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GENETIC STUDY OF GUDALI AND WAKWA BEEF CATTLE BREEDS OF ADAMAWA REGION, CAMEROON

Animal Science Project Topics & Materials

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

  1. 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.

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