Livestock Research for Rural Development 23 (6) 2011 | Notes to Authors | LRRD Newsletter | Citation of this paper |
Data were collected between 1968-1988 from a selection experiment involving the purebred Gudali and a two- breed- synthetic, the Wakwa. The data were collected at the experimental farm of the Animal and research stations of Wakwa, Ngaoundere, Cameroon and analyzed using the Proc Mixed procedure and MTDFREML.
Estimates for direct and maternal heritabilities were 0.37 and 0.05 and 0.55 and 0.23 for birth weight (BWT); 0.27 and 0.19 and 0.28 and 0.00 for weaning weight (WWT); 0.51 and 0.20 and 0.18 and 0.00 for yearling weight (YWT); 0.18 and 0.02 and 0.14 and 0.06 eighteen-month weight (EWT); 0.25 and 0.09 and 0.22 and 0.03 for twenty four month weight (TFM); 0.18 and 0.03 and 0.34 and 0.05 for thirty month weight (TWT) and 0.18 and 0.07 and 0.33 and 0.10 for thirty six month weight (TSW) for Gudali and Wakwa breeds, respectively. Estimates for genetic correlation between direct and maternal effects were -0.88 and -0.90 for BWT; -0.77 and -0.76 for WWT; -0.81 and -0.98 for YWT; 0.00 and 0.00 for EWT; -0.26 and -0.20 for TFWT; -0.45 and -0.34 for TWT and -0.02 and -0.05 for TSWT in Gudali and Wakwa, respectively. Most of the estimates for direct heritability were moderate to high; indicating that it was possible to improve upon the trait through selection; though the overall genetic progress might be hampered with, if selection was to be concentrated on direct performance, given the genetic antagonism between direct and maternal effect as indicated by the negative genetic correlations.
Keywords: Genetic parameters, Gudali, Wakwa, Cameroon, beef cattle
The annual meat production in Cameroon is about 105,052 tons, short of the annual demand estimated at 161,000 tons (Unpublished). The shortfall is made for by importation of about 36,000 tons of beef from Tchad and Central African Republic; and about 20,000 tons from Europe and Argentinia (World Bank 1989). Given the trends, successive governments of Cameroon have put in place a number of research programs aimed at improving beef cattle productivity (Mandon 1957; Lhoste 1968, 1969, 197; Mbah 1992; Tawah et al 19967). A successful improvement scheme was that obtained from crossing the American Brahman as paternal line and the local Gudali as the dam line. The resulting F1 generation proved to be most adaptable to the Cameroon environment, though highly susceptible to streptothricosis. On inter se mating it produced a filial generation, code named Wakwa that turned up to be more tolerant to streptothricosis. An alternative scheme which turned out to be very successful was the Gudali operation which was simply a systematic selection based on growth performance as to enhance the beef potential. The systematic selection on both the Wakwa and Gudali was carried on for a period of over thirty years and a lot of data had accumulated. Some studies attempted to evaluate the data: Abassa et al (1993) quantified factors affecting birth and weaning weight of Gudali and Wakwa assuming that herd effect was not important; Tawah et al (1993, 1994) estimated genetic parameters and trends for birth and weaning weights from data collected between 1971 and 1985. They equally assumed herd and permanent maternal environmental effects not to be important. Though genetic parameters for growth traits of various breeds of cattle exist in the literature, it is important that such estimates be obtained under a specific management and production environment as to evaluate the level of inheritance of any growth trait. The basic problem is the choice among alternative measures of growth traits those that can be useful for improvement strategies; a choice depending on the level of inheritance determined by estimated genetic parameters.
The study was conducted at the Beef- Herd Unit of the Wakwa Centre of the Institute of the Agricultural Research for Development, located in the Adamawa Region of Cameroon. Adamawa is situated between latitudes 60 and 80°N and 100 and 160°E. It is situated at 1100 m above sea level. It shares the Southern border with the Centre and East Regions; the South West border with the North-West and West Regions; the West with Nigeria; the East with the Central African Republic and the North with the North Region. It has a land area of over 64,000 km and is the third largest out of the ten Regions of Cameroon. The land is rugged and sparsely populated; with most of the population involved in cattle rearing (Bayemi et al 2005). The climate is characterized by a 3 to 5 months and 7 to 9 months of dry and wet seasons, respectively. Rainfall averages 900 to 1,500 mm per year and decreases further north. Mean relative humidity and temperature are 0.673 and 22.0° C and minimum and maximum temperatures are 10 and 34°C, respectively (Bayemi et al 2005).
For the Wakwa breed it was composed of 45 purebred Brahman bulls imported from the USA between 1952 and 1958 and six herds each consisting of 40 Gudali cows in 1965, and in 1969, 12 herds each consisting of 40 Gudali breeding cows (Tawah 1992). Purebred foundation bulls for the Gudali were purchased from the local farmers and were meticulously selected for breed standards including coat colour, age, size, conformation, temperament, adaptation and fertility as defined by Mandon (1957).
The Wakwa and Gudali breeds have been described by Mandon (1957); Lhoste (1969) and Tawah and Mbah (1989). The Wakwa is a two-breed synthetic developed from inter se mating of American Brahman x Gudali first filial generations and have been maintained at 0.50 exotic germplasm. The Gudali is a short-horned West African zebu, predominantly a subtype of the Adamawa Gudali that inhabits the Adamawa mountain ranges stretching from Nigeria to Cameroon (Tawah and Rege 1996). The breed is of good temperament and of excellent beef conformation and possesses a natural ability to produce and reproduce optimally under prevailing local conditions without much additional inputs (Tawah et al 1993). It is predominantly found in Ngaoundere in the Adamawa Region of Cameroon with some strains found in Banyo. It is a popular breed, especially in the smallholder sector of the Adamawa highlands of Cameroon (Tawah et al 1996).
Breeding cows were annually reshuffled within breed into various breeding herds, and breeding bulls assigned randomly to about 30 to 40 cows while ensuring minimum inbreeding. At birth, calves were weighed within 24 hours. They were ear-tagged and pedigreed according to sires and dams. Records were also kept on breed type, sex, date; month and year of calving. The calves were weighed monthly, subsequently. At weaning, 12, 24 and 36 months the animals were subjected to a selection scheme which was based on individual and progeny performance (Lhoste 1977; Tawah et al 1994). All the herds were maintained at the Wakwa Research Station and Breeding Station of the Ministry of Livestock, Fisheries and Animal Industry. Grazing and management were essentially extensive on natural pastures growing on granitic and basaltic soils. The pastures composed principally of Hyparrhenia spp, Panicum maximum, Andropogon guyanensis and Pennisetum purpurreum (Piot and Rippstein 1975). The herds were supplemented with rice bran and cotton-seed cake during the stressful dry season. Details on management system and production conditions have been documented (Lhoste 1968, 1977; Pamo and Yonkeu 1987; Tawah and Mbah 1989). Health management involved routine dipping against ticks, vaccinations against pasteurellosis, brucellosis, anthrax and rinderpest, and de-worming. Water was available all year round.
Data on calves born from 1968 to 1988 were compiled from various Adamawa herd-books maintained at the Wakwa Research Station. The data compiled included pedigree information on individual calves, sex, date, month and year of calving, birth years of sires and dams, birth weight (BWT) and weights at weaning (WWT), yearling (YWT) and eighteen months (EWT) selected from monthly weights for dates closest to eight, twelve and eighteen months, respectively. Weaning age (WAGE), yearling age (YAGE), eighteen-month age (EAGE), (adjusted weights for eight, twelve and eighteen months, respectively) and cow age group (CAG) were derived from the data set. CAG was calculated as the deviation of the dam’s year of birth from her corresponding calf’s birth year (CBY). Three cow age categories were defined; CAGl attributed to cow age group less than 8; CAG2 to cow age group greater than 7 but less than 11 and CAG3 to cow age group greater than 10. Two seasons were defined according to Abassa et al (1993) and Tawah et al (1993): a five months dry season extending from November to March and a seven months rainy season from April to October.
The data were edited for valid pedigree information, consistency checks of dates, ages at weaning, yearling, eighteen months and for weight ranges considered unreasonable for the age and sex of the animal. As a result, all birth weights less than 15kg or greater than 35 kg were discarded. Progeny not identified with herds were discarded and weaning weights less than 100 kg and yearling and eighteen months weights less than 120kg were omitted.
The Least Squares Means were estimated by use of Proc Mixed of the Statistical Analytical Systems (1991). Only fixed effects that significantly affected the dependable variables were included in the animal models for the estimation of genetic parameters. Restricted maximum likelihood estimates were obtained by the MTDFREML software (Boldman et al 1995). The animal model used for each trait was that that had the additive direct effect, maternal direct effect correlated to the direct effect, non-additive maternal permanent environmental effect, uncorrelated to direct and maternal effects and environmental effect, associated with the animal, fitted as random effects. Sex, season of calving, herd, calf birth year (CBY) and cow age group (CAG) were fitted as fixed effects. Ages at weaning (WAGE), yearling (YAGE) and eighteen months (EAGE) were fitted as linear covariates on weaning, yearling and eighteen months weights, respectively. The Simplex Method was used to search for a maximum of the residual likelihood function by evaluating likelihoods over a network of points determined from the simplexes with a 10E-9 used as stopping criterion. Where the search strategy did not converge to global maximum, the program was re-started until twice the logarithms changed no more by 0.00 to 0.02. Priors for (co)variance components were obtained from reported estimates by Tawah et al (1993).
The model in matrix notation was presented as y = Xb + Z1a+ Z2m + Z3c + e; where
y was an observation vector for growth traits (BWT, WWT, YWT, EWT) records;
X, an incidence matrix relating observations to the fixed and covariate effects;
b, a vector of identifiable non-random fixed (sex, season and year of calving, CAG) and covariate (WAGE, YAGE, EAGE) effects;
Z1, Z2 and Z3, known incidence matrices relating elements of additive direct, additive maternal direct and non-additive maternal permanent environmental effects to y;
a and m, non-observable correlated random vectors for direct and maternal effects;
c, nonobservable uncorrelated random vector associated with the non-additive maternal permanent environmental effect and e, random vector associated with residual effect of error. The vectors a, m, c and e were assumed to be multivariate normal (MVN) with zero mean and variance, ó2. Dams were assumed to be related only by their sires.
The variance-covariance structure was presented as
with s˛a is the direct additive genetic variance, s˛m the direct maternal genetic variance, sam the covariance for additive direct-maternal genetic effects, s˛c the uncorrelated non-additive maternal permanent environmental effect, A the numerator relationship matrix, I the identity matrix and s˛e the error random variable for the trait.
Sex, season of calving, herd, calf birth year (CBY) and cow age group (CAG) significantly (P<0.001) affected the performance traits (Ebangi et al 2002) and were included in the animal models. There was equally a highly significant breed effect ((P>0.0001) with marked differences between corresponding weight; the Wakwa having superior weights over the Gudali. This could be as a result of the positive effects of crossbreeding such as heterosis and the utilization of breeds’ differences to optimize genetic merit of performance traits under different environmental conditions (Koch et al 1985). Inbreeding coefficient (IC) was quite low in the two breeds and ranged between 0.07 and 0.10, indicating that inbreeding at present was not a serious problem in the Gudali and Wakwa beef breeds of Cameroon. Ndofor-Foleng et al (2010) obtained an average inbreeding coefficient of 0.07 for both Gudali and Wakwa.
The estimates of (co)variance components are presented in Tables 1 and 2 for the Gudali and Wakwa breeds, respectively. Estimates for direct additive genetic variances were higher than corresponding maternal genetic variance components. The estimates for direct additive genetic variances for Wakwa were higher than those for Gudali from birth to weaning and thereafter it was the reverse. The direct-maternal covariances were all negative but for EWT in both breeds.
Table 1. (Co) variance estimates for pre-weaning and post-weaning growth traits in Gudali beef cattle |
||||||
Trait/(Co)variance |
Ó2A |
Ó2M |
ÓAM |
Ó2PE |
Ó2e |
Ó2P |
BWT |
2.60 |
0.37 |
-0.87 |
0 |
5.03 |
7.16 |
WWT |
188 |
127 |
-119 |
40.3 |
449 |
686 |
YWT |
309 |
122 |
-157 |
57.3 |
273 |
605 |
EWT |
172 |
16.1 |
0.12 |
0.211 |
788 |
976 |
TFWT |
360 |
125 |
-54.8 |
79.7 |
1012 |
1443 |
TWT |
265 |
46.1 |
-50.2 |
55.8 |
1007 |
1456 |
TSWT |
262 |
100 |
-3.69 |
55.8 |
1017 |
1456 |
Ó2A direct additive genetic variance, Ó2M maternal additive variance, ÓAM direct-maternal covariance, Ó2PE permanent maternal environmental variance, Ó2e residual variance, Ó2P phenotypic variance |
Table 2. (Co) variance estimates for pre-weaning and post-weaning growth traits in Wakwa beef cattle |
||||||
Trait/(Co)variance |
Ó2A |
Ó2M |
ÓAM |
Ó2PE |
Ó2e |
Ó2P |
BWT |
4.63 |
1.98 |
-2.73 |
0 |
4.61 |
8.50
|
WWT |
214. |
67.9 |
-91.9 |
110 |
453 |
754 |
YWT |
107 |
0.013 |
-1.17 |
71.7 |
434 |
754 |
EWT |
157 |
64.5 |
0.012 |
0.174 |
878 |
1100
|
TFWT |
312 |
39.9 |
-22.3 |
125 |
990 |
1444
|
TWT |
265 |
46.1 |
-50.2 |
55.8 |
1007 |
1456
|
TSWT |
535 |
161 |
-15.5 |
11.1 |
912 |
1613
|
Ó2A direct additive genetic variance, Ó2M maternal additive variance, ÓAM direct-maternal covariance, Ó2PE permanent maternal environmental variance, Ó2e residual variance, Ó2P phenotypic variance |
The trends between direct and maternal genetic variances reported are in agreement with those reported by some researchers (Trus and Wilton 1988; Burfening et al. 1981; Bertrand and Benyshek 1987; Kars et al 1994; Haile-Mariam and Kassa-Mersha 1995; Meyer 1992). However, Wright et al (1991), and Diop and Van Vleck (1998), have reported higher estimates for maternal variances as against lower estimates of direct variance components for weaning and eighteen months weight.
Estimated genetic parameters are presented in Tables 3 and 4. Apart from EWT, TWT and TSWT for the Gudali; and YWT and EWT for Wakwa with low estimates for direct heritability, indicating low inheritance, the other estimates were moderately to highly heritable.
Table 3. Estimates of genetic parameters for pre-weaning and post-weaning growth traits in Gudali cattle |
||||||
Trait/parameter |
h2A |
h2M |
h2T |
c2 |
cAM |
rAM |
BWT
|
0.37
|
0.05
|
0.21
|
0
|
-0.12
|
-0.88
|
WWT
|
0.27
|
0.19
|
011
|
0.05
|
-0.17
|
-0.77
|
YWT
|
0.51
|
0.20
|
0.22
|
0.09
|
-0.26
|
-0.81 |
EWT |
0.18 |
0.02 |
0.18 |
0 |
0.12 |
0.00
|
TFWT |
0.25 |
0.09 |
0.24 |
0.06 |
-0.04 |
-0.26
|
TWT |
0.18 |
0.03 |
0.20 |
0.06 |
-0.03 |
-0.45
|
TSWT |
0.18 |
0.07 |
0.21 |
0.04 |
0.003 |
-0.02 |
h2A direct heritability, h2M maternal heritability, h2T Ó2A +1/2Ó2M +3/2ÓAM), c2 permanent maternal environmental variance as a proportion of the phenotypic variance, cAM direct-maternal covariance as a proportion of the total phenotypic variance, rAM genetic correlation between direct and maternal genetic effects |
All direct heritability estimates were higher than corresponding maternal (h2M) estimates. Similar patterns have been reported by some researchers (Burfening et al. 1981; Bertrand and Benyshek, 1987; Arnason and Kassa-Mersha 1987; Trus and Wilton 1988; Meyer 1992; Shi et al 1993; Kars et al 1994; Koots et al 1994; Khombe et al 1995; Haile-Mariam and Kassa-Mersha 1995; Van der Westhuizen 1997), though higher maternal heritabilities as against lower direct heritabilities are common in the literature (Wright et al 1991; Brown and Galvez 1969; Nelsen et al 1984; Cantet et al 1988; Hohenboken and Brinks 1971).
In this study maximum genetic contributions to total phenotypic variance were 51 and 20 % for YWT of Gudali, and 55 and 23% for BWT of Wakwa, for the direct and maternal direct; effects associated with the genotype of the Gudali and Wakwa, respectively. Consequently birth weight of Wakwa and yearling weight for Gudali were highly heritable. Selection therefore based on BWT could invariably bring about very large calves but this could increase the incidence of dystocia effect. Selection based on the yearling weight will invariably bring about animals with very high yearling weights.
Table 4. Estimates of genetic parameters for pre-weaning and post-weaning growth traits in Wakwa cattle |
||||||
Trait/Parameter |
h2A |
h2M |
h2T |
c2 |
cAM |
rAM |
BWT |
0.55 |
0.23 |
0.18 |
0 |
0.32 |
-0.90
|
WWT |
0.28 |
0.00 |
0.15 |
0.15 |
0.12 |
-0.76
|
YWT |
0.18 |
0.00 |
0.17 |
0.12 |
0 |
-0.98
|
EWT |
0.14 |
0.06 |
0.17 |
0 |
0 |
0.00
|
TFWT
|
0.22 |
0.03 |
0.12 |
0.09 |
-0.05 |
-0.21 |
TWT |
0.34 |
0.05 |
0.43 |
0.12 |
-0.015 |
-0.34
|
TSWT
|
0.33 |
0.10 |
0.38 |
0.01 |
-0.01 |
-0.05 |
h2A direct heritability, h2M maternal heritability, h2T Ó2A +1/2Ó2M +3/2ÓAM), c2 permanent maternal environmental variance as a proportion of the phenotypic variance, cAM direct-maternal covariance as a proportion of the total phenotypic variance, rAM genetic correlation between direct and maternal genetic effects |
The maternal heritability obtained in the study at birth (0.23) for the Wakwa and yearling (0.20) for the Gudali were moderate, an indication that improvement in the maternal traits could be effective. These results are generally in agreement with those reported by Tawah et al (1993) but for the very high direct heritability estimate (0.65) for birth weight; higher maternal heritability estimate (0.27) for weaning weight and lower negative genetic correlation (-0.39) estimate between direct and maternal effects for birth weight for the Wakwa. With the exception of the high direct heritability (0.51) at yearling for Gudali, the direct heritabilities reported at birth, WWT and EWT for Gudali in this study are within ranges for some tropical zebu cattle (Iloeje 1986; Kars et al 1994; Haile-Mariam and Kassa-Mersha 1995; Khombe et al 1995; Barlow 1978; Mohiudden 1993; Koots et al 1994) but for reports by Diop and Van Vleck (1998) on the Gobra breed. Genetic parameter estimates for tropical crossbred zebu are few in the literature. However, the estimates obtained for the different growth traits in Wakwa are within range of estimates obtained by Mackinnon et al (1991) for Africander crosses but differ from estimates reported by Deese and Koger (1967) on crossbred Brahman x Shorthorn.
The estimates for the genetic correlations between direct and maternal genetic effects were high but negative and ranging from -0.88 to -0.77 for Gudali; and -0.98 to -0.76 for Wakwa. They are higher than reported estimates by Kars et al (1994), Khombe et al (1995), Haile-Mariam and Kassa-Mersha (1995), Neser et al (1996) and Diop and Van Vleck (1998) for some tropical beef cattle but remain within range of estimates in literature (Hohenboken and Brinks 1971; Thompson 1976; Meyer 1992). Higher negative estimates of -1.05 and -0.61 have been reported by Cantet et al (1988) and Robinson (1996). Conversely, positive direct-maternal genetic correlations have been reported in the literature for different performance traits in beef cattle (Brown and Galvez, 1969; Thompson 1976; Wright et al 1987; Meyer 1992; Kars et al 1994; Van der Westhuisen 1997). Consequently, the magnitude and direction of the genetic correlation between direct and maternal effects appear to be inconclusive. Tawah et al (1993) attributed the high negative estimates in Gudali and Wakwa breeds to a form of adaptive mechanism towards the harsh tropical environment. Females which are inherently small as calves have an adaptive genetic advantage by the fact that they grow up to be small dams utilising effectively the suboptimal tropical production environment for their maintenance and for the growth of their calves than would be larger dams. Other scientists have offered alternative explanations to the apparently high negative direct-maternal genetic correlation, which could be a serious impediment to selection progress. Robinson (1996) attributed the high negative estimate to negative dam-offspring covariance effects or additional sire variations or sire x year variations not accounted for in the estimation models. Meyer (1997) attributed it to sources of variations such as paddocks or management groups not accounted for in the analyses. Lee and Pollak (1997a and b) attributed it to selective reporting, sire x year interaction and to potential heterogeneity of the correlation by gender not taken into account in the estimation of genetic parameters. The problem of heterogeneous variances has also been reported by Thrift et al (1981) and Garrick et al (1989) to account for the high negative estimate. Neser et al (1996) attributed the high negative estimate to herd-year-season x sex interactions. According to Van Vleck et al (1977), the practical implication of the high negative direct-maternal genetic correlation is the possible reduction in expected response to selection. These authors are unanimous that selection of males for direct and females for maternal genetic values be implemented in the cases where this estimate is highly negative. This will result in greater selection response in progeny after the first generation than would be, if selection of dams were based on direct genetic values. Considering the various explanations from different authors, direct-maternal genetic correlation may therefore be negative not because of genetic antagonisms between direct and maternal effects but because of certain variations which might not have been taken into account in the course of estimation.
Estimates for direct heritability were generally moderate to high. Moderate estimates were obtained for BWT, WWT and TWT and high estimate obtained for YWT in the Gudali. For the Wakwa moderate estimates were obtained for WWT, TFWT, TWT and TSWT and high estimates obtained for BWT for Wakwa. These moderate to high estimates of direct heritability are indications of moderate to high level of inheritance. But for YWT for the Gudali and BWT for the Wakwa and YWT that were moderate for maternal heritability, the rest were very low or non existence. Selection based on BWT will likely lead to a high incidence of dystocia. Also selection based on direct heritability will lead to a depreciation of the maternal traits as a result of the low estimates reported for maternal heritability.
The estimates for direct-maternal genetic correlation obtained in the present study were negative and high; suggesting a possible genetic antagonism between the direct and maternal genetic effects. The practical implication is that selection based solely on own performance will bring about a reduction in total expected response to selection. The level of inheritance obtained in this study, though generally moderate to high will be hampered by the low level of inheritance for the maternal traits and the very high but negative genetic correlation between the direct and maternal genetic effects.
The authors are grateful to the Institute of Agriculture Research for Development for authorizing the publication of this work and to the South Africa National Research Foundation for funding the realization of the work.
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Received 26 April 2011; Accepted 31 May 2011; Published 19 June 2011