. however , to solve the simultaneous equations , the genetic parameters ( heritability and genetic correlations ) and phenotypic parameters ( standard deviation and correlations ) among traits must be known . when these traits differ in variability , heritability , and in the correlation among their phenotypes and genotypes , index selection is more effective than independent culling levels or sequential selection , and the construction of an index is not easy without the use of matrix methods , particularly , if there are more than two sources of information , and improves as the number of traits in the selection index increases . dekkers   reported that the selection of both production traits ( protein yield , protein % ) and functional traits ( longevity , milkability , and somatic cell score ) increased the selection index efficiency to 58% . .   found that the selection of milk yield , somatic cell score , udder depth , teat placement , and foot angle improved efficiency of response in the aggregate genotype by 1% to 4% over selection for milk yield alone . sun et al .   reported that for improving milk yield , selection indices comprising milk , fat , or protein yields were 98%100% as efficient as an index comprising all three traits . selection on milk yield alone was 5% less efficient in improving milk yield compared with the selection using an index of all three traits . according to , every country should develop its selection index because the success of selection index from different countries can not be compared , even though breeding goals are very similar . for some time in zimbabwe , the only means of selection of local bulls have been on the basis of pedigree information and visual appraisal , which with no doubt had an adverse on genetic progress . essentially , two of the most heavily used zimbabwean bulls at one time had an average predicted difference of 370  kg milk . therefore , the objectives of this study were to develop a multitrait selection index for jersey cattle in zimbabwe and to test the accuracy and efficiency of the index . the index would give farmers an option of selecting one or more traits at a time , depending on the farmer 's selection goals . the total land area is 390,759  km and it is divided into five agroecological regions . rainfall patterns and crop production progressively deteriorate from region i to v. however , livestock production including dairying is practised in all the regions . in the regions with low rainfall , dairying is assisted by the production of drought resistant fodder crops . most dairy farms are located within 40  km of the major cities and towns . the standard 305-day milk production records of pure bred jersey were obtained from zimbabwe livestock identification trust ( lit ) . multivariate analyses using repeatability animal model and the asreml program developed by   were used . the animal model included fixed effects of herd - year - season , previous calving interval , days dry , linear , and quadratic regression coefficients of age at calving as covariates , permanent environmental , and animal effects . from this , variance components , heritability , predicted breeding values ( ebvs ) , and genetic and phenotypic correlations were estimated . the index was developed in order to allow breeders to select sires and dams for simultaneous improvement of both production and functionality traits . the production component of the index included milk yield ( m ) , fat yield ( f ) , protein yield ( p ) , fat percent ( f% ) , and protein percent ( p% ) , while the functional component included the somatic cell count ( scc ) . the selection index weights were then calculated as 
 ( 2)b = p1g12a , 
 where g12 is an n  m genetic variance - covariance matrix for m traits affecting profitability and n correlated indicator traits ( criteria ) and incorporates the additive genetic relationships between sources of information ; p is a n  n phenotypic ( co)variance matrix of correlated indicator traits ; b is a vector of index weights ( coefficients ) for the phenotypic values of the selection criteria ( traits ) ; a is an n  1 vector which express the relative importance by the breeder to each trait . in this case , as described by , an equal change in standard - deviation units was used for each trait . therefore , the  a  that were assigned to each trait were the reciprocals of the phenotypic standard deviations . as stated above , the optimum set of the selection index coefficients are those which maximise the correlation ( rhi ) or minimise the squared deviation between the selection index and the aggregate genotype ( breeding objective ) . therefore , according to , the accuracy of index selection is a function of the correlation ( rhi ) between the aggregate genotype and the index and was calculated as 
 ( 3)rhi2=i2h2 , 
 where i and h were the variances of the index and the breeding objective , respectively . these variances were calculated as 
 ( 4)i2=b'g12  a,h2=a'g22  a , 
 where a and g12 are as described above ; g22 is the m  m genetic variance - covariance matrix of the m traits in the breeding objective . the selection index developed was 
 ( 5)it=0.0004m+0.0109f+0.0313p+1.0004f%+2.4491p%0.1905scc . these means that animals can then be ranked according to these index values and selection based on these rankings . the positive signs for production traits and negative sign for functionality trait mean that the index developed will allow breeders to select sires and dams , which will be increasing the production traits and decreasing the functionality trait , respectively . the use of scc for selection purposes has been widely discussed because the elevation of scc with an infection is a clear indication of the occurrence of infection as well as of the immunological response to combat the infection . recent studies clearly indicate , however , that the genetic correlation with clinical cases of mastitis is reasonably high and that the relationship is linear . thus , the validity of using scc as an indirect measure to improve mastitis resistance has been strengthened . the variances ( i ) of this index ( it ) and ( h ) of the breeding objectives were computed as 12.23 and 13.43 , respectively . the accuracy of the index was 91.1% , and the correlation with the objective was 0.954 . to test the effect of individual criteria on the efficiency of the index ( it ) , the efficiency of these sub - indices was then compared to the efficiency of the overall index . table 1 clearly shows that most individual criteria have only a small influence on the efficiency of the index . however , when milk yield , fat yield , or protein yield is dropped from the index , the resultant subindices are only 51.7% , 53.3% , or 52.9% accurate , respectively , compared to the 91.1% of the total index with these criteria included . since dropping certain traits has a small influence on the efficiency of the index , the possibility to construct the total index without these traits ( criteria ) was investigated . however , when criteria were dropped from the index , the index weights ( coefficients ) of the remaining criteria changed . for instance when p% or f% was dropped from the index the index weights ( b - values ) for milk yield and scc changed from positive to high negative value and from negative to high positive , value respectively , implying decreased milk yield and increased scc , which is unacceptable . the same thing happened when f% and p% were both dropped at the same time from the index . in this study , a detailed description of the development of a multitrait selection index was presented . although this index was developed specifically for the jersey cattle breed in zimbabwe , the method employed can be used to develop indices for different breeds and different production systems within the same breed . the definition of the breeding objective and correlation structure between traits and criteria is necessary . application of these principles and results is necessary if the dairy cattle industry is to maximise the exploitation of genetics and to improve its relative competitive position .
a multitrait selection index ( it ) for zimbabwean jersey cattle was constructed . 
 the breeding objective was defined in terms of production and functionality traits . 
 the production component of the index included milk yield ( m ) , butterfat yield ( f ) , protein yield ( p ) , butterfat percent ( f% ) , and protein percent ( p% ) , while the functional component included the somatic cell count ( scc ) . 
 the index was termed as it = 0.0004 m + 0.0109f + 0.0313p + 1.0004f% + 2.4491p%  0.1905scc . 
 the accuracy of the index was 91.1% , and the correlation between this index and the aggregate breeding objective was 0.954 . 
 a selection index is more important in the selection of sires and cows . 
 this leads to the greatest genetic progress and hence productivity in the dairy sector . 
 therefore , the application of the selection index developed is necessary if the dairy cattle industry is to maximise the exploitation of genetics and to improve its relative competitive position .