.
Aster, J. C. (2004). Anaemia of diminished erythropoiesis. In V. Kumar, A. K.
Abbas, N. Fausto, S. L. Robbins, & R. S. Cotran (Eds.), Robbins
and Cotran Pathologic Basis of Disease (7th ed., p.638-649).
Saunders Co. Philadelphia.
Awodi, S., Ayo, J. O., Atodo, A. D., & Dzende, T. (2005). Some
haematological parameters and the erythrocyte osomotic fragility
in the laughing dove (Streptopella senegalensis) and the village
weaner bird (Ploceus cucullatus) (p.384-387). Proceedings of
the 10th Annual Conference of Animal Science Association of
Nigeria, (p.384-387).
Bawala, T. O., Akpan, U., Ogunnowo, A. O., Fasae, O. A. & Sogunle, O. M.
(2007). Influence of Mg supplementation on the haematological
indices of young West African Dwarf (WAD) goats. Proc. of the
32nd
Annual Conf. of NSAP, (pp.76-78).
128
Bello, K.M., Oyawoye, E.O., Bogoro, S.E. & Dass, U.D. (2011).
Performance of broilers fed varying levels of palm kernel cake.
International journal of poultry science 10 (4): 290-294
Berepubo, N. A., Mepba, H. D., Agboola, O. & Onianwah, R. I. (1995).
Inclusion rate and true metabolazable energy of palm kernel cake
in broiler chicken diets in a humid tropic environment. Journal of
applied Animal research 7:27-34.
Boateng, M., Okai, D. B., Baah, J. & Donkoh, A. (2008). Palm kernel cake
extraction and utilisation in pig and poultry diets in Ghana. Volume
20, Article #99. Retrieved August 17, 2008, from
http://www.lrrd.org/lrrd20/7/boat20099.htm
Boucher, S. E., Pedersen, C., Stein, H. H. & Schwab, C. G. (2009). Evaluation
of the furosine and homoarginine methods for determining reactive
Lys in rumen-undegraded protein. J. Dairy Sci. 92:3951-3958.
Cahaner, A. (1990). Genotype by environment interaction in poultry. In:
Proceedings of the 4th World Conference of Genetics Applied to
Livestock Production, Edinburgh, pp. 13–20.
Cahaner, A., Deeb, N., & Gutman, M. (1993). Effects of the plumage
reducing naked neck (Na) gene on the performance of fast
growing broilers at normal and high temperatures.
Poultry Science 72, 767–775.
Cameron, N. D. (1997). Selection indices and prediction of genetic merit in
animal breeding. CAB International, Wallingford.
Cavero, D.P., Icken, W., Schmutz, M. & Preisinger, R. (2011). Considering
genotype by environment interaction for selection of laying hens.
129
Paper presented at the XXII Latin American Poultry Congress
2011. Ciudad Autónoma de Buenos Aires, Buenos Aires
Argentina.
Cherian, G.F., Wolfe, W., & Sim, J.S. (1996). Dietary oils with added
tocopherols: effect on egg or tissue tocopherols fatty acids, and
oxidative stability. Poultry Science, 75; 423-431
Chiba, L. I. (2014). Animal Nutrition Handbook. Willey-Blackwell, A
JohnWiley & Sons, Inc.,Hoboken, NJ.
Chinajariyawong, C. & Muangkeow, N. (2011). Carcass yield and visceral
organs of broiler chickens fed palm kernel meal or Aspergillus
wentii TISTR 3075 fermented palm kernel meal. Walailak J Sci &
Tech 2011; 8(2): 175‐185.
Chin, F. Y. (1991). Oil palm – A rich source of animal feed. In: Asian
livestock, PHCA public. Bangkok, Thailand
Chin, F. Y. (1995). Palm kernel as supplement for fattening and dairy cattle in
Malaysia. Wisama Chase Perdena, Jalan Semantan, Bukit
Damansara, Malaysia.
Chineke, C. A., Ologun, A. G., & Ikeobi, C. O. N. (2006). Haematological
parameters in rabbit breeds and crosses in humid tropics. Pakistan
Journal of Biological Sciences, 9(11), 2102-2106.
Church, J. P., Judd, J. T., Young, C. W., Kebay, J. L., & Kim. W. W. (1984).
Relationships among diet constituents and specific serum clinical
components of subjects eating self-selected diets. Afric. J. Clin.
Nutr., 40:1338-1344.
130
Clinical Diagnostic Division. (1990). Veterinary Reference Guide, Eastman
Kodak Company, Rochester, New York.
Clubb, S.L. & Schubot, R. M. (1991). Haematological and serum
biochemical reference ntervals in juvenile cock. J. Vet. Med., 5: 16
-26.
Coon , C. N., Leske, K. L., Akavanhican, O. & Cheng, T. K. (1990). Effect of
oligosaccharide free soyabean meal on true metabolisable energy
and fibre digestion in adult rooster. Poultry Science, 69: 787-793.
Deaton, J. M., Kubena, L. F., Reece, F. N. & Lott, B. D. (1977). Effect of
dietary fiber on the performance of laying hens. British Journal of
Poultry Science 18: 711-714
Deeb, N. & Cahaner, A. (1999). The effects of naked neck genotypes,
ambient temperature, and feeding status and their interactions on
body temperature and performance of broilers. Poultry Science 78,
1341-1346.
Deeb, N. & Cahaner, A. (2001). Genotype-by-environment interaction with
broiler genotypes differing in growth rate. 1. The effects of high
ambient temperature and naked-neck genotype on lines differing in
genetic background. Poultry Science 80, 695–70
Donkoh, A. & Attoh-Kotoku, V. (2009). Nutritive value of feedstuffs for
poultry in Ghana: chemical composition, apparent metabolizable
energy and ileal amino acid digestibility. Livestock
Research for Rural Development. Volume 21, Article #32.
Retrieved November 6, 2015, from
http://www.lrrd.org/lrrd21/3/donk21032.htm
131
Durai, P. C., Maruthai, T. P. T., Arumugam, S. S., & Venugopal, O. A.
(2012). Haematological profile and erythrocyte indices in different
breeds of poultry. International Journal of Livestock Research,
2(3), 89-92.
Egenuka, F. C., Opara, M.N., Okoli, I. C. & Okeudo, N. J. (2013). Effect of
different dietary levels of palm kernel cake on growth, percentage
organ weights, haematological profile and serum biochemistry of
pullets. Journal of Agricultural Technology 9(1):1-10.
El-Safty, S.A., Ali, U.M. & Fathi, M.M. (2006). Immunological parameters
and laying performance of naked neck and normally feathered
genotypes of chicken under winter conditions of Egypt. Int. J.
Poult. Sci., 5: 780-785.
Ekiz, E. E., & Yalcintan (2013). Comparison of certain haematological and
biochemical parameters regarding pre-slaughter stress in saanen,
maltese, gokceada and hair goat kids. Journal of the Faculty of
Veterinary Medicine, Istanbul University, 39(2), 189-196.
Esminger, M. E. (1992). Poultry Science (3rd
ed.). Interstate Publishers, Inc.,
Danville, Illinois. pp. 106-160
Esuga, P. M. ; Sekoni, A. A. ; Omage, J. J. & Bawa, G. S., (2008). Evaluation
of enzyme (Maxigrain®) supplementation of graded levels of palm
kernel meal (PKM) on the performance of broiler chickens.
Pakistan J. Nutr., 7 (4): 607-613
Esonu, B.O., Opara, M.N., Okoli, I.C., Obikaonu, H.O., Udedibie, C. &
Iheshiulor, O.O.M. (2006). Physiological Response of laying Birds
to Neem (Azadiracta indica) Leaf Meal-based Diets: Body Weight,
132
Organ Characteristics and Heamatology. Online Journal of Health
Allied Sciences. 2:2-5.http://www.ojhas.org/issue18/2006-2-4.htm.
Etim, N. N., Enyenihi, G. E., Williams, M. E., Udo, M. D. & Offiong, E. E.
A. (2013). Haematological parameters: Indicators of the
physiological status of farm animals. British Journal of Science 10
(1): 33-45
Ezieshi, E. V. & Olomu, J. M. (2004). Comparative performance of broiler
chickens fed varying levels of palm kernel cake and maize offal.
Pak. J. Nutr., 3:254-257
Ezieshi, E. V. & Olomu, J. M. (2008). Nutritional evaluation of palm kernel
meal types: 2. Effects on live performance and nutrient retention in
broiler chicken diets. African Journal of Biotechnology 7 (8): 1171
1175
http://www.academicjournals.org/AJB/PDF/pdf2008/17Apr/Ezies
i%20and 20Olomu.pdf
Falconer, D.S. & Mackay, T.F.C. (1996). Introduction to quantitative
genetics (4th
edition). Longman, Harlow, UK.
FAO. (1996). Management of Waste from Animal Product Processing.
Wageningen, The Netherlands
FAO. (1998). Quarterly Bulletin of Statistics. Vol. 11, No.1/2
FAO. (2004). FAOSTAT Citation database results. Retrieved November 25,
2008, from http://faostat.fao.org/faostat
FAO. (2011a). Draft guidelines on phenotypic characterization of animal
genetic resources. Food and Agriculture Organisation of the United
Nations. Rome.
133
FAO. (2011b). FAOSTAT. World wheat production. Retrieved January 20,
2015, from http://faostat.fao.org/default.aspx
Filipowiæ N., Stojeviæ Z., Milinkoviæ-tur S., Ljubiæ B.B. & Zdelar-tukM.
(2007). Changes in concentration andfractions of blood serum
proteins of chickens during fattening. Vet. Arhiv. 77: 319-326.
Fischbach, F.T. & Dunning, M.B. (2004). A Manual of Laboratory and
Diagnostic Tests. 7th Edition. Lippincott Williams and Wilkins,
Philadelphia. PA, USA.
Flake, L. & Ashitey, E. (2008). Ghana Poultry and Products Annual Report,
2008. Foreign Agricultural Service; United States Development
Agency (USAID). Gain Report Number GH8006.
Flock, D. K., Ameli, H., & Glodek, P. (1991). Inbreeding and heterosis effects
on quantitative traits in a white leghorn population under long-term
reciprocal recurrent selection. Brit. Poult. Sci. 32, 451-462.
Forbes, J.M. (1998). Voluntary food intake and diet selection in farm animal.
Anthony Rowe Ltd, Chippenham, Wilt Shire, U.K.
Friedman, M. (1996). Food browning and its prevention: an overview. J.
Agric. Food Chem. 44:631-653.
Gernsten, T. (2006). Praktikum der Hundeklinik (10 ed.). Available at:
en.wikipedia.org/wiki/mean_corpuscular_haemoglobin.
Gernsten, T. (2009). Medline Plus Medical Encyclopedia: RBC Indices.
Available at: en.wikipedia.org/wiki/red_blood_cell_indices.
Göhl, B. O. (1998). Tropical feeds. FAO Software Developed by Speedy, A.
& Waltham, N. Version 8.
134
Gyamera, E. (2010). Palm Kernel Oil Residue (PKOR) as livestock feed in
Ghana: A case study of four (4) districts in the Central Region
(Unpublished master's thesis). University of Cape Coast, Cape
Coast, Ghana.
Hammami, H., Rekik, B., & Gengler, N. (2009). Genotype by environment
interaction in dairy cattle. Biotechnol. Agron. Soc. Environ.
13(1), 155-164
Hartley, C. W. S. (1977). The Oil Palm. 2nd Edition. Longman Group Ltd.
Pp.701-703.
Havenstein, G. B., Ferket, P. R., Scheideler, S. E. & Larson, B. T. (1994).
Growth, livability and feed conversion of 1957 vs. 1991 broilers
when fed "typical" 1957 and 1991 broiler diets. Poultry Sci. 73:
1785 – 1794.
Hetland, H. & Svihus, B. (2007). Inclusion of dust bathing materials affects
nutrient digestion and gut physiology of layers. J. Appl. Poult. Res.
16:22–26
Heuzé, V., Tran, G., Baumont, R., Lebas, F., Lessire, M., Noblet, J., &
Renaudeau, D. (2013). Wheat bran.
http://www.feedipedia.org/node/726
Hishamuddin, M. A. (2001). Malaysian palm kernel cake as animal feed.
Malaysian Palm Oil Board, Kuala Lumpur, Malaysia.
Hoste, S. (2007). Genetics and nutrition: what’s around the corner? In P.C.
Garnsworthy & J. Wiseman, Eds. Recent advances in animal
nutrition, pp. 275–288. Nottingham, UK, Nottingham
University Press.
135
Hristakieva, P., Mincheva, N., Oblakova, M., Lalev, M. & Ivanova, I.
(2014). Effect of genotype on production traits in broiler chickens.
Slovak J. Anim. Sci., 47, (1): 19-24
Hutagalung, R. I. (1981). The use of tree crops and their by-products for
intensive animal production. Intensive Animal Production in
Developing Countries (Smith, A.J. and Gunn, R.G. eds). pp. 151
184
Hutagalung, R.I., Mahyuddin, M. & Jalaludin, S. C. (1982). Feeds for farm
animal from the oil palm. In: The oil palm in Agriculture in the
Eighties. Volume II. Pushprajah and C. Poh-Soon, editors.
Incorporated Society of Planters, Kuala Lumpur, Pp 609-622.
Ibrahim, A. A., Aliyu, A. A. J., Wada, N. I. & Hassan A.M. (2012). Effect of
sex and genotype on blood serum electrolytes and biochemical
parameters of Nigerian indigenous chickens. Iranian Journal of
Applied Animal Science 2(4), 361-365
Isaac, L. J., Abah, G., Akpan, B. and Ekaette, I. U. (2013). Haematological
properties of different breeds and sexes of rabbits. Proc. of the 18th
Annual Conf. of Anim. Sci. Assoc. of Nig., pp. 24-27.
Islam, M. S., Lucky, N. S., Islam, M. R., Ahad, A., Das, B. R., Rahman, M.
M., & Siddiui, M. S. I. (2004). Haematological parameters of
Fayoumi, Assil and local chickens reared in Sylhet Region in
Bangladesh. International Journal of Poultry Science, 3(2), 144
-147.
Iwuji, T. C., & Herbert, U. (2012). Haematological and serum biochemical
characteristics of rabbit bucks fed diets containing garcimiola kola
136
seed meal. Proceedings of 37th Annual Conference of Nigerian
Society for Animal Production (pp.87-89).
Jain N.C. (1993): Comparative hematology of common domestic animals. In:
Jain N.C. (ed.): Essentials of Veterinary Hematology. 1st ed. Lea &
Febiger, Philadelphia, PA. 19–53
Jalaludin, S. (1996). Integrated animal production in the oil palm plantation.
Universiti Pertanian, Selangor, Malaysia.
Johnston, J. K., & Morris, D. D. (1996). Alterations in blood proteins. In B. P.
Smith (Ed.), International Animal Medicine (2nd ed.). USA:
Mosby Publishers.
Kang, M. S. (2002). Quantitative genetics, genomics and plant breeding.
CABI Publishing, New York, NY.
Kekeocha, C.C. (1995). Poultry production handbook. Pfizer Corporation,
Nairobi, pp. 183
Ladokun A.O., Yakubu A., Otite J.R., Omeje J.N., Sokunbi O.A. & Onyeji
E. (2008). Haematological and biochemical indices of naked neck
and normally feathered Nigerian indigenous chickens in a sub
humid tropical environment. Int. J. Poult. Sci. 7(1), 55-58.
Lander, E.S. & Schork, N.J. (1994). Genetic dissection of complex traits.
Science 265: 2037–2048.
Leenstra, F. (1989). Interaction between genotype of the broiler and its
nutrition. In: Merat, P. (ed.) Genotype × Environment Interactions
in Poultry Production. Institut National de la Recherche
Agronomique, Jouy-en-Josas, France, pp. 29–40.
137
Lessler, J., Ranells, N., & Choice, G. (2007). Grower Guidelines for Poultry
and Fowl Processing. North Carolina State University Cooperative
Extension. NC, USA
Liener; I. E. (1986). Nutritional significance of lectins in the diet. In I. E.
Liener, N. Sharon, & I. J. Goldstein (eds.), The Lectins: Properties,
Functions and Applications in Biology and Medicine (pp. 527
547). Academic Pr, Orlando, FL.
Lumeij J.T. (1997). Avian Clinical Biochemistry. In: Clinic
Biochemistry of Domestic Animals. J.J. Kaneko, J.W. Harvey &
M.L. Bruss, (Eds)., Academic press, San Diego. Pp. 857-883.
Lwelamira, J. (2012). Genotype-Environmental (G X E) Interaction for Body
Weights for Kuchi Chicken Ecotype of Tanzania Reared Under
Intensive and Extensive. Global Journal of Medical research.
Global Journals Inc. (USA)
Lynch, M. & Walsh, B. (1998). Genetics and analysis of quantitative traits.
Sunderland, MA: Sinauer.
Mackay, T.F.C. (1996). The nature of quantitative genetic variation revisited:
lessons from Drosophila bristles. BioEssays 18: 113–121.
Manzocco, L., Calligaris, S., Mastrocola, D., Nicoli, M. & Lerici, C. (2011).
Review of non-enzymatic browning and antioxiidant capacity in
proccesed food. Trends in Food Science and Technology 11. 340
-346
Maricle, E. A. (2008). Genotype by Environment interaction estimated by
using reaction norms in cattle. Master’s Thesis presented to the
138
Faculty of the Graduate School University of Missouri–Columbia
(unpublished).
Mary-Priya. & Gomathy V.S. (2008). Haematological and blood biochemicals
in male and female turkeys of different age groups. Tamilnadu J.
Vet. Anim. Sci. 4, 60-68.
Mathur, P. K. (2003): Genotype-Environment Interactions: Problems
Associated with Selection for Increased Production. In: Poultry
Genetics, Breeding and Biotechnology. (Eds. Muir, W. M., Aggrey,
S.E.) 83-99. CABI Publ. Wallingford, UK.
Mathur, P.K. and Horst, P. (1994a). Methods for evaluating genotype ×
environment interactions illustrated with laying hens. Journal of
Animal Breeding and Genetics 111, 265–288.
Maton, A., Hopkins, R. L. J., McLaughlin, C. W., Johnson, S., Warner, C. W.,
LaHart, D., & Wright, J. D. (1993). Human Biology and Health.
Englewood Cliffs, New Jersey, USA: Prentice Hall.
Maxwell, M. H., Robertson, G. W., Spences and McCongrouodala, C. C.
(1990). Composition of haematological values in restricted and ad
libitum feed domesticated fowls. RBC Characteristics. British
Poultry, 60:1474-1484.
McDonald, P., Edwards, R. A., & Greenhalgh, J. F. D. (1988). Palm kernel
meal. In Animal Nutrition (pp. 462-463) (4th
ed). Longman,
Harlow.
McDonald, P., Edwards, R. A. & Greenhalgh, J. F. D. (2002). Animal
Nutrition. 6th Edition. Longman, London and New York.
139
McKay, J. C., Barton, N. F., Koerhuis, A. N. M. & McAdam, J. (2000). The
challenge of genetic change in the broiler chicken. Pages 1–7 in:
The Challenge of Genetic Change in Animal Production.
Occasional Publication No. 27. W. G. Hill, S. C. Bishop, B.
McGuirk, J. C. McKay, G. Simm, A. J. Webb, ed. British Society
of Animal Science, Edinburgh, UK.
Merck Manual (2012). Haematologic reference ranges. Mareck Veterinary
Manual. Merck Manuals.com
Ministry of Food and Agriculture-Statistics, Research and Information
Directorate (MoFA-SRID), 2011. “Agriculture in Ghana: Facts and
Figures (2010)”.
Mitruka, B. M., & Rawnsley, H. M. (1977). Clinical biochemical and
hematological reference values in normal experimental animals
USA: Masson Publishing Inc. (p.134-135)
Mmereole, F. U. C. (2008). The effects of replacing groundnut cake
rubber seed meal on the haematological and serological indices of
broilers. International Journal of Poultry Science, 7(6), 622-624.
Mmereole, F.U.C. & Udeh, I. (2009). Genotype by diet interaction on body
weight of the local chicken and its crosses with Barred Plymouth
Rock. Int. J. Poult. Sci., 8 (5): 504-507
Musa, H. H., Chen, G. H., Cheng, J. H., Li, B. C. & Mekki, D. M. (2006).
Study on carcass characteristics of chicken breeds raised under the
intensive condition. Int. J. Poult. Sci., 5: 530-533
Nicoll, D., McPhee, S.J. & Pignone, M. (2001). Pocket Guide to Diagnostic
Tests. 3rd Edition. McGraw-Hill, New York, NY. United States.
140
NRC (National Research Council). (1994). Nutrient Requirements of Poultry.
9th rev. ed. Natl. Acad. Press, Washington, DC., USA
Nwokolo, E.N.; Bragg, D.B.; & Kitts, W.D. (1976). The availability of amino
acids from palm kernel, soyabean, cottonseed, and rapeseed meal
for the growing chick. Poultry Sci. 62:483-489
Nursten, H. (2005). The Maillard reaction. Chemistry, Biochemistry, and
Implications. Royal Society of Chemistry, Cambridge, UK.
Nyanu, C.A. (1999). Final Report on Poultry Production and Breeding
Technology Programme. Ministry of Food and Agriculture.
Odoi, F. N. A., Adam, I., Awuma, K. S., Adu, S. & Ayitey, A. N. A. (2007).
Stabilizing palm kernel oil residue (PKOR) for use in poultry and
livestock rations. Commissioned report (AgSSIP-RELC-World
Bank study).
Ojedapo, L. O., Akinokun, O., Adedeji, T. A., Olayeni, T. B., Ameen, S. A.
& Amao, S. R. (2008). Effect of strain and sex on carcass
characteristics of three commercial broilers reared in deep litter
system in derived savannah area of Nigeria. World J. Agric.
Sci., 4: 487-491
Ojewola, G. S., Oguike, M. A., Akomas, S. C., Likita, T., Onyiro, O. M. &
Wokocha, C. (2003). Comparison of the supplemental effects of
Roxazyme -G enzyme in palm kernel meal and brewers dried grain
based diets fed to male turkey poults. Nigeria Agricultural Journal
34: 116-124.
141
Ojewola, G. S. & Ozuo, U. K. (2006). Evaluation of palm kernel meal as
substitute for soybean meal in the diet of growing cockerels.
International Journal of Poultry Science 5(5): 401-403
Olabanji, R. O., Farinu, G. O., Akinlade, J. A. & Ojebiyi, O. O. (2007).
Growth performance and haematological characteristics of weaner
rabbits fed different levels of wild sunflower (Tithonia diversifolia
Hems L A. Gray) leaf blood meal mixture. Proc. of 32nd Animal
Conf. of Nig. Soc. For Anim. Prod., pp.207-209.
Olawumi, S. O. & Fagbuaro, S. S. (2011). Productive performance of three
commercial broiler genotypes reared in the derived savannah zone
of Nigeria. International Journal of Agricultural Research 6(11):
798-804
Olawumi, S.O., Fajemilehin, S.O. & Fagbuaro, S.S. (2012). Genotype × sex
interaction on carcass traits of three strains of commercial broiler
chickens. J. World’s Poult. Res. 2 (1): 21-24
Olorede, B.R. & Longe, O.G. (2000). Effect of replacing palm kernel cake
with sheabutter cake on egg quality characteristics, hematology and
serum chemistry of laying hens. Nigerian Journal of Animal
Production 27:19-23.
Olorede, B. R, Onifade, A., Okpara, A. O. & Babatunde, G. M. (1996).
Growth, nutrient retention, haematological and serum chemistry of
broiler chicken fed shea butter cake or palm kernel cake in the
humid tropics. Journal of Applied Animal research 10: 173-180.
Ologunowa, E. O., Chineke, C. A., Lasehinde, E. A. O., Ogunsusi, R. A.,
Aletor, V. A., Ologun, A. G., . . . Agbede, J. O. (2000). Rabbit
142
breeds analysis for haematological indices. Theses and
Dissertations (Animal Production and Health). Federal
University of Technology, Akure. Retrieved from
http://dspace.futa.edu.ng:8080/jspui/handle/123456789/1642.
Okai, D. B., Olympio, O. S., Bonsu, M. L. K. & Sam, E.S. 1994. Poultry and
Pig Production Handbook for the Ghanaian Farmer. Degraft
graphics and publications, Kumasi. P. 27.
Okantah, S.A, Aboe, P.A.T., Boa-Amponsem, K., Dorward, P.T. & Bryant, M.
J. (2005). Small-scale Poultry Production in Peri-urban Areas in
Ghana. A Technical Report on the DfID Livestock Production
Research Programme No. 7631. Animal Research Institute,
Achimota, Ghana.
Okeudo, N. J., Eboh K. V., Ndidi, V., IZugbekwe & Akanno, E. C. (2005).
Growth rate, carcass characteristics and organoleptic quality of
broiler fed graded levels of palm kernel cake. International
Journal of Poultry Science 4 (5): 330-333
Okeudo, N. J. Onyike, I. L. Okoli, C. V. & Chielo, I. L. (2006). Production
performance, meat quality and feed cost implications of utilizing
high levels of palm kernel cake in broiler finisher diets.
International Journal of Poultry Science 5 (12): 1160-1163
Okunlola, D. O., Olorunisomo, A. O., Aderinola, A. O., Agboola, A. S., &
Omole, O. G. (2012). Haematology and serum quality of red
Sokoto goats fed Baobab (Adansonia digitata) fruit meal as
supplement to guinea grass (Panicum maximum) (p.427-433).
143
Proceedings of the 17th Annual Conference of Animal Science
Association of Nigeria.
Onifade, A. A. & Babatunde, G. M. (1998). Comparison of the utilization of
palm kernel meal, Brewers dried grains and maize offal by broiler
chicks. British Poultry Science, 39: 245-250.
Onuh, S.O., Ortserga, D.D. & Okoh, J.J. (2010). Response of broiler chicken
to palm kernel cake and maize offal mixed in different ratios.
Pakistan journal of nutrition 9 (6): 516-519
Onwudike, O. C. (1986). Palm kernel meal as a feed for poultry. 2. Diets
containing palm kernel meal for starter and grower pullets. Animal
Feed Science Technology 18: 187-194
Osei, S. A. & Amo, J. (1987). Palm Kernel cake as a broiler feed ingredient.
Poultry Science 66: 1870-1873.
Oyawoye, B. M., & Ogunkunle, H. N. (2004). Biochemical and
haematological reference values in normal experimental animals
(p.212-218). New York: Masson.
Pahm, A. A., Pedersen, C. & Stein, H. H. (2008). Application of the reactive
Lys procedure to estimate Lys digestibility in distillers dried grains
with solubles fed to growing pigs. J. Agric. Food Chem. 56:9441
-9446.
Palo, P.E., Sell, J.L., Piquer, F.J., Vilaseca, L. & Soto-Salanova, M.F. (1995).
Effects of early restriction on broiler chickens. 2 performance and
digestive enzyme activities. Poultry Science 74:1470-1483.
144
Petek, S., Yalcin, S., Turkmut, L., Ozkan, S. & Cahanar, A. (1999). Season
by genotype interaction related to broiler growth rate and heat
tolerance. Poultry Science 78, 1353–1358.
Peters, S. O., Gunn, H. H., Imumorin, I. G., Agaviezor, B. O., & Ikeobi, C. O.
(2011). Haematological studies on frizzled and naked neck
genotypes of Nigerian native chickens. Tropical Animal Health
and Production, 43(3), 631-638.
Pingel, H., Hailu, C., Dang, T.H., Al-Mahrous, M. & Lengerken, G. (1995)
Effect of selection for plasma corticosterone level and feed
conversion ratio in dwarf White Rocks on adaptability to hot
temperature during growth. Archive für Tierzucht 38, 503
-511.
Prabhakaran, V.T. & Jain, J.P. (1994). Statistical Techniques for Studying
Genotype–Environment Interactions. South Asian Publishers Pvt.
Ltd, New Delhi, India.
Preisinger, R. & Flock, D.K. (2000). Genetic changes in layer breeding:
historical trends and future prospects. Pages 20-28 in: The
challenge of genetic change in animal production. Eds. W.G. Hill,
S.C. Bishop, B. McGuirk, J.C. McKay, G. Simm and A.J. Webb.
British Society of Animal Science, Edinburgh. Occasional
publication no. 27.
Purves, W. K., Sadava, D., Orians, G. H., & Heller, H. C. (2003). Life: The
science of Biology (7th ed., p.954). Sinauer Associates and W. H.
Freeman.
145
Pustzai, A., Clarke, E.M.W., King, T.P. & Stewart, J.C. (1979). Nutritional
evaluation of kidney beans (Phaseolus vulgaris): Chemical
composition, lectin content and nutritional value of selected
cultivars. Journal of Science Food Agriculture. 30:843-848.
Pond, W. G., Church, D. C. & Pond, K. R. (1995). Basic Animal Nutrition and
Feeding. 4th
edition. John Wiley & Sons, Inc. New York.
Razuki, W.M. & Al–Rawi, A.A. (2007). The response of two broiler
genotypes to various dietary protein levels. Iraqi Poult. Sci. J., 2:
234-245
Rentfrow, G. (2010). How much meat to expect from a carcass. A
consumer’s guide to purchasing freezer meats. Cooperative
Extension Service, University of Kentucky College of
Agriculture, Lexington, KY, 40546
Rhule, S.W.A. (1996). Growth rate and carcass characteristics of pigs fed diet
containing palm kernel cake. Anim. Feed. Sci. Tec., 61: 167-172
Ritchie, B. W., Harrison, J. G. & Harrison, R. L. (1994). Avian Medicine,
Winger’s Publishing, Inc, Florida.
Salami, R.I. (1995). A solution to the problem of increasing cost of poultry
feeds: A review of Literature. In Toby, T. U. (Ed.) New Direction
in Nigerian Vocational Education: Concepts and Research. pp. 143
–145.
Schalm, O. W., Jain, N. C., & Carroll, E. J. (1975). Veterinity haematology
(3rd ed.). USA: Lea & Fabiger, Philadelphia. pp.15-218.
Scott, M.L. & Dean, W.F. (1991). Nutrition and Management of ducks.
Cornell University Ithaca Press, New York, USA.
146
Senköylü, N., Hasan Akürek, H., Ersin Samli & Nuri Yurdakurban, (2004).
Performance and egg weight of laying hens on the diets with
various by-product oils from the oilseed extraction refinery.
Pakistan Journal of Nutrition 3 (1): 33-42, 2004.
hppt://www.pjbs.org/pjnonline/fin175.pdf. (20/04/2009).
Shakila, S., Reddy, P. S., Reddy, P.V.V.S., Ramana, J.V. & Ravi, A. (2012).
Effect of Palm Kernel Meal on the Performance of Broilers.
Tamilnadu J. Veterinary & Animal Sciences 8 (4) 227-234
Sheridan, A. K., 1990. Genotype x environment interaction. Pages 897-912 in:
Poultry Breeding and Genetics. R. D. Crawford, ed. Elsevier,
Amsterdam, the Netherlands.
Sidell, B. D., & O’ Brien, K. M. (2006). When bad things happen to good fish:
the loss of haemoglobin and myoglobin expression in Antarctic
icefishes. The Journal of Experimental Biology, 209, 1791-1802.
Singh, B.D., Choudhuri, P., Chandra, P., Malik, S. & Singh, B.P. (1998).
Effects of Naked neck gene on broiler performance of two
populations in tropical climate. In: Proceedings of 6th World
Congress on Genetics Applied to Livestock Production, Armidale,
Australia, pp. 361–364.
Soetan, K. O., Akinrinde, A. S., & Ajibade, T. O. (2013). Preliminary studies
on the haematological parameters of cockerels fed raw and
processed guinea corn (Sorghum bicolor) (p. 49-52). Proceedings
of 38th Annual Conference of Nigerian Society for Animal
Production.
147
Soltan, M. A. (2009). Growth performance, immune response and carcass
traits of broiler chicks fed on graded levels of palm kernel cake
without or with enzyme supplementation. Livestock Research
for Rural Development 21 (3) 2009.
Sterling, K. G., Pesti, G. M. and Bakalli, R. I. (2006). Performance of different
broiler genotypes fed diets with varying levels of dietary crude
protein and lysine. Poultry Science 85:1045–1054
Sundu, B. & Dingle, J. (2003). Use of enzymes to improve the nutritional
value of palm kernel meal and copra meal. Proceedings of
Queensland Poultry Science Symposium Australia 1 (14): 1-15
Sundu, B. Kumar, A. & Dingle, J. (2006). Palm Kernel meal in broiler diets:
Effect on chickens performance and health. Worlds Poultry
Science Journal, 62:316-325.
Szabó A., Mézes M., Horn P., Sütö Z., Bázár G. Y. and Romvári R. (2005).
Developmental dynamics of some blood biochemical parameters in
the growing turkey (Meleagris gallopavo). Acta Vet. Hung. 53:
397-409.
Taha, A. E., Abd El-Ghany, F. A. & Sharaf, M. M. (2010). Strain and sex
effect on productive and slaughter performance of developed local
Egyptian and Canadian chicken strains. Egypt Poult Sci., 30: 1059
-1072
Tang, S. T. & Teoh, P. K. (1995). Palm kernel oil extraction- The Malaysian
experience. Paper presented to the World Conference on
Processing of palm kernel and coconut oil. Kuala Lumpur,
Malaysia.
148
Togun, V. A. & Oseni, B. S. A. (2005). Effect of low level inclusion of
biscuit dust in broiler finisher diet on pre-pubertal growth and some
haematological parameters of unsexed broilers. Res. Comm. Anim.
Sci., 1(2):10-14.
Togun, V. A., Oseni, B. S. A., Ogundipe, J. A., Arewa, T. R., Hammed, A. A.,
Ajonijebu, D. C., ... Mustapha, F. (2007). Effects of chronic lead
administration on the haematological parameters of rabbit–a
preliminary study. Proc. of the 41st Conf. Of the Agric. Soc. Of
Nig., p.341.
Tung, H. T. Cook. F. W., Wyatt, R. D. and Hamitton, P. B. 1975. The
anaemia caused by Aflatoxin. Poultry Science 54: 1962-1969.
Ugwuene, M. C. (2011). Effect of dietary palm kernel meal for maize on the
haematological and serum chemistry of broiler turkey. Nigerian
Journal of Animal Science, 13, 93-103.
VSN International (2011). GenStat for Windows 14th Edition. VSN
International, Hemel Hempstead, UK. Web page:
GenStat.co.uk
Wang, S.Y., Bottje, W., Maynard, P., Dibner, J. & Shermer, W. (1997).
Effect of Santoquin and oxidized fat on liver and intestinal
glutathione in broilers. Poultry Science; 76 (7): 961-967
Wardlaw, G. M. (1991). Contemporary nutrition issues and insight. (4th ed.)
Mcgraw-Hill Inc. pp. 155-156.
Yalcin, S., Settar, P., Ozkan, S. & Cahaner, A. (1997). Comparative
Evaluation of Three Commercial Broiler Stocks inHot Versus
Temperate Climates. Poultry Sci. 76:921–929
149
Yaman M. A., Kita K. & Okumura J. (2000). Different responses of protein
synthesis to refeeding in various muscles of fasted chicks. Br.
Poultry Sci. 41: 224-228.
Yangtul, T. (2010). The effect of PKOR inclusion in commercial layer rations.
(Unpublished master's thesis). University of Cape Coast, Cape
Coast, Ghana.
Yaylayan, V. A., & A. Huyghues-Despointes (1994). Chemistry of Amadori
rearrangement products: analysis, synthesis, kinetics, reactions, and
spectroscopic properties. Crit. Rev. Food Sci. Nutr.34:321-369.
Yeong, S. W., Mukherjee, T. K. & Hutagalung, R. I. (1983). The nutritive
value of palm kernel cake as a feedstuff for poultry. In: Proc. Of
the National Workshop on Oil Palm By-Product Utilization, Kuala
Lumpur, Malaysia. pp. 100-107
Yunis, R. & Cahaner, A. (1999). The effects of the naked neck (Na) and
frizzle (F) genes on growth and meat yield of broilers and their
interactions with ambient temperatures and potential growth rate.
Poultry Science 78, 1347–1352.
150
APPENDICES
APPENDIX A: GROWTH PERFORMANCE PARAMETERS
Analysis of variance
Variate: INI_WT_g
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 724.5 724.5 0.80 0.389
FEED_A 2 122.9 61.4 0.07 0.935
BREED_B.FEED_A 2 3626.4 1813.2 2.00 0.179
Residual 12 10902.2 908.5
Total 17 15376.0
Tables of means
Variate: INI_WT_g
Grand mean 616.0
BREED_B Cobb 500 Ross 308
609.6 622.3
FEED_A 0.0 0.1 0.2
612.6 616.4 618.9
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 590.0 628.5 610.4
Ross 308 635.1 604.4 627.4
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
151
s.e.d. 14.21 17.40 24.61
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 30.96 37.92 53.62
Analysis of variance
Variate: FIN_WT_g
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 31000. 31000. 2.67 0.128
FEED_A 2 160384. 80192. 6.90 0.010
BREED_B.FEED_A 2 9156. 4578. 0.39 0.683
Residual 12 139529. 11627.
Total 17 340070.
Tables of means
Variate: FIN_WT_g
Grand mean 2716.
BREED_B Cobb 500 Ross 308
2674. 2757.
FEED_A 0.0 0.1 0.2
2849. 2654. 2644.
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 2783. 2607. 2633.
Ross 308 2915. 2701. 2656.
152
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 50.8 62.3 88.0
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 110.8 135.6 191.8
Analysis of variance
Variate: WT_GAIN_g
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 22246. 22246. 2.02 0.180
FEED_A 2 168823. 84412. 7.68 0.007
BREED_B.FEED_A 2 10072. 5036. 0.46 0.643
Residual 12 131940. 10995.
Total 17 333081.
Tables of means
Variate: WT_GAIN_g
Grand mean 2100.
BREED_B Cobb 500 Ross 308
2065. 2135.
153
FEED_A 0.0 0.1 0.2
2236. 2037. 2025.
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 2193. 1978. 2022.
Ross 308 2280. 2096. 2028.
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 49.4 60.5 85.6
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 107.7 131.9 186.5
Analysis of variance
Variate: ADG_g_day
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 18.605 18.605 2.10 0.173
FEED_A 2 137.428 68.714 7.76 0.007
BREED_B.FEED_A 2 8.023 4.011 0.45 0.646
Residual 12 106.217 8.851
Total 17 270.273
154
Tables of mean
Variate: ADG_g_day
Grand mean 60.01
BREED_B Cobb 500 Ross 308
58.99 61.02
FEED_A 0.0 0.1 0.2
63.91 58.22 57.88
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 62.68 56.51 57.77
Ross 308 65.13 59.93 58.00
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 1.402 1.718 2.429
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 3.056 3.743 5.293
Analysis of variance
Variate: FI_PER_BIRD_g
155
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 2481. 2481. 0.03 0.872
FEED_A 2 965645. 482823. 5.29 0.022
BREED_B.FEED_A 2 32740. 16370. 0.18 0.838
Residual 12 1094598. 91217.
Total 17 2095465.
Tables of means
Variate: FI_PER_BIRD_g
Grand mean 5699.
BREED_B Cobb 500 Ross 308
5711. 5687.
FEED_A 0.0 0.1 0.2
5984. 5698. 5416.
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 5948. 5700. 5484.
Ross 308 6019. 5695. 5348.
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 142.4 174.4 246.6
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
156
rep. 9 6 3
d.f. 12 12 12
l.s.d. 310.2 379.9 537.3
Analysis of variance
Variate: WI_PER_BIRD_g
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 1258766. 1258766. 1.30 0.276
FEED_A 2 7041694. 3520847. 3.65 0.040
BREED_B.FEED_A 2 1239801. 619901. 0.64 0.543
Residual 12 11588488. 965707.
Total 17 21128750.
Tables of means
Variate: WI_PER_BIRD_g
Grand mean 10921.
BREED_B Cobb 500 Ross 308
11186. 10657.
FEED_A 0.0 0.1 0.2
11715. 10862. 10186.
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 11683. 11082. 10792.
Ross 308 11747. 10643. 9580.
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
157
d.f. 12 12 12
s.e.d. 463.3 567.4 802.4
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 1009.3 1236.2 1748.2
Analysis of variance
Variate: W_F_RATIO
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 0.02964 0.02964 0.92 0.355
FEED_A 2 0.01772 0.00886 0.28 0.763
BREED_B.FEED_A 2 0.01841 0.00921 0.29 0.756
Residual 12 0.38504 0.03209
Total 17 0.45082
Tables of mean
Variate: W_F_RATIO
Grand mean 1.917
BREED_B Cobb 500 Ross 308
1.957 1.876
FEED_A 0.0 0.1 0.2
1.958 1.910 1.882
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 1.963 1.944 1.965
158
Ross 308 1.953 1.876 1.799
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 0.0844 0.1034 0.1463
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 0.1840 0.2253 0.3187
Analysis of variance
Variate: W_F_RATIO
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 0.02964 0.02964 0.92 0.355
FEED_A 2 0.01772 0.00886 0.28 0.763
BREED_B.FEED_A 2 0.01841 0.00921 0.29 0.756
Residual 12 0.38504 0.03209
Total 17 0.45082
Tables of means
Variate: W_F_RATIO
Grand mean 1.917
159
BREED_B Cobb 500 Ross 308
1.957 1.876
FEED_A 0.0 0.1 0.2
1.958 1.910 1.882
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 1.963 1.944 1.965
Ross 308 1.953 1.876 1.799
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 0.0844 0.1034 0.1463
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 0.1840 0.2253 0.3187
Analysis of variance
Variate: FCR
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 0.051598 0.051598 8.97 0.011
FEED_A 2 0.061193 0.030597 5.32 0.022
BREED_B.FEED_A 2 0.008854 0.004427 0.77 0.485
160
Residual 12 0.069018 0.005751
Total 17 0.190663
Tables of means
Variate: FCR
Grand mean 2.717
BREED_B Cobb 500 Ross 308
2.770 2.663
FEED_A 0.0 0.1 0.2
2.676 2.799 2.675
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 2.713 2.884 2.714
Ross 308 2.640 2.714 2.636
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 0.0358 0.0438 0.0619
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 0.0779 0.0954 0.1349
161
APPENDIX B: CARCASS CHARACTERISTICS
Analysis of variance
Variate: Warm_Carcass_Wt
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 13880. 13880. 0.98 0.342
FEED_A 2 219751. 109876. 7.74 0.007
BREED_B.FEED_A 2 8763. 4381. 0.31 0.740
Residual 12 170320. 14193.
Total 17 412714.
Tables of means
Variate: Warm_Carcass_Wt
Grand mean 2098.
BREED_B Cobb 500 Ross 308
2070. 2126.
FEED_A 0.0 0.1 0.2
2254. 2027. 2012.
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 2213. 1981. 2015.
Ross 308 2294. 2073. 2009.
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 56.2 68.8 97.3
162
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 122.4 149.9 211.9
Analysis of variance
Variate: Warm_Dressing_%
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 0.777 0.777 0.16 0.696
FEED_A 2 35.376 17.688 3.64 0.058
BREED_B.FEED_A 2 3.000 1.500 0.31 0.740
Residual 12 58.350 4.863
Total 17 97.504
Tables of means
Variate: Warm_Dressing_%
Grand mean 77.17
BREED_B Cobb 500 Ross 308
77.41 77.11
FEED_A 0.0 0.1 0.2
78.14 76.38 75.99
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 79.57 76.02 76.54
Ross 308 78.71 76.75 75.64
163
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 1.040 1.273 1.800
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 2.265 2.764 3.923
Analysis of variance
Variate: Chilled_WT
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 14556. 14556. 0.98 0.343
FEED_A 2 98644. 49322. 3.30 0.072
BREED_B.FEED_A 2 7973. 3986. 0.27 0.770
Residual 12 179085. 14924.
Total 17 300258.
Tables of means
Variate: Chilled_WT
Grand mean 2066.
BREED_B Cobb 500 Ross 308
2030. 2094.
164
FEED_A 0.0 0.1 0.2
2130. 1994. 1989.
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 2121. 1960. 1983.
Ross 308 2200. 2045. 1979.
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 57.6 70.5 99.7
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 125.5 153.7 217.3
Analysis of variance
Variate: Chilled_Dressing%
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 0.952 0.952 0.20 0.666
FEED_A 2 19.055 9.528 1.95 0.184
BREED_B.FEED_A 2 2.508 1.254 0.26 0.777
Residual 12 58.532 4.878
Total 17 81.048
165
Tables of means
Variate: Chilled_Dressing%
Grand mean 76.28
BREED_B Cobb 500 Ross 308
76.51 76.85
FEED_A 0.0 0.1 0.2
77.73 75.62 75.48
BREED_B FEED_A 0.0 0.1 0.2
Cobb 76.21 75.18 75.31
Ross 75.47 75.71 74.51
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 1.041 1.275 1.803
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 2.268 2.778 3.929
Analysis of variance
Variate: Thigh_g
Source of variation d.f. s.s. m.s. v.r. F pr.
166
BREED_B 1 199.9 199.9 0.20 0.662
FEED_A 2 2744.1 1372.1 1.38 0.289
BREED_B.FEED_A 2 1358.3 679.1 0.68 0.524
Residual 12 11945.3 995.4
Total 17 16247.6
Tables of means
Variate: Thigh_g
Grand mean 300.6
BREED_B Cobb 500 Ross 308
303.9 297.3
FEED_A 0.0 0.1 0.2
316.6 286.5 298.7
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 309.7 300.8 301.3
Ross 308 323.5 272.2 296.1
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 14.87 18.22 25.76
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
167
d.f. 12 12 12
l.s.d. 32.41 39.69 56.13
Analysis of variance
Variate: Drumstick_g
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 151.3 151.3 0.20 0.662
FEED_A 2 2075.7 1037.8 1.38 0.290
BREED_B.FEED_A 2 1031.8 515.9 0.68 0.523
Residual 12 9055.6 754.6
Total 17 12314.4
Tables of means
Variate: Drumstick_g
Grand mean 261.6
BREED_B Cobb 500 Ross 308
264.5 258.7
FEED_A 0.0 0.1 0.2
275.5 249.3 259.9
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 269.4 261.8 262.2
Ross 308 281.6 236.9 257.6
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
168
s.e.d. 12.95 15.86 22.43
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 28.22 34.56 48.87
Analysis of variance
Variate: Breast_g
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 13282. 13282. 2.44 0.144
FEED_A 2 27558. 13779. 2.53 0.121
BREED_B.FEED_A 2 1406. 703. 0.13 0.880
Residual 12 65370. 5447.
Total 17 107616.
Tables of means
Variate: Breast_g
Grand mean 659.
BREED_B Cobb 500 Ross 308
632. 687.
FEED_A 0.0 0.1 0.2
706. 610. 663.
BREED_B FEED_A 0.0 0.1 0.2
Cobb 730. 649. 680.
Ross 681. 571. 645.
169
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 34.8 42.6 60.3
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 75.8 92.8 131.3
Analysis of variance
Variate: Back_g
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 1618. 1618. 0.46 0.508
FEED_A 2 24950. 12475. 3.58 0.060
BREED_B.FEED_A 2 1606. 803. 0.23 0.797
Residual 12 41784. 3482.
Total 17 69959.
Tables of means
Variate: Back_g
Grand mean 492.
BREED_B Cobb 500 Ross 308
501. 496.
170
FEED_A 0.0 0.1 0.2
543. 455. 477.
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 546. 478. 480.
Ross 308 540. 432. 475.
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 27.8 34.1 48.2
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 60.6 74.2 105.0
Analysis of variance
Variate: Wing_g
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 53. 53. 0.05 0.825
FEED_A 2 1081. 540. 0.52 0.609
BREED_B.FEED_A 2 1371. 686. 0.66 0.537
Residual 12 12558. 1046.
Total 17 15063.
171
Tables of means
Variate: Wing_g
Grand mean 227.4
BREED_B Cobb 500 Ross 308
225.6 229.4
FEED_A 0.0 0.1 0.2
236.9 218.0 227.1
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 223.6 225.6 227.7
Ross 308 250.3 210.3 226.6
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 15.25 18.68 26.41
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 33.23 40.69 57.55
Analysis of variance
Variate: Abdominal_Fat_Pad_g
Source of variation d.f. s.s. m.s. v.r. F pr.
172
BREED_B 1 0.5227 0.5227 2.02 0.181
FEED_A 2 0.6099 0.3050 1.18 0.341
BREED_B.FEED_A 2 0.0992 0.0496 0.19 0.828
Residual 12 3.1026 0.2585
Total 17 4.3343
Tables of means
Variate: Abdominal_Fat_Pad_g
Grand mean 18.62
BREED_B Cobb 500 Ross 308
18.45 18.79
FEED_A 0.0 0.1 0.2
18.38 18.66 18.82
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 18.13 18.59 18.63
Ross 308 18.63 18.73 19.01
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 0.240 0.294 0.415
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
173
d.f. 12 12 12
l.s.d. 0.522 0.640 0.905
Analysis of variance
Variate: Liver_g
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 1.23 1.23 0.02 0.891
FEED_A 2 61.01 30.51 0.49 0.626
BREED_B.FEED_A 2 66.52 33.26 0.53 0.601
Residual 12 750.12 62.51
Total 17 878.88
Tables of means
Variate: Liver_g
Grand mean 55.9
BREED_B Cobb 500 Ross 308
55.6 56.1
FEED_A 0.0 0.1 0.2
53.8 58.3 55.6
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 56.2 56.4 54.2
Ross 308 51.4 60.2 56.9
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
174
s.e.d. 3.73 4.56 6.46
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 8.12 9.95 14.07
Analysis of variance
Variate: Heart_g
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 1.0854 1.0854 2.52 0.139
FEED_A 2 0.4011 0.2006 0.47 0.639
BREED_B.FEED_A 2 0.9168 0.4584 1.06 0.376
Residual 12 5.1719 0.4310
Total 17 7.5752
Tables of means
Variate: Heart_g
Grand mean 12.16
BREED_B Cobb 500 Ross 308
11.92 12.41
FEED_A 0.0 0.1 0.2
12.02 12.37 12.10
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 11.83 12.36 11.55
Ross 308 12.20 12.37 12.65
175
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 0.309 0.379 0.536
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 0.674 0.826 1.168
Analysis of variance
Variate: Kidney_g
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 5.236 5.236 3.01 0.108
FEED_A 2 1.236 0.618 0.35 0.708
BREED_B.FEED_A 2 4.261 2.131 1.22 0.328
Residual 12 20.892 1.741
Total 17 31.625
Tables of means
Variate: Kidney_g
Grand mean 14.48
BREED_B Cobb 500 Ross 308
13.94 15.02
176
FEED_A 0.0 0.1 0.2
14.35 14.24 14.84
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 13.38 13.45 14.98
Ross 308 15.32 15.03 14.70
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 0.622 0.762 1.077
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 1.355 1.660 2.347
Analysis of variance
Variate: Spleen_g
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 0.4545 0.4545 2.35 0.151
FEED_A 2 0.0619 0.0309 0.16 0.854
BREED_B.FEED_A 2 0.1596 0.0798 0.41 0.670
Residual 12 2.3164 0.1930
177
Total 17 2.9924
Tables of means
Variate: Spleen_g
Grand mean 1.89
BREED_B Cobb 500 Ross 308
1.73 2.05
FEED_A 0.0 0.1 0.2
1.81 1.96 1.90
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 1.66 1.91 1.62
Ross 308 1.96 2.00 2.18
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 0.207 0.254 0.359
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 0.451 0.553 0.782
Analysis of variance
178
Variate: Gizzard_g
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 33.275 33.275 3.40 0.090
FEED_A 2 19.185 9.593 0.98 0.403
BREED_B.FEED_A 2 21.118 10.559 1.08 0.371
Residual 12 117.446 9.787
Total 17 191.023
Tables of means
Variate: Gizzard_g
Grand mean 58.19
BREED_B Cobb 500 Ross 308
56.83 59.55
FEED_A 0.0 0.1 0.2
59.63 57.24 57.71
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 56.74 56.53 57.23
Ross 308 62.11 57.95 58.20
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 1.475 1.806 2.554
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
179
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 3.213 3.935 5.565
APPENDIX C: SERUM BIOCHEMICAL PROFILE
Analysis of variance
Variate: Glucose
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 5478. 5478. 2.34 0.152
FEED_A 2 852. 426. 0.18 0.836
BREED_B.FEED_A 2 4866. 2433. 1.04 0.383
Residual 12 28037. 2336.
Total 17 39232.
Tables of means
Variate: Glucose
Grand mean 178.
BREED_B Cobb 500 Ross 308
196. 161.
FEED_A 0.0 0.1 0.2
172. 176. 188.
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 176. 216. 195.
Ross 308 167. 135. 181.
Standard errors of differences of means
180
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 22.8 27.9 39.5
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 49.6 60.8 86.0
Analysis of variance
Variate: Cholesterol
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 282.7 282.7 1.93 0.190
FEED_A 2 276.0 138.0 0.94 0.417
BREED_B.FEED_A 2 149.6 74.8 0.51 0.613
Residual 12 1759.2 146.6
Total 17 2467.5
Tables of means
Variate: Cholesterol
Grand mean 112.6
BREED_B Cobb 500 Ross 308
116.6 108.6
181
FEED_A 0.0 0.1 0.2
116.8 113.6 107.4
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 124.2 113.9 111.6
Ross 308 109.4 113.2 103.2
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 5.71 6.99 9.89
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 12.44 15.23 21.54
Analysis of variance
Variate: Triglyceride
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 43.6 43.6 0.06 0.813
FEED_A 2 4110.6 2055.3 2.76 0.103
BREED_B.FEED_A 2 2747.8 1373.9 1.84 0.200
Residual 12 8941.2 745.1
182
Total 17 15843.1
Tables of means
Variate: Triglyceride
Grand mean 96.1
BREED_B Cobb 500 Ross 308
94.6 97.7
FEED_A 0.0 0.1 0.2
112.3 75.9 100.2
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 99.6 68.3 115.9
Ross 308 125.0 83.6 84.6
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 12.87 15.76 22.29
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 28.04 34.34 48.56
Analysis of variance
Variate: Total_Protein
183
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 0.0296 0.0296 0.14 0.711
FEED_A 2 0.5537 0.2769 1.34 0.297
BREED_B.FEED_A 2 0.1053 0.0527 0.26 0.778
Residual 12 2.4719 0.2060
Total 17 3.1606
Tables of means
Variate: Total_Protein
Grand mean 3.90
BREED_B Cobb 500 Ross 308
3.86 3.94
FEED_A 0.0 0.1 0.2
3.76 3.79 4.14
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 3.61 3.83 4.12
Ross 308 3.90 3.75 4.17
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 0.214 0.262 0.371
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
184
rep. 9 6 3
d.f. 12 12 12
l.s.d. 0.466 0.571 0.807
Analysis of variance
Variate: Albumin
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 0.1494 0.1494 0.99 0.340
FEED_A 2 0.0037 0.0019 0.01 0.988
BREED_B.FEED_A 2 0.0903 0.0452 0.30 0.747
Residual 12 1.8127 0.1511
Total 17 2.0562
Tables of means
Variate: Albumin
Grand mean 2.110
BREED_B Cobb 500 Ross 308
2.019 2.201
FEED_A 0.0 0.1 0.2
2.117 2.090 2.123
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 2.110 1.910 2.037
Ross 308 2.123 2.270 2.210
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
185
d.f. 12 12 12
s.e.d. 0.1832 0.2244 0.3173
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 0.3992 0.4889 0.6914
Analysis of variance
Variate: ALKP
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 4919. 4919. 11.28 0.006
FEED_A 2 1581. 790. 0.18 0.836
BREED_B.FEED_A 2 1435. 717. 1.65 0.234
Residual 12 5234. 4362.
Total 17 11747.
Tables of means
Variate: ALKP
Grand mean 402.
BREED_B Cobb 500 Ross 308
95. 78.
FEED_A 0.0 0.1 0.2
81. 93. 80.
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 85.00 88.00 94.00
186
Ross 308 77.00 81.00 89.00
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 6.90 6.70 8.70
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 14.50 13.50 17.50
Analysis of variance
Variate: Sodium
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 391.9 391.9 0.87 0.370
FEED_A 2 1392.0 696.0 1.54 0.254
BREED_B.FEED_A 2 93.1 46.5 0.10 0.903
Residual 12 5427.1 452.3
Total 17 7304.1
Tables of means
Variate: Sodium
Grand mean 146.7
BREED_B Cobb 500 Ross 308
187
142.0 151.3
FEED_A 0.0 0.1 0.2
141.9 139.1 159.0
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 140.1 134.2 151.7
Ross 308 143.7 144.0 166.3
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 10.03 12.28 17.36
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 21.84 26.75 37.83
Analysis of variance
Variate: Potassium
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 130.36 130.36 2.12 0.171
FEED_A 2 154.74 77.37 1.26 0.319
188
BREED_B.FEED_A 2 77.05 38.52 0.63 0.551
Residual 12 738.33 61.53
Total 17 1100.47
Tables of means
Variate: Potassium
Grand mean 9.9
BREED_B Cobb 500 Ross 308
7.3 12.6
FEED_A 0.0 0.1 0.2
5.8 12.2 11.8
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 6.0 8.3 7.5
Ross 308 5.6 16.2 16.1
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 3.70 4.53 6.40
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 8.06 9.87 13.95
189
Analysis of variance
Variate: Calcium
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 3.371 3.371 3.15 0.101
FEED_A 2 1.624 0.812 0.76 0.489
BREED_B.FEED_A 2 0.793 0.396 0.37 0.698
Residual 12 12.828 1.069
Total 17 18.616
Tables of means
Variate: Calcium
Grand mean 10.13
BREED_B Cobb 500 Ross 308
10.57 9.70
FEED_A 0.0 0.1 0.2
9.78 10.12 10.51
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 10.50 10.46 10.75
Ross 308 9.05 9.78 10.28
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 0.487 0.597 0.844
190
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 1.062 1.301 1.839
Analysis of variance
Variate: Chloride
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 8602. 8602. 0.78 0.393
FEED_A 2 21372. 10686. 0.97 0.405
BREED_B.FEED_A 2 15754. 7877. 0.72 0.507
Residual 12 131618. 10968.
Total 17 177347.
Tables of means
Variate: Chloride
Grand mean 141.
BREED_B Cobb 500 Ross 308
119. 163.
FEED_A 0.0 0.1 0.2
114. 190. 119.
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 114. 126. 117.
Ross 308 114. 253. 122.
191
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 49.4 60.5 85.5
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 107.6 131.7 186.3
APPENDIX D: HAEMATOLOGICAL PROFILE
Analysis of variance
Variate: Hb
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 2.0605 2.0605 3.84 0.074
FEED_A 2 0.3782 0.1891 0.35 0.710
BREED_B.FEED_A 2 2.1189 1.0595 1.97 0.181
Residual 12 6.4399 0.5367
Total 17 10.9974
Tables of means
Variate: Hb
Grand mean 10.76
192
BREED_B Cobb 500 Ross 308
10.42 11.09
FEED_A 0.0 0.1 0.2
10.57 10.92 10.78
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 9.84 11.02 10.39
Ross 308 11.29 10.81 11.18
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 0.345 0.423 0.698
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 0.752 0.922 1.503
Analysis of variance
Variate: PCV
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 21.386 21.386 10.74 0.007
FEED_A 2 7.202 3.601 1.81 0.206
BREED_B.FEED_A 2 24.074 12.037 6.05 0.105
193
Residual 12 23.884 1.990
Total 17 76.546
Tables of means
Variate: PCV
Grand mean 27.33
BREED_B Cobb 500 Ross 308
26.24 28.42
FEED_A 0.0 0.1 0.2
26.48 27.99 27.53
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 26.54 28.22 26.60
Ross 308 28.86 27.75 28.45
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 0.665 0.815 1.152
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 1.449 1.775 2.510
194
Analysis of variance
Variate: RBC
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 0.17801 0.17801 3.18 0.100
FEED_A 2 0.05110 0.02555 0.46 0.644
BREED_B.FEED_A 2 0.14341 0.07171 1.28 0.313
Residual 12 0.67153 0.05596
Total 17 1.04405
Tables of means
Variate: RBC
Grand mean 2.282
BREED_B Cobb 500 Ross 308
2.182 2.381
FEED_A 0.0 0.1 0.2
2.220 2.275 2.350
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 2.077 2.300 2.170
Ross 308 2.363 2.250 2.530
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 0.1115 0.1366 0.1932
195
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 0.2430 0.2976 0.4208
Analysis of variance
Variate: WBC
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 0.00109 0.00109 0.02 0.890
FEED_A 2 0.44663 0.22332 4.07 0.045
BREED_B.FEED_A 2 0.03221 0.01611 0.29 0.751
Residual 12 0.65827 0.05486
Total 17 1.13820
Tables of means
Variate: WBC
Grand mean 2.290
BREED_B Cobb 500 Ross 308
2.298 2.282
FEED_A 0.0 0.1 0.2
2.420 2.382 2.068
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 2.423 2.340 2.130
Ross 308 2.417 2.423 2.007
196
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 0.1104 0.1352 0.1912
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 0.2406 0.2946 0.4167
Analysis of variance
Variate: MCV
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 11.60 11.60 0.15 0.708
FEED_A 2 26.33 13.16 0.17 0.848
BREED_B.FEED_A 2 178.71 89.36 1.14 0.353
Residual 12 942.93 78.58
Total 17 1159.56
Tables of means
Variate: MCV
Grand mean 119.7
BREED_B Cobb 500 Ross 308
120.5 118.9
197
FEED_A 0.0 0.1 0.2
119.7 121.2 118.3
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 116.4 122.7 122.6
Ross 308 123.0 119.8 114.0
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 4.18 5.12 7.24
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 9.10 11.15 15.77
Analysis of variance
Variate: MCH
Source of variation d.f. s.s. m.s. v.r. F pr.
BREED_B 1 3.111 3.111 0.43 0.525
FEED_A 2 9.901 4.951 0.68 0.523
BREED_B.FEED_A 2 12.240 6.120 0.84 0.454
Residual 12 86.917 7.243
Total 17 112.169
198
Tables of means
Variate: MCH
Grand mean 47.33
BREED_B Cobb 500 Ross 308
47.75 46.91
FEED_A 0.0 0.1 0.2
47.63 48.05 46.31
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 47.41 47.94 47.89
Ross 308 47.85 48.16 44.73
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 1.269 1.554 2.197
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
l.s.d. 2.764 3.385 4.788
Analysis of variance
Variate: MCHC
Source of variation d.f. s.s. m.s. v.r. F pr.
199
BREED_B 1 2.138 2.138 0.47 0.504
FEED_A 2 3.171 1.585 0.35 0.711
BREED_B.FEED_A 2 5.162 2.581 0.57 0.579
Residual 12 54.111 4.509
Total 17 64.582
Tables of means
Variate: MCHC
Grand mean 39.40
BREED_B Cobb 500 Ross 308
39.75 39.06
FEED_A 0.0 0.1 0.2
39.99 39.03 39.18
BREED_B FEED_A 0.0 0.1 0.2
Cobb 500 41.09 39.08 39.07
Ross 308 38.89 38.99 39.29
Standard errors of differences of means
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
d.f. 12 12 12
s.e.d. 1.001 1.226 1.734
Least significant differences of means (5% level)
Table BREED_B FEED_A BREED_B
FEED_A
rep. 9 6 3
200
d.f. 12 12 12
l.s.d. 2.181 2.671 3.778
![VO Sandpit, November 2009 Data Citation in the Earth and Physical Sciences Sarah Callaghan [sarah.callaghan@stfc.ac.uk] with thanks and acknowledgement](https://img.pdfslide.us/doc/110x75/56649e7c5503460f94b7ebad/vo-sandpit-november-2009-data-citation-in-the-earth-and-physical-sciences.jpg)
