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AN INVESTIGATION INTO THE POSSIBLE GENETIC IMPROVEMENT OF REPRODUCTION AND SURVIVAL RATE IN AFRINO AND MERINO SHEEP USING A THRESHOLD MODEL

 

WJ Olivier1, M.A.SNYMAN1,  J.J.OLIVIER1, J.B. VAN WYK2 & G.J.ERASMUS2

1Grootfontein Agricultural Development Institute, Private Bag X529, Middelburg, Cape, 5900, South Africa

2Department of Animal Science, University of the OFS, Box 339, Bloemfontein, 9300, South Africa

 

 

Introduction

Reproduction and survival rate is of great economical importance to the small stock industry. It was suggested by Dickerson5 that there is more potential for increasing both biological and economic efficiency of lamb production through genetic improvement in reproduction than in growth rate and body composition.

 

In the past when data were analysed, linear models, such as Harvey=s10, were the most frequently used for discontinuous and continuous data. The problem with a linear model is that the method of analyses is based on a continuous phenotypic distribution and does not take the discontinuity of threshold traits into consideration. The main theoretical reason fornot using BLUP (Best Linear Unbiased Prediction) with categorical data is that breeding values and residuals are not independent of each other8. Threshold models is therefore more suitable when analysing reproduction traits.

 

The reproductive traits analysed in this study included fertility, fecundity and  litter size at birth and weaning. Survival rate, although not a reproductive trait by definition, was also analysed. The main objectives of this study was the estimation of heritabilities and sire breeding values for each of these traits using a threshold model and to investigate the possibility of selection based on these estimated heritabilities and resulting breeding values.

 

Materials and methods

Data collected on the Grootfontein Merino stud (from 1968 to 1996), the Carnarvon Merino flock (from 1964 to 1983) and the Carnarvon Afrino flock (from 1972 to 1994) were used for this study. Detailed descriptions of the management and selection procedures followed in these flocks were reproted earlier for the Grootfontein Merino stud14, for the Carnarvon Merino flock6 and for the Carnarvon Afrino flock15.

 

The Grootfontein Merino stud (GMS) is run under relatively favourable nutritional conditions, which include irrigated pastures and supplementary feeding. Grootfontein Agricultural Development Institute is located near Middelburg (31°28'S,25°1'E) in the North-eastern Karoo region. The Carnarvon Afrino (CAF) and Merino (CMF) flocks were kept on natural pasture at the Departmental Experimental Station near Carnarvon (30°59'S,20°9'E) in the North-western Karoo region. The vegetation consists of mixed grass and karoo shrub and is described as arid karoo. The average annual rainfall is 209mm and it occurs mainly during the autumn months. The official grazing capacity is 5,5 hectare per small stock unit.

 

The reproductive traits analysed included fertility (whether a ewe lambed or not; 0 or 1), litter size at birth (number of lambs born to a ewe mated; 0,1,2 or 3), fecundity (number of lambs born to a ewe that lambed; 1,2 or 3), litter size at weaning (number of lambs weaned to a ewe mated; 0,1,2 or 3) and survival rate from birth to weaning (whether a lamb born alive, was dead or alive at weaning; 0 or 1). Only sires with more than ten progeny for GMS and CMF and seven sires for CAF were retained for the analyses. The description of the data sets are given in Table 1.

 

The data were analysed by means of a GFCAT program12. GFCAT is a set of programmes for the analyses of mixed threshold models with support for REML-type variance components estimation base on the methods of Gianola & Foulley9. Under these models, the respective traits occur as a result of an underlying unobserved phenotype exceeding a given threshold11. The unobserved continuous phenotypes are assumed to be normally distributed. For each trait  a vector, F, of means corresponding to subpopulations determined by combinations of levels of fixed b and random s factors, is modelled as:

 

F = Xb + Zs

 

where F is a vector of underlying means, b  is a vector associated with the effects of year (19 for CMF), year-season (36 for GMS and 26 for CAF), age of dam in years (7 for GMS, 6 for CMF and 5 for CAF) and birth status (3 for all three flocks; only for survival rate), s is a vector of sire effects and X and Z are design matrices. The s effect are assumed to be normally distributed, with E(s) = 0 and Var(s) = Aδ2s, where A is a numerator relationship matrix.

 

Solutions for thresholds and vectors b and s were computed as described by Konstantinov11. The different traits of each of the flocks were analysed separately. The heritability (h2) was estimated by setting the error variance to unity;

 

h2 =  4 x  δ2s / (1 + δ2s)

 

The expression of the solutions for the thresholds, all the effects and the estimated breeding values are in units of residual standard deviations of the underlying scale due to the fact that the underlying scale is unknown.

 

Table 1. Description of the data sets

 

 

 

 

No. of records

 

No. of sires

 

No. of

 

No. of

 

 

 

GMS

 

CMF

 

CAF

 

GMS

 

CMF

 

CAF

 

categories

 

thresholds

 

Fertility

 

8590

 

8777

 

3580

 

193

 

420

 

113

 

2

 

1

 

Fecundity

 

7432

 

6237

 

3186

 

185

 

420

 

108

 

3

 

2

 

LSBa

 

8590

 

8777

 

3580

 

193

 

420

 

113

 

4

 

3

 

LSWa

 

8590

 

8777

 

3580

 

193

 

420

 

113

 

4

 

3

 

Survival rate

 

10210

 

9174

 

4816

 

217

 

493

 

148

 

2

 

1

a - LSB = Litter size at birth and LSW = Litter size at weaning

 

Results

Estimates of sire variances and heritabilities for the different traits are given in Table 2. As expected, the heritability of fecundity in all three flocks was higher than that of fertility. In CMF the heritability for litter size at birth was approximately the same as that of fertility. In GMS and CAF the heritability of fertility was slightly lower than that of litter size at birth. In all three these flocks the heritability for survival rate was approximately zero.

 

Reported heritabilities for fertility, estimated with linear model paternal half-sib analyses, ranged from 0.012 to 0.22 13 for fertility, from -0.12 3 to 0.35 1  for litter size at birth and from 0.00 4 to 0.26 1 for litter size at weaning. The heritability estimates in this study for fertility and litter size at birth and weaning are within the range of previously published heritabilities, although the latter were not estimated with a threshold model.

 

Table 2. Heritability estimates for each trait in the different flocks.

 

 

 

 

GMS

 

CMF

 

CAF

 

Trait

 

Sire Variance

 

Heritability

 

Sire Variance

 

Heritability

 

Sire Variance

 

Heritability

 

Fertility

 

0.01836

 

0.072

 

0.05334

 

0.203

 

0.05342

 

0.20

 

Fecundity

 

0.04527

 

0.173

 

0.08416

 

0.311

 

0.11832

 

0.42

 

LSB

 

0.03374

 

0.131

 

0.05276

 

0.201

 

0.07366

 

0.27

 

LSW

 

0.02373

 

0.092

 

0.04780

 

0.183

 

0.04919

 

0.19

 

Survival rate

 

0.00000

 

0.000

 

0.177*10-4

 

0.468*10-6

 

0.00515

 

0.02

a - LSB = Litter size at birth and LSW = Litter size at weaning


Conclusion

The heritability estimates and the variation in sire breeding values indicate that it is possible to improve reproduction rate, but not survival rate, genetically through selection.

 

References

1.   ABDULKHALIQ, A.M., HARVEY, W.R. & PARKER, C.F., 1989. Genetic parameters for ewe productivity traits in the Columbia, Suffolk and

      Targhee breeds. J. Anim. Sci. 67: 3250-3257

 

2.   ATKINS, K.D., 1986. A genetic analysis of the components of lifetime productivity in Scottish Blackface shheep. Anim. Prod. 1986. 43: 405-419

 

3.   BASTHAKUR, A.K., BURFENING, P.J., VAN HORN, J.L. & BLACKWELL, R.L., 1973. A study of some aspects of lifetime production in Targhee

      and Columbia sheep. J. Anim. Sci. 36: 813-820

 

4CLARKE, S.E. & HOHENBOKEN, W.D., 1983. Estimation of repeatability, heritability and breed differnces for lamb production. J. Anim. Sci. 56:

     309-315

 

5.   DICKERSON, G.E. , 1978. Animal size and efficiency: Basic concepts. Anim. Prod. 27:367

 

6.   ERASMUS, G.J., DE LANGE, A.O. DELPORT, G.J. & OLIVIER, J.J., 1990. Genetic and phenotypic parameter estimates of production traits in

      Merino sheep in an arid environmenrt. S.  Afr.  J.  Anim.  Sci. 20(1):31-34

 

7.   FALCONER, D.S., 1989. Introduction to quantitative genetics. Longman, New York

 

8.   GIANOLA, D., 1982. Theory and analyses of threshold characters. J. Anim. Sci. 54, 1078-1096.

 

9.   GIANOLA, D. & FOULLY, J.L., 1983. Sire evaluation for ordered data with a threshold model. Dgenet.Sel. Evol. 15:201

 

10. HARVEY, W.R., 1990. User=s guide for LSML76 - Mixed model least squares and maximum likelihood computer program. Ohio State University,

      Columbus, USA

 

11. KONSTANTINOV, K.V., ERASMUS, G.J. & VAN WYK, J.B., 1994. Evaluation of Dormer sires for litter size and lamb mortality using a threshold

      model. S. Afr. J. Anim. Sci. 24(4): 119-121

 

12.  KONSTANTINOV, K.V., 1995. GFCAT: User=s guide. University of the Orange Free State, Bloemfontein

 

13MARTIN, T.G., NICHOLSON, D., SMITH, G. & SALES, D.I., 1981. Phenotypic and genetic parameters for reproductive performance in a

       synthetic line of sheep. J. Agric. Sci. 96: 107-113

 

14. OLLIVIER, J.J., 1989. Genetic and environmental trends in the Grootfontein Merino stud. PhD-thesis, University of the Orange Free State,

      Bloemfontein

 

15. SNYMAN, M.A., ERASMUS, G.E. & VAN WYK, J.B., 1995. Non-genetic factors influencing growth and fleece traits in Afrino sheep. S. Afr. J.

     Anim. Sci. 25(3):70-74


 

Published

Proceedings 35th SASAS congress, Nelspruit, 1-3 July, 153-155