A seminar presented for MSc course fulfillment

1. Bahir Dar University
College of Agriculture and Environmental
Sciences
Senior seminar on:
Estimation of genetic parameter for reproduction
traits of Dairy cattle in Ethiopia: A Review
By: Assemu Tesfa
May 15-2014

2. 1. Introduction
Ethiopia is endowed with diversity of animal, plant and
microbial genetic resources (IBC, 2004).
gate way and diverse climatic conditions (Mohamed
et al., 2004).
The dairy sector in the country had show considerable
increment from 1.8 to 3% (Mohamed et al., 2004).
The productivity of dairy cattle depends largely on their
reproductive performance.
Reproduction is an indicator of reproductive efficiency
and the rate of genetic progress in both selection and
crossbreeding programs (Nuraddis Ibrahim, 2011). 2

3. Introduction….
Among the reproductive traits,
AFS, NSPC, DO and CI are the bases for a profitable
dairy farming (Enyew et al., 1999).
Estimating genetic parameters - main topic of animal
breeding during the past half century (Sang, 2003).
Study of genetic composition of a population summarizes
the nature and productivity of that population.
The most commonly used parameters for estimating
genetic parameters are Heritability, Repeatability and
Genetic correlation (Yibrah, 2008).
3

4. Introduction….
The genetic parameter estimates are helpful
(Abegaz et al., 2002; Juma and Alkass, 2006).
In determining the method and predict responses
of selection,
Choosing a breeding system to be adopted for
future improvement
Estimation of genetic gains (Khalid et al., 2001).
4

5. This paper was initiated to address the following
objectives.
To review the reproductive potentials of dairy
cattle in Ethiopian
To review the genetic parameter estimated for
reproductive traits of dairy cattle in Ethiopia
To direct the importance of genetic parameter
estimates for animal breeding
Introduction…. objective
5

6. 2. Reproductive performances of dairy
cattle in Ethiopia
Reproductive efficiency is expressed by the extent
of reduction of reproductive wastage and
it affects lifetime milk and meat production
(Nuraddis Ibrahim, 2011).
The expression 'reproductive performance' does not
usually refer to a single trait, but to a combination of
many traits.
An important prerequisite for the sustainability of a
dairy production system is that cows must have
efficient reproductive performance (Azage and
Alemu, 1998).
6

7. Table 1. Summary of reproductive of Ethiopian indigenous dairy
cattle
7
Breed CI AFC DO
Lactation
NSC Source
Milk
Yield
Len
gth
Horro
12.2 50.0 152 559 285 1.69
Hailemariam and Mekonnen
(1996)
Arsi 14.6 32.8 211 809 272 2 Mulugeta Ayalew et al. (2008)
Boran 20.7 57.6 339 494 155 1.61 Yifat Denbarga et al. (2012)
Barka
13.2 30.3 253 674 279 1.11
Hailemariam and Mekonnen
(1996); Million and Tadelle
(2003)
Fogera
18.6 47.6 303 613 353 1.62
Prabhakar and Addisu (2004);
Goshu et al. (2005)
Highland
zebu
15.1 53 148 - - 2.2
Niraj Kumar et al. (2014)
Crossbree
d cattle
12.4 34.8 85.6 - - 1.52 Hunduma Dinka (2012)
Ogaden
16.4 50.3 195 - - 2 Getinet Mekuriaw et al. (2009)
Metema
highland 19.2 46.1
204.
1
- - 1.74 Tesfaye Mengsitie (2007
0
50
100
150
200
250
300
AFS
AFC
CI
NSC
DO

8. Table 2. Comparison between the local and exotic
blood
levels of cattle’s in Ethiopia for reproductive
traits
(in months)
Traits
CI
Mean+ SD
AFC
Mean + SD
AFS
Mean + SD
NSC
Mean + SD
DO
Mean + SD
Local
breeds
16.30+1.55 49.75+8.97 40.91+9.18 1.73+0.32 206.48+35.6
Cross
breeds
15.77+
2.25
37.53 + 4.42 28.54 +4.75 2.71+ 4.08
136.09+
34.9
8

9. 3. Methods applicable for genetic parameter
estimation
The choice of method is a matter of deciding which gives
the most precise estimate:
 with the given facility and the type of data available
(Kanakaraj, 2001).
Maximum likelihood (ML)
It is a parameter values for which the likelihood is
maximized that tells:
 how likely the data have been sampled from a
population with the selected parameter values.
9

10. Methods applicable…..
In estimating variance components by ML, data are
generally assumed to have a multivariate normal
distribution.
The drawback of ML estimation in a mixed model is that:
 fixed effects are treated as if they were known, i.e.
the loss in degrees of freedom due to fitting these
effects is ignored.
If the model of analysis comprises many fixed effects,
as is almost invariably the case for animal breeding data,
this can yield estimates considerably biased.
10

11. Restricted Maximum Likelihood (REML)
Fortunately, a modified ML procedure, Restricted
Maximum Likelihood (REML) as described by (Muhittin et
al., 2009) overcomes this problem
by maximizing only the part of the likelihood which is
independent of the fixed effects.
Conceptually, this is achieved by replacing the data by
linear functions thereof, ’error contrasts’, with an
expectation of zero.
For balanced data, REML estimates are equal to those
from an ANOVA (Karin Meyer, 2003).
SAS, GENSTAT (Mostert et al., 2010), SPSS, Stata, and
WOMBAT (Meyer Karin, 2007) provide options for REML
analyses.
Methods applicable…..
11

12. The linear univariate and bivariate animal model
analyses were run using a restricted maximum
likelihood method of MATVEC program (Wang et al.,
2001; John, 2005):
 allow to obtain heritability's and genetic correlations
of both productive and reproductive performance
traits (Navid, 2012).
In general cases the following multivariate linear mixed
model for t traits can be applicable (Meyer, 2004) .
y = μ+Xb+Zu+e
Methods applicable…..
12

13. Table 3. Average values for heritability and
repeatability for
dairy cattle
13
Traits Heritability (h2) Repeatability (r)
Milk yield 0.25 0.50
Calving interval 0.08 0.15
Conception rate 0.05 -
% fat 0.50 0.60

14. 4. Genetic parameter estimates for reproductive
traits of dairy cattle in Ethiopia
1. Heritability (h2)
Obviously heritability is important among the
several factors determining how much genetic
improvement can be made in any trait
(Aynalem, 2006).
h2 = 2G / 2P and h2 = 2A / 2P
(Kanakaraj, 2001; Khalid et al., 2001; Cilek and Sahin,
2009).
Heritability can be increased by providing uniform
environment, use of multiple measurements,
adjustment of records, and accurate measurement
of data. 14

15. Genetic parameter estimates…
Different estimates of heritability may be found for the
same trait in different populations or in one population at
different times (Dalton, 1981).
At birth weight, Kenana cattle had 0.19 (Saeed et al.,
1987), 0.31 of Kenana (Khalifa and Khalafallah, 1981),
0.13 of Zebu (Rico et al., 1985), 0.32 of Boran
(Mekonnen, 1987) and 0.13 of Boran (Arnason and
Kassa, 1987) heritability.
Estimates of maternal heritability’s for Kenyan Boran
ranged from 0.25 to 0.27 (Wasike et al., 2006).
15

16. 4.1.1 Heritability for CI
Calving interval is probably the best indicator of a cow's
reproductive efficiency (Mostert et al., 2010).
Calving interval has a very low heritability (Cassell,
2001).
Million Tadesse and Tadelle Dessie (2003), reported that,
heritability value of 0.03 for first calving interval in Holstein
dairy cattle;
Hailemariam (1994) reported a heritability value of 0.04 for
Boran in Ethiopia;
Gebeyehu et al. (2014) and Kefena Effa et al. (2011)
reported a respective heritability of 0.28 and 0.17 for
Holstein Friesian cattle and cross breed dairy cattle in
Ethiopia;
Genetic parameter estimates…
16

17. 4.1.2 Heritability for AFC
Heritability of age at first calving is generally low,
indicating that this trait is highly influenced by
environmental factors.
Cassle (2001) reported a heritability value of 0.14 for age at
first calving in Holstein cattle while
Hailemariam (1994) reported a heritability value of 0.07 in
Boran cattle in Ethiopia. Gebeyehu et al. (2014) reported a
value of 0.53 for Holstein Friesian cattle in Ethiopia.
4.1.3 Heritability for MY
Haile-Mariam et al. (2003) reported a respective heritability
of 0.32 and 0.09 for mean MY and persistency of MY for
dairy cattle in Ethiopia.
And 0.23 values for heritability were reported by Gebeyehu
et al. (2014) for Holstein Friesian cattle in Ethiopia.
Genetic parameter estimates…
17

18. 4.2 Repeatability
Repeatability measures the correlation between the
repeated measurements of the same individual.
It indicates the gain in accuracy that may be expected
from the use of the mean multiple measurements
instead of single measurement (Kanakaraj, 2001).
The low repeatability values indicate that an animal
evaluation for the traits based on repeated observations
is:
more reliable than evaluation on a single observation,
higher influence of specific environmental effects on a given
record
Genetic parameter estimates…
18

19. R = σ2 b / σ2 b + σ2 e ……………………… (1)
r = VA+VD+VI+VEP ………..………………. (2)
VP
 R = repeatability, σ2 b = variance between cows, σ2 e = variance
between records within cows.
Repeatability estimation result of:
0.39 for MY of Jersy breed (Edward et al., 2013);
between 0.17 and 0.23 for milk yield for Butana dairy cow
(Badri et al., 2009), and
Amin et al. (2013), reported repeatability for days open
and calving interval of 0.08 and 0.09, respectively for
indigenous zebu cattle.
Genetic parameter estimates…
19

20. 4.3 Genetic correlation among traits
Estimates of genetic correlation between any pair of traits suggest that
selection for one trait can lead to an indirect genetic response in the
other trait (Kanakaraj, 2001).
Buchman et al., (1982) estimated 0.28 and Clark (1985) 0.63 for
genetic correlations of birth and weaning weight for zebu cattle.
A phenotypic correlation of 0.32 between these traits was found by
Buchman et al. (1982)
Direct-maternal genetic correlation estimate of -0.54, -0.57 and –0.80
was reported for South African Bonsmara, Ethiopian Boran and Kenan
Boran (Wasike et al., 2006).
Genetic correlation between mean MY and CI increased from 0.43 in
the first to 0.58 in the second parity while that of persistency of MY
(parity 1 and 2) with CI (0.04 to 0.18) and were reported for dairy cattle
in Ethiopia by Haile-Mariam et al. (2003).
Genetic parameter estimates…
20

21. 5. Importance of genetic parameter
estimates
Parameters of a population, developed from the
knowledge of genetic variance and covariance and the
environmental contribution to the overall phenotypic
variance:
Summarizes the nature of the population and
Required for planning efficient breeding programs in
animal husbandry.
Useful in the improvement of economically important
characters in cattle through breeding (Carlos et al., 2012).
With knowledge of heritability, animal geneticists can:
determine whether or not a particular trait can be improved
by selection, by improvement of management practices, or
both; and
21

22. Importance of genetic…..
The magnitude of heritability dictates the choice of the
selection method and breeding system (Paul et al. 2003;
Navid, 2012).
Repeatability helps to know how the data reproduces
the outcome in unchanged conditions that brings
accuracy in selection program (Roman et al., 2000).
The genetic correlation expresses the extent to which
two characters are influenced by the same genes and
it is important when selecting for net merit involving
several traits.
The response of selection is the combined result of direct
selection for each trait and indirect selection caused by
the genetic correlation between the traits (Wasike et al.,
2006).
22

23. Conclusion and Recommendation
Livestock production including the dairy sector in Ethiopia had
revolutionized (demand, market, post-harvest, technologies).
The reproductive performances of Ethiopian indigenous breeds
and exotic once producing in Ethiopia shows lower result. Due
to:
Environmental factors (mainly of the changing climate) and
Absence of integrated record on the sector that leads a
biased result
Development of effective genetic evaluation and improvement
programs requires:
@ Knowledge of the genetic parameters for economically
important production traits.
23

24. …. Recommendation
Based on the review points, the following
recommendations were indicated.
focus should be given for the estimation of genetic
parameters for productive and reproductive traits.
It is necessary to keep well developed and designed
record keeping system.
 Selection and designing of breeding programs should be
based on the results genetic parameter estimates
 Estimates of genetic parameters needed to control
breeding programs,
have to be regularly updated, due to changing
environments and ongoing selection and crossing of
populations.
24