Hindawi Publishing Corporation ISRN Veterinary Science Volume 2013, Article ID 148030, 3 pages http://dx.doi.org/10.1155/2013/148030 Research Article A Proposed Selection Index for Jersey Cattle in Zimbabwe Edward Missanjo, 1 Venancio Imbayarwo-Chikosi, 2 and Tinyiko Halimani 2 1 Malawi College of Forestry and Wildlife, Private Bag 6, Dedza, Malawi 2 Department of Animal Science, Faculty of Agriculture, University of Zimbabwe, P.O. Box MP 167, Mount Pleasant, Harare, Zimbabwe Correspondence should be addressed to Edward Missanjo; edward.em2@gmail.com Received 5 February 2013; Accepted 28 February 2013 Academic Editors: P. Butaye and J. F. Hocquette Copyright © 2013 Edward Missanjo et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A multitrait selection index (  ) for Zimbabwean Jersey cattle was constructed. he breeding objective was deined in terms of production and functionality traits. he production component of the index included milk yield (), butterfat yield (), protein yield (), butterfat percent (%), and protein percent (%), while the functional component included the somatic cell count (SCC). he index was termed as   = 0.0004 + 0.0109 + 0.0313 + 1.0004% + 2.4491% − 0.1905SCC. he 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. his leads to the greatest genetic progress and hence productivity in the dairy sector. herefore, 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. 1. Introduction K¨ onig and Swalve [1] presented a multiple correlation method of constructing optimum selection indexes. However, to solve the simultaneous equations, the genetic parameters (heri- tability and genetic correlations) and phenotypic parameters (standard deviation and correlations) among traits must be known. When these traits difer in variability, heritability, and in the correlation among their phenotypes and genotypes, index selection is more efective than independent culling levels or sequential selection [2], and the construction of an index is not easy without the use of matrix methods, par- ticularly, if there are more than two sources of information, and improves as the number of traits in the selection index increases [3]. Dekkers [4] reported that the selection of both produc- tion traits (protein yield, protein %) and functional traits (longevity, milkability, and somatic cell score) increased the selection index eiciency to 58%. Sørensen et al. [5] found that the selection of milk yield, somatic cell score, udder depth, teat placement, and foot angle improved eiciency of response in the aggregate genotype by 1% to 4% over selection for milk yield alone. Sun et al. [6] reported that for improving milk yield, selection indices comprising milk, fat, or protein yields were 98%–100% as eicient as an index comprising all three traits. Selection on milk yield alone was 5% less eicient in improving milk yield compared with the selection using an index of all three traits. According to [7], every country should develop its selec- tion index because the success of selection index from dif- ferent countries cannot 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 diference of −370 kg milk [8]. herefore, the objectives of this study were to develop a multitrait selec- tion index for Jersey cattle in Zimbabwe and to test the accuracy and eiciency of the index. he index would give farmers an option of selecting one or more traits at a time, depending on the farmer’s selection goals. 2. Materials and Methods 2.1. Environment. Zimbabwe is located in southern Africa in the tropical savannah region. he total land area is