GENETIC STUDY OF GUDALI AND WAKWA BEEF CATTLE BREEDS
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GENETIC STUDY OF GUDALI AND WAKWA BEEF CATTLE BREEDS
ABSTRACT
The goal of the current study was to assess the genetic performance of the
Wakwa and Gudali beef cattle. Information used in this study came from the Institute of
Wakwa Station, Cameroon’s Agricultural Research for Development (IARD).
Information utilised
included 2276 performance records for the animals and the pedigree data for 3788 animals.
Cattle from Gudali and Wakwa, weighing in from birth to 36 months, respectively
between 1968 and 1988.
The information was gathered from herd books (calf record sheet,
a cow progeny record sheet and a bull progeny record sheet) containing pedigree data and
Performance data for the Gudali and Wakwa breeds from birth to 36 months of age.
The raw data were adjusted so that the entries used included entire calf information.
the animal’s name, sire’s name, dam’s name, gender, date and season of birth, herd, and
birth weights, 3-month weights, 4-month weights, and 6-month weights
Yearling weight (YWT), 6-month weight (6MWT), weaning weight (WWT), 12-month weight (12MWT), and 18-
weights for the following months: 18 MWT, 24 MWT, 30 MWT, and 36 MWT.
weight per month (36MWT). The fixed effects that were incorporated into the model’s
generic linear models process as a preliminary analytical method for the
integrated in statistics Analysis System 8.2 of the statistics software.
Breeding percentage
used the Multiple Trait Derivative Free Numerator Relationship Matrix to calculate
The Multiple Trait Derivative Free Restricted Maximum (MTDFNRM) programme
Package for likelihood (MTDFREML).
Analysis was done on the genetic components of the growth attributes.
employing the MDTFREML package. From these, the genetic variance that is added (2
a), mother
Variation (2
m), variations in error (2
e), phenotypic variability (2)
Covariance among additive
Variance resulting from additive genetic and maternal factors (am), genetic and maternal
At convergence, heritabilities and variance (ram) were derived.
Genetic resemblance (rG)
computed between growth characteristics. According to preliminary assessments, all fixed effects
of calf birth month and year, season, sex, herd, and herd-year-season had an extremely high correlation
While the sire’s year of birth had significant (p 0.0001) influence on all the development parameters examined,
significant (p 0.05) for every feature examined, with the exception of the 30- and 36-MWT. Within the Gudali
All features except BWT, 3MWT, and cow age group were not significant (p > 0.05) for breed.
4MWT and 24MWT both produced effects that were highly significant (p 0.01). In the Wakwa, as well
All features except BWT, 3MWT, and cow age group were not significant (p > 0.05) for breed.
WWT and 4MWT. The study’s average inbreeding coefficient ranged from 0 to 1.
to 8%. For every variable examined, maternal variations were consistently lower than additive genetic
variations between the two cattle breeds.
the relationship between maternal and direct components
was adversarial in all attributes examined.
The h2 test for direct heritability
estimations for the BWT, 3MWT, 4MWT, 6MWT, WWT, and YWT
There were 0.39, 0.24, 0.22, 0.10, 0.25, 0.21, 0.18, and 0.25 at 18MWT, 24MWT, 30MWT, and 36MWT, respectively.
For the Gudali cattle, the corresponding values are 0.18 and 0.18. However, the direct heritability (h2)
a)
BWT, 3MWT, 4MWT, 6MWT, WWT, YWT, 18MWT, 24MWT, 30MWT, and
36MWT were successively 0.41, 0.22, 0.17, 0.25, 0.21, 0.16, 0.15, 0.22, 0.34, and 0.33
acquired for the livestock of the Wakwa. Wakwa’s birth weight’s direct heritability estimate was
high (0.41).
Modest genetic additive heritability (h2)
a) BWT estimates were obtained.
YWT (0.21), 24MWT (0.25), 3MWT (0.39), 4MWT (0.24), WWT (0.24), and
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Gudali animals. H2 medium
3MWT (0.22), 6MWT (0.25), WWT (0.21), and a were achieved.
In the Wakwa cattle, 24MWT (0.22), 30MWT (0.34), and 36MWT (0.33). The demeaning heritable
6MWT (0.10), 18MWT (0.18), 30 MWT (0.18),
and 36 MWT (0.18) features were present for the
Wakwa cattle included 4MWT (0.17), YWT (0.16), and 18MWT. Gudali cattle were also present.
(0.15). Inheritability from the mother (h2)
BWT (0.05), 3MWT (0.13), and 4MWT estimates were made.
(18MWT), 24MWT (0.09), 30MWT (0.09), 6MWT (0.07), WWT (0.11), and YWT (0.10)
(0.03), and 36MWT (0.07) for cattle from Gudali. Moreover, Wakwa cattle maternal heritability
the following: BWT (0.16), 3MWT (0.16), 4MWT (0.14), 6MWT (0.18), WWT (0.18), and YWT
(17MWT, 0.13), 18MWT, 24MWT, 30MWT, 0.05), and 36MWT, 0.10). the mother
Heritability for performance attributes varies between lowly heritable and medium in both breeds.
inherited qualities. Heritabilities ranged from moderate to high values, indicating that selection for
Despite the antagonistic connection between direct and maternal growth features, they were successful.
effects. Some of the growth metrics’ additive direct genetic relationships were
high and favourable (0.50 to 0.99). For maternal genetics, the same pattern was seen.
Although several variables had negative genetic relationships (0.53 to 0.99), BWT
BWT and 36MWT (-0.79); and EMWT (-0.80).
BWT and specific genetic relationships
BWT and YWT; and WWT; BWT and 18MWT; BWT and 36MWT;
YWT and 18MWT, YWT and 36MWT, and WWT and 18MWT all work together.
0.53, 0.39, -0.66, -0.21, 0.88, 0.87, 0.70, 0.70, 0.60, and 0.50 for 18MWT and 36MWT respectively.
the Gudali livestock. The Wakwa cattle exhibit direct genetic relationships between similar features.
Those values were, successively, 0.79, 0.52, -0.50, -0.31, 0.95, 0.79, 0.69, 0.93, 0.60, and 0.49. the mother
For Gudali cattle, the genetic correlations between the same attributes 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; 0.50; and 0.60
0.53 for cattle from Wakwa.
The high and favourable values for the additive genetic and
Strong correlations between the growth metrics show that one feature is being selected for.
would lead to an improvement in the other trait’s genetic makeup.
The overall performance standard
comes near to that described in literature for beef cattle of the two breeds of cattle. The
estimates of genetic characteristics and data on the consequences of the different
Factors should be taken into consideration while creating breeding plans for the herds under study.
CHAPITER 1
Introduction in general.
Introduction 1.1.
One of the most significant economic sectors in many developing countries is agriculture.
Up to 80% of the population can rely on it as a survival mechanism in some nations (Cupps,
2007).
It is crucial to the rural economies of developing countries (Omage et al.,
2007). The food crisis that has gripped Africa and developing nations calls for a more comprehensive approach.
unified effort.
Nearly all of the major dietary sources in emerging nations are starchy.
foods like cereal crops, tubers, and roots. Evidently, these cannot and do not meet the
population’s needs for protein.
Consuming enough protein, especially animal protein, is
consistently far below the advised rate (Omage et al., 2007). American Medical
Association advised adults to consume 34.4g of animal protein everyday as a minimum.
day.
Unfortunately, the average animal protein consumption in poor nations is only 7.5g.
compared to the 28g that the typical Briton consumes (Wines, 2009).
Over 800 million people globally experience hunger or malnutrition.
due to inadequate food supply, poor distribution, and also because the population is too large
Being poor prevents one from having enough money to buy a sufficient amount and quality of food.
(Palitza, 2009; Bayemi et al., 2005).
It is accurate to say that Africans who eat food experience this.
they are primarily made up of oil and starch (Redmond, 2009)
. Cattle production provides a way to
animal protein is changing quickly because beef is widely accepted around the world.
(2007) Zahraddeen et al.
Additionally, cattle provide food, nourishment, and revenue.
cultural and societal roles. But they continue to produce primarily meat, milk, hides, and dung.
both traction.
Consumption of beef and milk is increasing at a rate of more than 5% annually.
till 2020, when it is anticipated to develop even quicker (Cupps).
Cattle goods are in greater demand as a result of a mix of factors, including high
Increased affluence, population growth, urbanisation, and dietary diversity in
Protein is replacing very high levels of starchy staples in emerging nations (Nwosu, 2002).
Due to these factors, the majority of African nations have started breed evaluation programmes, which
potentially result in a rise in livestock output. a crucial element of effective
Planning for upcoming breeding plans is based on documentation of past selection’s progress.
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But few of these studies have been done for cow breeds, particularly in
mainly due to their lengthy generational gap (Abdullah and Olutogun, 2006).
In Cameroon, cattle are a significant component of the livestock industry. The nation is
Moreover supplied with the means to produce animal feed year-round,
especially given the accessibility of weeds, feed, and crop residue. The importance of cattle in
Depending on the people’s ethnicity and culture, Cameroon can be viewed in a variety of ways. They
skins are used in industry to generate animal proteins,
which are a significant source of wealth.clothing, luggage, and other furniture for the home (Redmond, 2009). increasing the number of cattle
Production would not only enhance Cameroonians’ diets but may also generate surpluses for
export. The new arrangement of a cattle sector in Cameroon and its new situation
The global economy encourages cow farmers to look for more productive varieties.
They typically turn to unrestricted crossbreeding as a way to quickly enhance the
live-weight.
Despite the fact that crossbreeding has been strongly advocated for improving cattle
if not adequately managed, breeds in the tropics, the results could be severe (Ferraz et
al., 2006).
The Gudali cattle of Adamawa, Cameroon, have experienced this in the past.
suffered from unchecked crossbreeding with the red and white Zebu breeds over the years.
The native Gudali breed is the dominant one in the Adamawa area of Cameroon, and it
approximately 19% of Cameroon’s total cattle production (Ngaoundere Gudali 15% and
Banyo Gudali 4%) and is still the most well-liked, particularly in the small-scale farmer sector of the
Adamawa, according to Tawah et al.
A short-horned Zebu cattle known as the Gudali can be found
within the continent of West and Central Africa.
It has a good disposition and makes great beef.
production capacity; and are capable of reproducing and producing at their best under the current conditions
without a lot of additional input, tropical environmental conditions (Ebangi, 1999). They
are docile,
have wonderful temperaments, and are also fairly hardy. It is between
compared to many other cattle breeds, huge in size and slowly developing (Tawah and Mbah
1989).
As a result, the Institute of Agricultural Research for Development made an effort.
(IARD) of Cameroon to hybridise the Brahman with the native Gudali in order to enhance the
characteristics of native Gudali growth.
The local bulls were crossed with the Brahman bulls (Figure 2).
Using Gudali cows, the first filial generation known as “Prewakwa” is produced. It was intertwined with
create the Wakwa, a synthetic two-breed cattle breed (Figure 3).
Wakwa is distinguished by
a range of coat hues. Males and females weigh roughly 512 and 426 kg when they are mature.
is distinguished by its many different coat hues. It features a long but broad face that is slightly convex.
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drooping ears, short horns with a broad base, an oval hump, and a back that is straight but broad.
1999 (Ebangi).
Any breed’s ability to genetically advance in a specific environment will depend on
determining the main environmental performance restrictions, developing methods of
reducing or containing them, and then assessing the breed’s capacity to adapt to them
restrictions that are difficult to control.
understanding the role of non-genetic factors in the
Therefore, farm animal performance is crucial when considering breeding.
projects designed to increase production and foster the creation of alternative breeding
policies.
Improvement of live-weight attributes is a breeding goal for beef that is becoming more and more essential.
Production systems for cattle and other livestock (Peters et al., 1998).
The mean’s shift from
During the first few cycles of a population-based directional selection, one of the traits is among
the most trustworthy standards for determining how much genetic variation is exploitable in a particular population
genetic diversity.
Consequently, understanding the genetic factors, their magnitude, and direction
of genetic relationships between key metrics that are economically significant in a selection.
A programme is required. This will be required for the genetic prediction and optimisation.
advancement following a selection programme.
In constructing animal models, estimation of variance components is always a crucial technique.
breeding schemes. Since error variance, estimates of variance components must be precise.
As disparities between expected and true levels of growth for anticipated breeding values,
El-Said et al. (2005) found an increase in the variance components.
Genetic connections and heritabilities
Estimates are crucial inputs needed for livestock breeding research as well as in the
designing and implementing useful animal breeding programmes.
When employing genetic
When it comes to animal breeding, heritability is a crucial population factor because it
dictates, in large part, the likelihood of modifying a population through selection.
hence, you’re right
Understanding heredity will aid in determining the animals’ proper breeding value.
selected for a course of future improvement.
A lot of economically significant characteristics, such growth characteristics,
connection in which the value of one is changed along with the value of the other
one more.
The idea of genetic correlation is this. initial calf growth, particularly in
Suckling period is impacted by maternal additive genetics as well as direct additive genetics.
environmental impacts that are genetic and maternal in nature.
Consequently, in particular if there is a
Negative correlation between maternal genetic impacts and direct effects; both effects should be considered
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best genetic advancement by taking selection procedures into consideration (Dezfuli and
2009 (Mashahekhi).
In conclusion, research on environmental variables, heritability estimations, and correlation
Give essential information on the relationship between growth features and the degree of inbreeding for beef cattle
breeding strategies. Such details will be beneficial for genetic advancement and
attainment of improved performance levels.
The Institute of Agricultural Research for Development (IARD) has conducted research on
Wakwa Station, Cameroon gathered data on Gudali and’s growing performance,
Wakwa beef cattle, of which some have not been fully utilised. the current
Employing data gathered between 1968 and 1988,
the study evaluated growth traits genetically.
from the Wakwa Station location of the Institute of Agricultural Research for Development (IARD).
1.2 Objectives Of The Study
The goals of this investigation were to:
1. Examine the environmental (non-genetic) elements that influence growth features in the Gudali and
Wakwa livestock.
2. Find out how much the Gudali and Wakwa beef cattle population is inbred.
3. Calculate the (Co) variance components of the Gudali and Wakwa beef cattle’s growth attributes.
4. Calculate the breeds’ respective growth trait heritabilities.
5. Determine the genetic relationships between maternal (ram) and direct influences on growth. qualities of performance.
6. Calculate the genetic correlations between the two breeds of cattle’s growth qualities.
1.3 Justification Of The Study
The phenotypic performance of an animal is the outcome of its actual genetic potential in addition to its
ability to specifically handle environmental stress.
It is crucial to comprehend the
influence of genotype and environment on a population’s performance attributes. info on
The criterion for assessing the breed’s merits will be growth performance.
estimations for
Not only is it important to consider environmental and adaptability factors when planning future breeding
techniques but would allow the Gudali selection plan to be carried out more precisely.
Wakwa herds are bred. Despite the crucial part the environment plays in beef cattle,
Research on environmental issues are scarce in the literature due to the production in Cameroon. Tawah and co.
(1993) investigated the preweaning growth performance parameters for Gudali and Wakwa.
utilising the Least Square method, livestock. Ebangi et al. (2002a) looked at factors that could
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Using a mixed model method, we may examine growth features before and after weaning. Although these
Only a part of the data from both breeds was used by the writers. These elements need to be researched.
the remaining selection data for the two breeds’ growth traits.
All populations exhibit some degree of inbreeding (Pico et al., 2004). This
effects in beef cattle, for example, are widely documented in all major livestock
Burrow (1993) has reviewed them.
Various studies indicate that the amount of inbreeding
may differ between populations. While inbreeding can jeopardise the immediate
In addition to affecting population health and survival,
it also makes harmful genes more active.
of choice. Considering the aforementioned, any genetic assessment should take into account the rate of
Inbreeding and its impact on animals’ average phenotypic performance (Analla
et al., 1999).
If there is no variation, a trait cannot be improved (Nwosu, 2002).
Genetic, environmental, and phenotypic variations are crucial components in selection and in
the advancement of livestock.
while keeping the herd’s reproductive effectiveness
In order to meet production, improving growth potential should be of special concern.
from the manufacturing process.
Genetic characteristics that describe enduring diseases are interesting.
features of growth that evolve over time.
Having sufficient information and using genetic
Variations are crucial for improving the economic traits of beef cattle through
breeding. The most accurate estimation of these metrics for economic significance features is
needed:
When creating effective breeding strategies,
predicting the genetic gains anticipated under mass selection in order to spot issues and
foreseen limitations for necessary measures,
When determining breeding value,
calculating the speed and direction of the selecting process, and
(Nwakalor, 1975) in the creation of selection indices.
Many economically significant beef characteristics, such as growth characteristics, are related in some way.
if the value of one changes along with the value of the other. This
relates to the idea of genetic connection.
According to Pico (2005), it would be helpful to understand the
empirical connections between a growth trait’s additive genetic and maternal correlations
population.
Understanding the genetic relationships between attributes will aid in the prediction of
Correlated responses and in determining the animal’s breeding value for such features
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considered. Additionally, breeders are most interested in genetic relationships because they can
describe the expected changes that will occur in the upcoming generation.
Consequently, correlation
Estimates between attributes show the strength and direction of the relationship between the
characteristics that aid in creating programmes for cattle improvement.
Previous attempts to study beef cattle’s genetics have mainly been limited to small, single
(Abdullah and Olutogun, 2006) Herd populations.
However, more focus has been placed recently on
has been used to examine cattle herd performance and progeny in America and Australia.
(Meyer, 2005).
While genetic research in the industrialised world has received a lot of media attention
However, it is regrettable that there is little information on genetic research in our
Tropics-specific breeds of cattle.
Indeed, following decades of exploitation, regional breeds are now
As a source of valuable diversity, according to Nwosu (2002). Therefore, there is a requirement for the
genetic analysis of our native population.
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Ngaoundere Gudali cow in Figure 1.
Agricultural Research for Development Institute
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Brahman cattle, Figure 2.
American Brahman Breeders Association, Houston, Texas, as a source
Australian Meat and Livestock Corporation,
Handbook of Australian Livestock, 2000.
Board of Regents of Oklahoma State University, Third Edition
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Figure 3: Cattle from Wakwa
Agricultural Research for Development Institute
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