Genetic parameters for growth traits in zambian cattle

Measuring genetic parameters for growth traits in Zambian Indigenous
Cattle
E.Musimuko 1, a,b
, C.K Bottema1
, W.S Pitchford1
1
University of Adelaide, School of Animal and Veterinary Science, P.O. Box 5371SA Adelaide,
Australia.
a
Natural Resources Development College, P. O. Box 310099, Lusaka, Zambia.
b
National Institute for Scientific and Industrial Research, Lusaka, Zambia.
Corresponding author: emusimuko8@gmail.com
Short title: Genetics of growth traits in Zambian Cattle
Abstract
Genetic parameters for growth in two Zambian Cattle was estimated using data
obtained from 266 Angoni and 606 Boran weaning weights for 15 years, fitted in
linear mixed models to estimate variances and heritabilities. The calves weaned in
the winter season had a mean overall weaning weight of 131.85±1.89 kg versus
those weaned in summer with a mean overall weaning weight of 129.86±1.89 kg.
The data further indicated at weaning that the Boran calves were heavier than the
Angoni (19%, 142 vs 119 kg); steers were heavier than heifers (7%, Boran 146 vs
137 kg and Angoni 124 vs 114 kg). The change in the log-likelihood was used to
test for improvements when comparing models. Fixed effects of sex, breed, and
age were determined on weaning weight. Random effects included breed by
animal and breed by dam. Separate breed variances were not significant and so
the overall direct heritability and maternal heritability was moderate (20%±0.12
and 19%±0.18, respectively) using the best model (6). Thus, these heritability
estimates of direct and maternal effects on weaning weight indicate it should be
possible to make good genetic progress for this trait. Zambian cattle provide rich
genetic resources, exhibiting moderately heritability, and therefore, have the
potential to be improved by using appropriate planning and flexible breeding
programs.
Keywords: Heritabilities, Genetic parameters, Maternal, Direct, Growth
Introduction
Appropriate evaluation of genetic parameters for production traits is needed in
order to effectively improve cattle productivity through genetic selection. A routine
evaluation of genetic parameters is essential to help predict the genetic progress
(Goddard and Hayes, 2009). For many years, animal breeders have manipulated
the genetic composition of livestock species by making use of the variation that
exists within breeds and within populations to improve animal growth and
production (Goddard and Hayes, 2009; Eggen, 2012). In the quest to improve the
productivity of livestock populations, traditional breeding practices do not employ
molecular selection of DNA genotypes known to be associated with specific traits.
Instead, animal breeders achieve enhanced production by selecting superior
animals as progenitors in the next generation based on estimated breeding values
that are calculated from the phenotypic records of related individuals (Dekkers,
2004). In addition to phenotypic records, pedigree records and heritability
estimates are necessary for predicting the genetic progress based on estimated
breeding values (Henderson, 1975). Production traits are complex in nature,
usually quantitative, and controlled by many genes with small effects. That is, they
are polygenic and show both additive genetic effects and environmental effects
(Falconer and Mackay, 1996). Substantial evidence from quantitative genetics

studies has shown there is a statistical connection between a phenotype and its
genotypic basis, even though the exact genes determining the quantitative traits
are generally not known (Falconer and Mackay, 1996; Lynch and Abegg 2010).
There has been an unprecedented increase in demand for meat and other
livestock products during the past few decades in Africa and a corresponding need
to improve cattle production. Consequently, several studies have been conducted
to assess genetic parameters for growth rate in different African breeds of cattle in
different regions and countries of Africa. Although similar information on
production traits has been recorded for several years in Zambia, the information
has not been used to estimate the breeding values for Zambian cattle. Therefore,
estimating genetic parameters for production traits in Zambian cattle breeding
programs would be useful to predict genetic progress of the local beef cattle and to
quantify the amount of genetic improvement that can be achieved within a specific
time frame.
Material and methods
Study site
Data for this study were acquired from the Lilayi Stud farm, owned by Alan Miller,
located at Latitude S 15° 31' 7.57 and Longitude E 28° 18' 4.90 in Zambia (Figure
4.1). The farm is located at an average 1272 meters above sea level, with
temperature ranges between 10o
C to 31o
C and the annual rainfall is approximately
800mm per year (Mulemba, 2009). All pedigree cattle were registered with the
Herd Book Society of Zambia (HSBZ). HSBZ guidelines require that stud farm
owners provide notification of births with full identification of cattle in terms of
breed, species details, and tattoo marks within 30 days of birth.
The farm was opened in 1926 and in 1985; the Angoni stud herd was introduced
as a second stud herd to Boran. During the data collection, 303 cattle (62 pure
Angoni breed, 136 pure Boran breed and 105 crossbreds) were kept on the farm.
Animals were managed on natural and improved pastures of giant star grass
(Cynodon plectostachynus) or Rhodes grass (Chloris gayana) mixed with
legumes, such as Stylo (Stylosanthes guianensis) or Siratro (Macroptilium
atropurpureum) for grazing and hay.
Data collection and analysis
Numerous data sheets were scrutinised for dates of birth, weaning weights and
dates, identities for each individual animal and coding system prior to data entry
for analysis. Twelve animals without proper sire or dam records were excluded
from data analysis after the edit checks and the data from the years 1995 to 2009
were retained in this study (Figure 1). The data prior to 1995 were not included as
there were only a low number of records available prior to 1995.
Data edit and analysis for weaning weight in kilograms was assessed using
ASREML version 3.0 (Gilmour et al. 2009). After the data edit, the herd structure
was determined from the Angoni and Boran pedigrees. All detected duplicate
records were removed from the data analysis. Within the herd, there were 1128
calves from 39 sires, 11 paternal grandsires, paternal 12 grand dams, 389 dams,
25 maternal grandsires and 148 maternal grand dams were retained over five
generations (Table 1).

Table 1: Herd structure of data for Zambian zebu cattle (Bos indicus) from Lilaya Farm
*
Generation interval- average age of parents at birth of their offspring for the period 1995 to
2009. Classification of these data used Wald statistics with an adapted ASREML version 3.0
by Meuwissen and Luo (1992).
Eight different models were fitted to estimate genetic parameters using univariate
ASREML procedures (Gilmour et al., 2009. The models varied in their inclusion or
exclusion of maternal genetic effects and the correlation between direct and
maternal genetic effects. Weaning weight was analysed for genetic parameters
using a univariate REML procedure (Gilmour et al., 2009). Comparison of the
different univariate models was made using log-likelihood ratio tests to determine
the best model to fit the weaning weight data. The difference in log likelihood ratio
between pairs of models was doubled and tested against chi-square values with a
95% significance level (Gilmour et al., 2009). All calculations were carried out
using the restricted maximum likelihood method (Meyer and Hill, 1997). For each
trait in the analysis, convergence was considered to have been reached when the
variance of function values (−2*logL) was less than 10−8
. The (co)variance
components σ2
Aa, σ2
Ma, σ2
Ab, σ2
Mb, σA.Ma, σA.Mb σ2
Ea and σ2
Eb were derived at
convergence variance of 10−8
, using standard symmetric matrix notation.
The direct-maternal genetic correlations (rA.M) and the total heritabilities (h2
T) were
calculated after convergence. Total-additive heritability used in the total heritability
calculation was defined by Willham (1972) as h2
T = (σ2
A + 0.5 σ2
M + 1.5 σA.M)/ σ2
p.
The estimated covariance components were used to obtain direct heritability (h2
),
maternal heritability (m2
), the covariance between direct and maternal effects as a
proportion of the phenotypic variance for each breed (Angoni and Boran) (cAM) and
direct-maternal additive genetic correlation as rA.M = σA.M / (σ2
A x σ2
M )1/2
. However,
based on Swalve (1993), the statistical analysis of likelihood-ratio-test (LRT) was
applied by multiplying the difference by -2 and then comparing the results to chi-
square (χ2
) test statistics with the number of parameters taken as the degrees of
freedom.
Henderson (1973) described the notation as follows:
LRij =-2loge (Lj/Li) =2logeLi – 2logeL Equation 1
where Lij= likelihood of maximum models that would be compared corresponding
models. Weaning weight was analysed by fitting a linear mixed model was using
ASREML v3.0 (Gilmour et al. 2009) with fixed effects as listed below and various
random effects as outlined in Table 4.3:
Yijk = µ + Bi + Sj + Yk + Gl + (AB)(i) +S(B) +Y(B) +B(G)+ εijk
where:
Yijkl = given weaning weight,
µ = overall mean,
Bi = fixed effect of breed season (i= Angoni, Boran),
Item Category Size Number
Animal Records 1128
Sire
Angoni
39
11
Boran 28
Dam
Angoni
872
266
Boran 606
Sex of calves weaned
Female
2
452
Male 420
Year of Birth 15
*
Generations 5

Sj = fixed effect of sex levels (j= female, male),
Yk = fixed effect of the year (k= 1995, 1996…2009),
Gs = fixed effect of season (season) (s= winter, summer),
A (B) = fixed effect of age at weaning nested within breed,
S (B) = fixed effect of sex of calf nested within breed,
Y (B) = fixed effect of year of birth nested within breed,
B (G) = fixed effect of breed nested within season (season),
and
εijkl = random error.
Variance components were estimated using the ASREML programme of Gilmour
et al. (1999). The expected estimate parameters were determined using the
method that involves maximizing the likelihood function given the data (Table 4.3).
Results
Results
Distribution of births
Descriptive statistical analysis indicated that breeding occurred throughout entire
year (Figure 4.3).The highest number of calves were born in 1995 (85) and lowest
recorded in 2009 (26). Note that in 2002 and 2009 only Boran calves were born
and in 2007 only Angoni calves were born. However, Boran calves had highest
proportion (69.5%) of all the total number of calves born between 1995 and 2009.
Figure:1 Distribution of birth records from 1995 to 2009 on the Lilayi farm in Zambia.
Courtesy of Alan Miller (2012).
4.3.2 Weaning Weight
The contribution of weaning weight to the genetic improvement of growth in beef
cattle is significant because the weight of a calf at weaning reflects both the ability
for it to grow and the mothering ability of the dam. Tests of significance of fixed
effects of year of birth, season (winter and summer), breed (Angoni and Boran)
and sex (male and female) were determined (Table 2). All main effects were highly
significant but the interactions were not significant.

Table 2: Statistical analysis of variance for zebu cattle from Liyayi farm.
Source of variation Degrees of Freedom F-probability
Age 1 50.01**
Sex 1 59.66**
YOB 14 17.44**
Breed 1 50.89**
Age.Breed 1 0.09
Sex.Breed 1 0.14
YoB.Breed 10 1.24
Breed.Season 2 0.03
**P < 0.001; YOB= year of birth; DF= degrees of freedom. Breed =Angoni and Boran cattle
The year with the highest weaning weight was 1998 (152±3.3 kg), while the lowest
was in 1996 (105±4.2 kg) (Figure 4.4). The data further indicated at weaning that
the Boran calves were heavier than the Angoni (19%, 142 vs 119 kg); steers were
heavier than heifers (7%, Boran 146 vs 137 kg and Angoni 124 vs 114 kg)
(Appendix 4.2). The average age at weaning was 212 days and weaning weight
was 137 kg. Based on the regression of weaning weight on age, the average daily
gain was 0.19 kg/d for the Angoni calves and 0.26 kg/d for the Boran calves.
Figure : Annual variation in weaning weight for Angoni and Boran calves born 1995 to 2009.
Data courtesy of Alan Miller (2012). YOB = Year of Birth.
Estimates of covariance components and genetic parameters
The covariance components and genetic parameters for weaning weight for
Angoni and Boran cattle were estimated (Table 4.5). Model 6 estimated 3
components: the additive, maternal and residual components. Models 1-5 tested
for significant improvements by including additional components for breed
differences in each of the components in Model 6 and also for the covariance
between additive and maternal effects. Thus, Model 1 had 8 components.
However, adding any of these additional 5 components was not a significant
improvement in the fit except for possibly model 5 which had 4 components: a
common direct and maternal genetic effect but separate residual variances for the
two breeds.

Model 8 differed from Model 6 by dropping the maternal component and included
only an additive and residual effect (2 components) but there was a significant
deviation indicating the maternal effect should be included. Model 7 was similar to
Model 8, but had separate breed effects for the additive and residual components
(for a total of 4), but was no better than Model 8. Although not presented, a
common covariance was also tested in addition to the 3 components in Model 6
but it was not significant. Thus, it was clearly demonstrated that common variance
components can be used across the breeds and that both an additive and
maternal genetic effect on weaning weight should be included.
The results indicate no significant difference in the heritability between the Angoni
and Boran breeds although model 5 (with common genetic effects but different
residual variances) resulted in slightly different heritabilities (Table 4.5). For Model
6, the direct heritability for Angoni and Boran obtained was 20% and the maternal
heritability was 19%.

Table 4.1: Estimated genetic parameters for weaning weight in zebu cattle (Bos indicus).
Parameters
MODELe
1 2 3 4 5 6 7 8
σ2
Aa 46.7±0.9 52.4±0.9 - 77.3±1.1 - - 70.8±3.6
σ2
Ab 96.4±1.6 73.7±1.9 - 70.8±1.9 - - 77.7±3.9
σ2
A - - 70.3±2.11 - 71.8±2.3 71.1±2.2 - 149.4
σ2
Ma 50.5±1.2 76.9±2.4 72.3±2.44 - - - -
σ2
Mb 71.7±1.8 51.7±2.9 52.3±2.22 - - - -
σ2
M - - - 61.4±3.1 62.1±3.2 64.5±3.3 -
σ A,Ma 28.1±0.8 - - - - - -
σ A,Mb -25.9±-0.6 - - - - - -
rA.Ma 0.58 - - - - - -
rA,Mb -0.31 - - -- - - -
σ2
Ea 194.1±5.4 189.6±4.8 180.7±6.4 179.4±4.3 182.3±6.5 - 152.73±4.8
σ
2
Eb 218.7±6.4 256.7.2±8.9 258.4±9.5 254.1±8.9 253.3±3 - 222.4±7.1
σ
2
E - -- - - - 231.1±10 -
a
σ
2
Pa 319.8 318.9 323.3 318.1 316.2 - 223.5
a
σ
2
Pb 360.6 382.1 377.0 386.3 387.2 - 300.1
a
σ
2
P - - - - - 348.7 - 373.3
b
h2
Aa 14.6 16.4 21.7 24.3 22.7 - 31.7
b
h2
Ab 26.7 19.3 18.6 18.9 18.5 - 25.9
b
h
2
A - - - - - 20.4 - 40.0
c
h
2
Ma 15.8 24.1 22.4 19.3 19.6 - -
c
h
2
Mb 19.9 13.5 13.9 15.9 16.0 - -
c
h2
M - - - - - 18.5 -
d
h2
Ta 35.7 - - - - - -
d
h2
Tb 25.9 - - - - - -
LogL -2914.6 -2915.0 -2915.0 -2915.2 -2915.2 -2917.8 -2923.3 -2924.4
LRij (0.78) (0.04) (0.28) (0.04) (5.2) (11.1)
**
2.2
LRij B 18.4**
13.2** 2.2
**p<0.01; a
subscript(σ2
p) = Σ(σ2
A + σ2
M + σA.M + rA.M + σ2
E); b
subscript (h2
A) = σ2
A/σ2
p);c
subscript (h2
M)= σ2
M/σ2
p; d
subscript( h2
T)=( σ2
As + 0.5σ2
Ms
1.5σA.Ms)/ σ
2
p
e
MODEL –description of all models are in the text. Fully Parameters are explained (Table 4.2) LRij=2logeLi – 2logeL, LRijB indicate
difference between 8 and other models in question.

Discussion
Genetic parameters for weaning weight in zebu cattle
The results are not in agreement with some studies (Pico et al., 2004). The direct and
maternal heritability estimates herein (20 and 19% respectively) were different from
another study of Brahman cattle (Pico et al., 2004) which found a 14% direct
heritability and 6% maternal heritability, but they were similar to the estimates of 20%
and 15% for Tswana indigenous cattle in Botswana (Raphaka and Dzama, 2010).
These studies indicated that maternal heritability significantly differed from direct
heritability using different models. Moreover, Praharani (2009) demonstrated that
there was significant difference in the co-variances between additive and maternal
effects in Bali cattle. The direct and maternal heritability estimated were 30% and
8%, respectively. One major factor that possibly could cause a large difference in
direct and maternal heritability estimates would have been that the Bali cattle were
largely been supplemented throughout the season such that environmental factors
would contribute largely to the final weaning weight. This was different from Angoni
and Boran cattle herein that were pastures fed only.
Diop and Van Vleck (1998) found the same heritability estimate as herein (20%) for
zebu Gobra (Bos indicus) cattle in Senegal, and the heritability estimated for
Brahman cattle in Venezuela was only slightly lower (16%) (Plasse et al., 2002). The
direct (24%) and maternal (13%) heritabilities of Boran obtained in the study by Koots
et al. (1996) were also similar to the Zambian cattle in this study. The results herein
are also comparable to the indigenous Tswana cattle in Botswana, with direct
heritability estimates of 20% and maternal heritability estimates of 25%. However, the
heritability estimates of the Boran cattle herein (direct =19%-27% and maternal =14-
20%) were lower than the Boran heritability estimates in Kenya reported by Wasike
et al., (2006) (direct =31% and maternal =16-23%).
Several factors may have been responsible for the differences in the heritability
estimates between the Kenya Boran and Zambian Boran, including the differences in
the size of the data sets (Wright et al., 1985) and in the models applied to estimate
the genetic effects. The direct and maternal heritability (45% and 5%, respectively)
estimated in Asturiana de los Valles beef cattle (Gutierrez, 1997) using an animal
model that included additive and maternal effects were different from estimates in
this study, even though the `best' animal model were chosen according to the
significant difference in the likelihood values similar to herein. The underlying reason
may be attributed to the different types of breeds. The Asturiana is a double muscling
taurine breed while the Zambian breeds are zebu and not doubled muscled.
Interestingly, Meyer and Hill (1997) reported direct heritability (19-22%) and maternal
heritability (14-18%) in Hereford cattle similar to this study. This is significant
because by employing the appropriate genetic improvement program, Zambian cattle
should achieve the same genetic gains observed in Hereford.
The significant maternal component is supported by most other studies analysing
weaning weight that have used various models to measure the genetic parameters
for growth traits in cattle (Nester et al., 1996; Meyer 1997; Wasike, 2006).
Interestingly, the heritability levels were moderate but comparable to other breeds
suggesting the lower observed post-weaning, joining and mature weights are
attributed to environmental variance rather the lack of genetic variance. This further
supports the finding of Thorpe et al. (1980) and Walker (1964) who reported that
Angoni cattle are more efficient in terms of calf weaned per cow than Boran cattle

Non genetic parameters
Effect of year of birth
Weaning weights were consistently higher from 1998 to 2006 and lower from 2007 to
2009 (Figure 4.4). Numerous factors could have contributed to the variation in
weaning weights observed over this 15 year period. For example, in sub-Sahara
Africa regions, including Zambia, there has been a wide variation of annual rainfall
and distribution patterns within seasons that are usually characterised by long
droughts and heavy rainfalls (Marlowe and Gains 1958). The long persistent
droughts and heavy storms that results in floods affect both the quantity and quality
of pastures produced (Okantah, 1992; Sottie et al., 2009). This has a direct
relationship with cattle output, affecting the weights of calves weaned in a particular
year. Pauw and Thurlow (2011) showed that the quantity of maize yield was strongly
positively correlated to the patterns of rainfall in a study on climate change, reflecting
the effects of varying periods of drought that influence the availability of pastures and
feed. Similar year effects on weaning weight (P<0.01) were obtained in a study of
Bali cattle (Praharani, 2009).
The results of this study suggest that the season of birth has only a slight influence
on weaning weight (Appendix 3.2). Comparing the two seasons (winter and summer)
using simple averaging statistics indicates a difference of 2 kg between the 2
seasons. The calves weaned in the winter season had a mean overall weaning
weight of 131.85±1.89 kg versus those weaned in summer with a mean overall
weaning weight of 129.86±1.89 kg. The higher weights obtained in winter seasons
are similar to those reported in other studies (Manzi et al., 2002; Pico et al., 2004).
The calves born in winter may have a better chance of utilising good pastures prior to
weaning. Calves born in winter are also usually weaned when the rumen is probably
fully functional, six months after the date of birth (Heinrichs 2009). It is likely that the
difference in weaning weight between the winter season and summer season may be
influenced by environmental factors rather than genetic factors (Marlowe and Gains
1958).
Effect of sex
The results in this study indicate a significant difference (P<0.001) in mean weight
between weaner steers (135.7±2.15kg) and weaner heifer (126.1± 2.15kg). The
difference in weaning weight between males and females has long been explained
by the action of testosterone circulating in the blood system of males responsible for
secondary sexual characteristics (e.g. skeletal conformation and muscle size) acting
as a growth hormone. However, the sex by breed interaction on weaning traits was
not significant herein. Therefore, the indigenous Angoni breed should still respond
favourably, similar to the improved Boran cattle subjected to same management
systems.
Effect on breed
At weaning, the Boran calves were 19% heavier than Angoni (142 vs 119kg). These
results are in agreement with other studies that have found Boran calves have
greater weaning weights than Angoni calves (Thorpe et al., 1981). Studies in Kenya
showed significant differences between the weaning weights even in the small Boran
cattle versus the improved Boran cattle (Haile‐Mariam and Kassa‐Mersha, 1995).
However, Walker (1972) showed that when weaning weights are translated as a total
output of a cow-calf unit, there was no statistically significant difference between the
Angoni and Boran breeds (P>0.05). This suggests that even though the Angoni
breed has lower weaning weights than the Boran, the Angoni breed is likely to be as
efficient and productive as the Boran. Nesting breed with each of other fixed effects

(sex, year of birth and season) within the models showed no statistical difference
under similar management systems.
Conclusion
Weaning weight was analysed as a model for other traits to quantify genetic variation
in Zambian breeds under Zambian conditions. This study has confirmed that weaning
weights are affected by both additive and maternal genetics and separate
parameters for breeds are not necessary despite them differing in weaning weight by
19%. Common fixed effects of age, sew, season and year of birth have been also
quantified. The results are similar to studies in other countries and breeds and the
moderate heritability estimates demonstrate is there is ample opportunity for making
genetic improvement in weaning weight
References
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Genetic parameters for growth traits in zambian cattle

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