TABLE OF CONTENTS
Title
Page ……………………………………………….………………………………,,…… i
Certification……………………………….…………………….……………..……………….
ii
Dedication
…………………………………………………………………….………….….. iii
Acknowledgement……..…………………………………………………..……..……….
….v
Table
of Content…..…………………………………………………………… ……..……vii
List
of Tables …………………………………………………………………………………..viii
List
of Figures………………………………………………………………………………..ix
Abstract
……………………………………………………………………………………………………………….x-xiii
CHAPTER ONE: INTRODUCTION
1.1:
Introduction
…………….….……………………………………………………. …1-9
1.2: Objective of
Study………………………………………………………………… …..4
1.3 Justification
…………………………….…………………………………..……….4-6
CHAPTER TWO: LITERATURE REVIEW
2.1
World’s Population of Cattle
……………………………………………….……10-12
2.2 Origin and History of the Breeds …………………………………………….…11-12
2.3 Breed Description ……………………………………………..13
2.4 Environmental (non-genetic) factors affecting growth traits. …15-17
2.5 Inbreeding and Its Effect on Growth Traits ……………17-21
2.6 Co (Variance) Components and Genetic
Parameters for Growth Traits …………21-24
2.6.1 Variances components for growth traits………………….21-23
2.6.2 Covariance components between direct and
maternal effect for growth traits……23-24
2.6.3 Correlations between direct and maternal (ram)
effects on growth traits…………24-26
2.7 Heritability Estimates for Growth Traits……………………………..26-35
2.8 Genetic correlations between growth traits…………..…..35-39
CHAPTER
THREE: MATERIALS AND METHOD
3.1 The Study Area …………………………………………………………………….40-41
3.2 Foundation Animals …………………………………………………………………..41
3.3 Breeding Programme (Selection and mating) …………….43
3.4 Management of the Herd …………………………………..43
3.4.1 Calf
Management …………………………………………………………………..43-44
3.4.2 Health
Management ……………………………………………………………………44
3.4.3 Feeding
Management ………………………………………………………………….44
3.5 Source of Data, Data Collection and Editing. …………………44-45
3.5.1 Source
of Data ……………………………………………………………………….44-45
3.5.2
Data Collection. …………………………………………………………………………45
3.5.3
Data editing ………………………………………………………………………….45-46
3.6 Statistical
Analysis …………………………………………………………………46-47
- Genetic Analysis ……………………………………………………………………47-50
CHAPTER
FOUR: RESULTS AND DISCUSSION
4.1 Basic statistics …………………………………………………………………………54
4.2 Factors which affect growth performance of
Gudali and Wakwa calves ………..55-66
4. 3 Results of inbreeding …………………………………………………………….64-66
4.4 Variance- covariance components………………..67-70
4.5 Heritability estimates in the Gudali and Wakwa cattle …..72-82
4.7 Genetic correlations between growth traits. …………………83-90
CHAPTER FIVE: CONCLUSION AND RECOMMENDATION……..91-94
5.1
Conclusion
……………………………………………………………………………………………………
5.2 Recommendations…………………………………………………………………………………….
References……………………………………………………………………………………..95-121
Appendices……………………………………………………………………………..122-178
LIST OF TABLES
Table 1: Cattle Population in Some Countries ………………………11
Table 2: Some heritability estimates for growth traits
in beef cattle ……………………27-31
Table
3: A summary of literature estimates of
across trait correlations from bivariate and multiple analysis
……………………………………………………………………………..37
Table 4: Descriptive statistics for growth traits in
Gudali cattle and Wakwa beef cattle……53
Table 5: Summary of Least Square Means for fixed
effects (non-genetic) factors affecting
growth performance of Gudali and Wakwa beef cattle ………………………………………58
Table 6: Coefficient of Inbreeding of Performance
Traits of Gudali and Wakwa …………66
Table 7: Number and percentage of inbred animals in the
Gudali and Wakwa breed cattle..66
Table 8:
Estimates of (co) variance for growth traits in Gudali and Wakwa cattle
………..67
Table
9: Estimation of heritabilities for
growth traits in the Gudali and Wakwa cattle……..71
Table 10: Direct additive Genetic correlation (ra), maternal genetic correlation (rm) between growth traits in Gudali. ……………………..83
Table 11: Direct additive Genetic correlation (ra)
and maternal genetic correlation (rm) between growth traits in
Wakwa cattle. ……………………………………………………..83
LIST OF FIGURES
Figure 1: Ngaoundere Gudali ………………………..7
Figure 2: Brahman cattle ……………………………….8
Figure 3: Wakwa cattle ……………………………………………………………………………9
Figure 4: Distribution Map of Gudali Beef Cattle in Africa ……14
Figure 5: Map showing the Adamawa region of Cameroon. …………..42
Figure 6: Body weights of Gudali and Wakwa at
different growth traits ……………………54
Figure 7: Additive genetic, maternal, residual and phenotypic variance of growth trait in the Gudali and Wakwa cattle ………………….68
Figure 8: Direct and maternal heritability of the
Gudali and Wakwa cattle ………………..73
Figure 9: Additive genetic and maternal correlations between growth traits in the Gudali and Wakwa cattle ………………………………….86
Figure 10: Genetic and maternal correlations between growth traits in the Gudali cattle…………………………………………….87
Figure 11: Genetic and maternal correlations between growth traits in the Wakwa cattle………………………………………………….87
ABSTRACT
The present study was
carried out to evaluate genetically the growth performance of the Gudali and
Wakwa beef cattle. Data
utilized for this study was obtained from the Institute of Agricultural
Research for Development (IARD), Wakwa Station, Cameroon. The data used consisted of pedigree information of
3788 animals and 2276 performance records for the Gudali and Wakwa cattle
respectively, ranging from birth to 36-months weight collected from 1968 and
1988. The data were collected
from compiled herd books (calf record sheet, bull progeny record sheet and cow
record sheet) consisting of pedigree information and performance records from
birth to 36-months weight for both the Gudali and Wakwa breeds. The raw
data were edited such that the utilized records gave complete information on
calf identity, sire identity, dam identity, sex of animal, dates of birth,
season of birth, herd and weights at birth, 3- month weight (3MWT), 4- month
weight (4MWT), 6-month weight
(6MWT), weaning weight (WWT), 12-month
weight (12MWT), yearling weight
(YWT), 18-month weight (18MWT), 24-month weight (24MWT), 30-month weight (30MWT) and 36-month weight (36MWT). In order to determine the fixed effects that were included in the
model, a preliminary analysis was performed using the general linear models
procedure as implemented in the statistical package, Statistical Analysis
System 8.2. Inbreeding coefficient
was calculated using the Multiple Trait Derivative Free Numerator
Relationship Matrix (MTDFNRM) programme of the Multiple Trait Derivative Free Restricted Maximum Likelihood (MTDFREML) package. Genetic parameters of
the growth traits were analyzed using MDTFREML package. From these, the additive
genetic variance (σ2a),
maternal variance (σ2m),
error variances (σ2e),
phenotypic variance (σ2p),
covariance between additive genetic and maternal variance (σam), correlation between
additive genetic and maternal variance (ram),
and heritabilities were derived at convergence.
Genetic correlation (rG) between growth traits was also calculated.
Preliminary analyses showed that all fixed effects of calf month and
year of birth, season, sex, herd and herd-year-season had a highly significant
(p < 0.0001) effects on all the growth traits studied while year of birth of
sire was significant (p < 0.05) for all the traits studied except for 30-
and 36-MWT. In the Gudali breed, cow age group was not significant (p >
0.05) for all traits except BWT, 3MWT, 4MWT, and 24MWT, which had highly
significant (p < 0.01) effects. Also, in the Wakwa breed, cow age group was
not significant (p > 0.05) for all traits except BWT, 3MWT, 4MWT, and WWT.
The average inbreeding coefficient obtained in this study ranged from 0 to 8%. Maternal variances for all traits studied
were consistently lower than additive genetic variance in both breeds of
cattle. The covariance between direct and maternal components was antagonistic
in all traits studied.
The direct heritability (h2a)
estimates for BWT, 3MWT, 4MWT 6MWT, WWT, YWT, 18MWT, 24MWT, 30MWT, and 36MWT
were 0.39, 0.24, 0.22, 0.10, 0.25, 0.21, 0.18, 0.25, 0.18 and 0.18 respectively
for the Gudali cattle. On the other hand, the direct heritability (h2a)
estimates of BWT, 3MWT, 4MWT 6MWT, WWT, YWT, 18MWT, 24MWT, 30MWT, and 36MWT
were 0.41, 0.22, 0.17, 0.25, 0.21, 0.16, 0.15, 0.22, 0.34 and 0.33 respectively
were obtained for the Wakwa cattle. The direct heritability estimate of birth
weight in Wakwa was high (0.41). Moderate additive genetic heritability (h2a)
estimates were obtained for BWT (0.39), 3MWT (0.24), 4MWT (0.22), WWT (0.24),
YWT (0.21), 24MWT (0.25) in the Gudali cattle. Medium h2a
were obtained for 3MWT (0.22), 6MWT (0.25), WWT (0.21), 24MWT (0.22), 30MWT
(0.34), and 36MWT (0.33) in the Wakwa cattle. The lowly heritable traits
included 6MWT (0.10), 18MWT (0.18), 30 MWT (0.18) and 36MWT (0.18) for the
Gudali cattle, while for the Wakwa, they included 4MWT (0.17), YWT (0.16) and
18MWT (0.15). The maternal
heritability (h2m) estimates were BWT (0.05), 3MWT (0.13), 4MWT (0.15), 6MWT (0.07) WWT (0.11), YWT (0.10)
18MWT (0.05), 24MWT (0.09), 30MWT (0.03), 36MWT (0.07) for Gudali cattle. Also,
the maternal heritability for the Wakwa cattle include: BWT (0.16), 3MWT
(0.16), 4MWT (0.14), 6MWT (0.18) WWT (0.18), YWT (0.13), 18MWT (0.14), 24MWT
(0.03), 30MWT (0.05) and 36MWT (0.10). The maternal heritability for
performance traits in both breeds falls between lowly heritable and medium
heritable traits. The moderate to
high values of heritabilities indicated that selection for growth traits was
effective in spite of the antagonism association between direct and maternal
effects. The additive direct genetic correlations between some of the growth
parameters were positive and high (0.50 – 0.99). The same pattern was observed
for maternal genetic correlations among traits (0.53 – 0.99), though some had
negative genetic correlations (BWT and EMWT (-0.80); BWT and 36MWT (-0.79).
Direct genetic correlations between BWT and WWT; BWT and YWT; BWT and 18MWT;
BWT and 36MWT; WWT and YWT; WWT and 18MWT; WWT and 36MWT; YWT and 18MWT; YWT
and 36MWT and 18MWT and 36MWT were 0.53, 0.39, -0.66, -0.21, 0.88, 0.87,
0.70, 0.70, 0.60 and 0.50 for the Gudali cattle. The direct genetic
correlations between the same traits in the Wakwa cattle were 0.79, 0.52, -0.50, -0.31, 0.95, 0.79,
0.69, 0.93, 0.60, and 0.49 respectively. The
maternal genetic correlations between the same traits for Gudali cattle were 0.72,
0.39, -0.81, -0.89, 1.00, 0.99, 0.97, 0.60, 0.70; and 0.50; 0.62, 0.32, -0.80, -0.79,
0.75, 0.99, 0.99, 0.50, 0.60 and 0.53 for Wakwa cattle. The positive and high
values reported for the additive genetic and maternal correlations between the
growth parameters indicate that selection for one trait would result in genetic
improvement in the other trait. On the whole, the level of performance of the
two breeds of cattle comes close to that reported in literature for beef
cattle. The estimates of genetic parameters as well as information obtained on
effects of the various factors should be of use in designing breeding
programmes for the herds studied.
CHAPTER ONE: GENERAL INTRODUCTION.
- Introduction.
Agriculture is one of the
most important sectors in the economy of many developing countries where
it provides survival mechanism for up to 80% of the population (Cupps, 2007).
It plays a central role
in the rural economy of the developing nations (Omage et al., 2007). The
food crisis that has engulfed Africa and the developing countries requires a
more concerted effort. Major food sources in the developing countries are
almost entirely starchy foods such as tubers, roots, and cereal crops. These
obviously do not and cannot satisfy the protein needs of the populace. Protein
intake and particularly animal protein consumption is generally grossly below
the recommended rate (Omage et al., 2007). The British Medical
Association recommended a minimum daily intake of 34.4g of animal protein per adult
per day. Unfortunately most developing countries, consumption is at 7.5g of
animal protein as against 28g consumed by an average Briton (Wines, 2009).
Over 800 million people worldwide
suffer from malnutrition and hunger either because of low food production and
unequal distribution and also because the people are too poor and therefore
lack the income to acquire adequate quantities and qualities of food (Bayemi et
al., 2005; Palitza, 2009). This is true of the people of Africa who consume
foods that consist mainly of starch and oil (Redmond, 2009). Cattle production
offers an avenue for rapid transformation in animal protein, because beef enjoys
wide acceptability in the world (Zahraddeen et al., 2007). Cattle also
contribute to subsistence, nutrition, income generation, social and cultural
functions. However, their main products remain meat, milk, hides, manure and
traction. Beef and milk consumption
have grown more than 5% per year and are projected to grow even faster until
2020 (Cupps, 2007).
The
expanding demand for cattle products is the result of a combination of high
income growth, population growth, urbanisation and the diversification of the
diets in developing countries away from very high levels of starchy staples to
protein (Nwosu,2002). It is for these reasons that most African
countries have embarked on breed evaluation which could lead to an increase in
livestock production. An important component of successful planning of future
breeding schemes is from documentation of progress from past selection.
However, few of such documentations have been conducted for cattle breeds,
especially in Africa, largely because of their long generation interval (Abdullah
and Olutogun, 2006).
Cattle constitute an important part of the
livestock sector in Cameroon. The country is also endowed with the resources
for the production of animal feed all the year round, especially as forage,
crops residuals and weeds are readily available. Cattle are important in
Cameroon in several ways depending on the ethnic group and the culture of the
people. They serve as an important source of income, animal proteins; skins are
used in industry to produce wears, bags and other household furniture (Redmond,
2009). Therefore increasing cattle production would not only improve the diet
of Cameroonians but could create surpluses for export. The new scenario of the
Cameroonian beef industry, inserted in the new order of a global world economy,
induces the cattle producers to search for more productive breeds. They
generally resort to uncontrolled crossbreeding as a means to rapidly improve on
the live-weight. Though crossbreeding has been widely proposed for improvement
of cattle breeds in the tropics, the consequences could be disastrous if not
properly handled (Ferraz et al., 2006). This has been the case with the
Gudali cattle of Adamawa, Cameroon which has along the years suffered from
uncontrolled crossbreeding with the white and red Zebu breeds.
In
Adamawa region, Cameroon, the local Gudali is the predorminant breed and it constitutes about 19% of
total cattle production in Cameroon (Ngaoundere Gudali 15% and Banyo Gudali 4%)
and remains the most popular, especially in smallholder sector of the Adamawa (Tawah et al., 1993). The Gudali
(Figure 1) is a
short-horned Zebu cattle found within the West and Central African region. It
is of good temperament; excellent beef production potential; and can produce
and reproduce optimally under the prevailing conditions of the tropical
environment without much additional inputs (Ebangi, 1999). They are docile, and
have great temperaments; in addition, they are quite hardy. It is medium to
large sized and slow maturing compared with many other cattle breeds (Tawah and
Mbah 1989).
Attempts were therefore made at Institute of Agricultural Research for
Development (IARD) of Cameroon to crossbreed the Brahman with the local
Gudali to improve on the growth traits of the local Gudali. The Brahman bulls
(Figure 2) were crossed with the local Gudali cows to produce the first filial
generation called “Prewakwa”. It was inter
se mated to produce a two-breed synthetic beef breed, the Wakwa (Figure 3).
Wakwa is characterized by a variety of coat colours. At maturity, males and
females weigh about 512 and 426 kg, characterized by a variety of coat colours.
It has a broad but slightly convex face, long but drooping ears, short but
broad-based horns an oval hump and a straight but broad back (Ebangi, 1999).
Genetic
improvement of any breed within a given environment will depend on identifying
the major environmental constraints to performance, devising means of
alleviating or controlling them and then evaluating the breed for its
adaptability to cope with constraints that can not be readily controlled.
Knowledge of non-genetic influences on the performance of farm animals is therefore
very important when planning breeding programmes aimed at improving
productivity and in the development of other breeding policies.
Improvement
of live-weight traits is an increasingly important breeding goal in beef cattle
and other livestock production systems (Peters et al., 1998). The change
in the mean of a trait during a few initial cycles of a directional selection
imposed on a population is among the most reliable criteria for estimating the
exploitable amount of genetic variation in a given genetic population.
Therefore, knowledge of the genetic parameters, magnitude and direction of
genetic correlations of certain major metric traits of economic importance in a
selection program are needed. This will be necessary in the optimization and
prediction of genetic progress from a selection program.
Estimation of variance components is
always an important tool in developing animal breeding programs. Estimates of
variance components must be accurate since error variance for predicted
breeding values increases as differences between estimated and true value of
variance components increase (El-said et al., 2005). Heritabilities
and genetic correlations estimates are essential parameters required in
livestock breeding research as well as in the design and application of
practical animal breeding programmes. In applying genetic concepts to animal
breeding, heritability is a fundamental population parameter since it largely
determines the prospect for changing a population by selection. Therefore,
correct knowledge of heritability will help to predict breeding value of the
animals hence their proper selection for further improvement programme.
Many
economically important traits such as growth traits have some form of
relationship where a change in the value of one is accompanied by a change in
the value of the other. This is the concept of genetic correlation. Initial growths
of calf, especially in suckling period, are affected not only by direct
additive genetic but also by maternal additive genetic and maternal permanent
environmental effects. Therefore, particularly, if there is a negative
correlation between direct and maternal genetic effects, both effects should be
taken into account in selection processes to achieve optimum genetic progress
(Dezfuli and Mashayekhi, 2009).
In
conclusion, study of environmental factors, estimates of heritability,
correlation between growth traits and the level of inbreeding, provide vital
information for beef cattle breeding programmes. Such information will be
useful for genetic improvement and attainment of higher levels of performance.
Over the years the Institute of Agricultural Research for Development
(IARD), Wakwa Station, Cameroon accumulated records
on the growth performance of Gudali and Wakwa beef cattle, some of which have not
been comprehensively exploited. The present study was to evaluate genetically the growth
traits using data collected from 1968 and 1988 from the Institute
of Agricultural Research for Development (IARD), Wakwa Station.1.2 Objectives of the Study.
GENETIC STUDY OF GUDALI AND WAKWA BEEF CATTLE BREEDS OF ADAMAWA REGION, CAMEROON
