Livestock Science 152 (2013) Contents lists available at SciVerse ScienceDirect. Livestock Science

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1 Livestock Science 152 (2013) Contents lists available at SciVerse ScienceDirect Livestock Science journal homepage: Selection strategies for fertility traits of Holstein cows in Iran Heydar Ghiasi a,n, Ardeshir Nejati-Javaremi b, Abbas Pakdel b, Oscar González-Recio c a Department of Agricultural Science, Payame Noor University, Iran b Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran c Departamento de Mejora Genética, Instituto Nacional de Investigaciones Agrarias, Madrid, Spain article info Article history: Received 2 July 2012 Received in revised form 23 November 2012 Accepted 7 December 2012 Keywords: Fertility Genetic improvement Selection strategy Restricted selection index Economic value abstract Efficient reproductive performance is an important prerequisite for sustainable dairy production systems. Moreover, reproductive performance has become one of the most important functional traits in the dairy cattle industry, because of its economic importance, as well as its effect on animal welfare. The objective of present study was to evaluate the consequence of alternative selection strategies to improve fertility performance of Holstein cows in Iran. The traits milk production (M), number of inseminations per conception (INS), days from calving to first insemination (DFS), maternal calving difficulty (MCD) and direct calving difficulty (DCD) were included in an aggregate genotype (H). The selection indices in each strategy were a different combination of fertility traits and milk production. Although fertility traits were included in the aggregate genotype, in the first selection strategy, in which selection was based on milk production, unfavorable genetic gain for fertility traits occurred. In the second strategy, in which genetic gain for milk production was restricted to zero, in comparison to the other strategies, the genetic gains for profit and fertility traits were lowest and highest, respectively. In the third strategy, in which genetic gain for fertility traits was restricted to zero, the genetic gains for milk production and profit differed little compared to the first strategy. Favorable genetic gains were obtained for both fertility traits and milk production in the fourth strategy, in which proportional restriction was used. Although the genetic gain for milk production in this strategy was lower than in the first strategy, genetic gain for profit showed slightly differed. & 2012 Elsevier B.V. All rights reserved. 1. Introduction Profitability of dairy herds depends on production and functional traits. The functional traits are animal characteristics that increase efficiency by reducing production costs (Forabosco, 2005). Improvement of fertility increases net returns through decreasing calving interval, involuntary culling rate, and replacement cost, and by increasing milk production (Bagnato and Oltenacu, 1994). Milk production is the major source of income on dairy n Corresponding author. Tel.: þ addresses: ghiasei@gmail.com, ghiasi@ut.ac.ir (H. Ghiasi). farms. However, because of negative genetic correlations that exist between milk production and functional traits, selection based on milk production alone could increase the cost of production (Rauw et al., 1998; Young, 1970). In the past decades in many breeding programs that are carried out in most countries around the world, the major emphasis has been placed on milk production. However, beginning in 2000, because of quota-based milk marketing systems, price constraints, labor costs and deterioration of the functional traits, most breeding companies around the world shifted their emphasis from milk production alone to include functional traits (Miglior et al., 2005). The dairy cattle population in Iran has undergone a strong selection for milk production. Iranian /$ - see front matter & 2012 Elsevier B.V. All rights reserved.

2 12 H. Ghiasi et al. / Livestock Science 152 (2013) Holstein breeders have been using semen from dairy bulls sourced mainly from North American Holstein sire especially USA and Canada. The fertility performance has not been included in Iranian Holstein population breeding goal and the national genetic evaluations are carried out only for milk production and linear type traits (Sadeghi- Sefidmazgi et al., 2012). The average relative emphasis placed on production and reproduction traits in the selection indices in 2003 were 59.5% and 12.6%, respectively. Across countries, the Danish S-Index placed most emphasis (37%) on health and reproductive performance (Miglior et al., 2005). A previous study (Gonzalez-Recio et al., 2006) reported that it is unlikely to improve fertility by selecting for production and profitability, but genetic degradation of fertility could be decelerated by including fertility in the total merit index. The objective of the present study was to evaluate the consequence of alternative selection strategies to improve the fertility performance of Holstein cows in Iran. 2. Materials and methods 2.1. Alternative selection strategies to improve reproductive performance Different selection strategies were used to improve reproductive performance in the Iranian Holstein dairy cow. Selection indices in these strategies were various combinations of fertility traits and milk production based on selection index and restricted selection index theory (Cunningham et al., 1970; Hazel, 1943). The aggregate genotype was as follows: H ¼ ðv 1 M ÞþðV 2 INSÞþðV 3 DFSÞþðV 4 MCDÞþðV 5 DCDÞ where V i is the economic value of ith trait, M is the milk production, INS is the number of inseminations per conception, DFS is the number of days from calving to first insemination, MCD is the maternal calving difficulty and DCD is the direct calving difficulty. Genetic parameters (Table 1) were estimated using a total of 72,124 records of parities 1 to 6 from 27,113 cows collected from 1981 to 2007 in 15 large Iranian Holstein herds reported by Ghiasi et al. (2011) and Economic values (Table 2) were estimated using 10 Iranian Holstein herds reported by Ghiasi et al. (2011); unpublished doctoral thesis) were used in this study Strategy 1 In this strategy different selection indices were developed based on the aggregate genotype. The selection indices were as follows: I 1 ¼ ðb 1 MÞþðb 2 IFLÞþðb 3 DFSÞ I 2 ¼ b 1 M I 3 ¼ ðb 1 M I 4 ¼ ðb 1 M I 5 ¼ ðb 1 M Þþðb 2 DOÞ Þþðb 2 DFSÞþðb 3 INSÞ Þþðb 2 DFSÞ where b 1, b 2 and b 3 are index weights; IFL is the interval between first and last insemination, DO is the number of days open, DFS, INS and M are defined in the previous section. Selection index weights were derived in the standard way as follows: b ¼ P 1 Gv ð1þ where P is the phenotypic variance covariance matrix between the traits in the selection index, b is the vector of index weights, G is the genetic variance covariance matrix between traits in the selection index and traits in the aggregate genotype and v is the vector of economic values. Table 2 Estimated economic values (US$) for milk production (M), number of inseminations per conception (INS), calving interval (CI), stillbirth (SB) and maternal calving difficulty (MCD) and direct calving difficulty (DCD). Traits M (kg) INS 82 CI (day) 2.08 SB (%) 1.27 DCD 6.63 MCD 2.48 Value economic value ($/cow per year) Table 1 Heritabilities (bold), genetic correlations (above diagonal) and phenotypic correlations (below diagonal) for traits that used in this study. Traits a INS CI DFS IFL DO M DCD MCD INS (0.004) b CI DFS IFL DO M DCD MCD a INS¼number of inseminations to conception, CI¼calving interval, DFS¼days from calving to first service, IFL¼interval between first and last insemination, DO¼days open, M¼305 days milk production, MCD: maternal calving difficulty and DCD: direct calving difficulty. b Standard errors of all estimates were below

3 H. Ghiasi et al. / Livestock Science 152 (2013) Strategy 2 In this strategy selection indices weights were derived based on restricted selection index theory. Response for milk production was restricted to zero. The selection indices used in this strategy were the same as those in strategy 1. Restricted selection indices were derived based on the method presented by Cunningham et al. (1970) as follows: Index weights for the restricted selection indices were derived by adding the jth column of G for the restricted trait j with a zero in the final position as a row and column to P, adding a dummy variable (b d ) to the vector b and a row of zeroes to G as follows: " #" # P G j b G 0 ¼ G ð2þ j 0 0 b d Eq. (2) was solved for b and b d using Eq. (1) Strategy 3 In this strategy, simultaneous multiple restrictions were applied. Selection response on INS and DFS were restricted to zero without restriction on milk production. In this strategy a few selection index restrictions were applied on INS alone, because the new matrix P was not positive definite. Methods and selection indices used in this strategy were the same as those in strategy 2. To obtain the index weights in simultaneous multiple restricted indices, the corresponding row and column of G for each of the restricted traits with a zero in the final position were added to the P and for each of the restricted traits a dummy variable (x i ) and a row of zeroes were added to the vectors b and G, respectively, as follows: P G 1 G n b G ^ ^ U x ^ G 0 n x n 2.5. Strategy ¼ G 0 The objective of this strategy was to improve simultaneously DFS, INS and milk production although the unfavorable genetic correlation exists among these traits. ð3þ In this strategy, proportional restriction indices were derived. Selection index weights were derived so that restrict the genetic gain in some traits in the aggregate genotype to be proportional according to a vector of proportionalities (K). Vector K was obtained so that a ratio of genetic in milk and DFS to genetic gain in INS in strategy 4 to be same proportionality in strategy 1, but with a negative sign (increase in milk and favorable decrease in INS and DFS) as follows. K ¼ DG milk, DG INS, DG DFS DG INS DG INS DG INS where DG milk, DG INS and DG DFS were the genetic gains in milk, INS and DFS, respectively, in strategy 1. The method described by Lin (2005) was used to obtain proportional selection indices. Index weights in the proportional restricted selection indices were obtained as Eq. (3) by adding a vector K with zero in the first and final position to the P as follows: 2 3 P G i... G n G 0 i 0 :: 0 K b x ^ ^ G 0 74 ^ 5 ¼ G 0 n ^ 5 x n 0 K Selection response for a given trait j in the aggregate genotype was predicted as follows: DG j ¼ b0 G pffiffiffiffi j i, s s I ¼ b 0 Pb I where G j is the jth column of G; s I is the standard deviation of the index and i is selection intensity, which equals one. 3. Results and discussion 3.1. Strategy 1 Expected genetic gains in each trait in the breeding objective obtained using different selection indices are presented in Table 3. Based on strategy 1, the genetic gain for milk production was favorable. Although INS and DFS are included in the breeding objective, undesirable genetic gains for fertility traits (INS and DFS) were also obtained in this strategy. The second and fourth selection indices had the highest and lowest genetic gains for milk production, respectively, among the other selection ð4þ Table 3 Selection indices and expected genetic gains in strategy 1. I M IFL 1.53 DFS I M I M 1.7 DO I M DFS INS I M 1.96 DFS

4 14 H. Ghiasi et al. / Livestock Science 152 (2013) indices. However, the decline in fertility performance and profit was the opposite. The observed decline in fertility traits was because of the unfavorable genetic correlation that exists between milk production and fertility traits (INS and DFS). Similar results were reported by Gonzalez- Recio et al. (2006). Although improvement in fertility traits (INS and DFS) cannot be obtained using strategy 1, reduction of fertility performance may be slowed by using this strategy in comparison to the situation in which these traits are not included in the breeding objective. The genetic gains for MCD and DCD in all selection indices in this strategy were the same and favorable Strategy 2 The results obtained for the first strategy indicated that, by increasing milk production, fertility performance will be decreased. One of the solutions to overcome this problem is to restrict the genetic gain for milk production to zero as was applied in the second strategy. Expected genetic gain for the various traits and gain in profit for different selection indices in the second strategy are presented in Table 4. By restricting the genetic gain for milk production to zero, considerable favorable genetic gain for fertility performance, as well as for MCD and DCD, was obtained. In this strategy, the third selection index was the best. In the fourth selection index, which included milk production and DFS, the unfavorable genetic gain was observed for INS. In comparison to the first strategy, the genetic gain in profit was low. In the first strategy, most of expected gain in profit was due to milk production. However, in the second strategy, in which restriction was imposed, the expected genetic gain in profit due to milk was zero. Thus, approximately 98% of the expected gain in profit in the second strategy was due to improvement in fertility performance. The results of the second strategy indicated that, if milk production is at a satisfactory level and fertility is a problem in the herd, this strategy can be used to improve fertility performance without undesirable effects on milk production. In addition, this strategy is suitable in milk production quota systems Strategy 3 The expected genetic gains and selection indices based on strategy 3 are presented in Table 5. In this strategy, the genetic gains for INS and DFS were restricted to zero. The results indicated that favorable genetic gains can be obtained for milk production without undesirable effects on fertility performance traits. Genetic gains for milk production varied among the different selection indices. The fourth and third selection indices had the highest and lowest genetic gains for milk production, respectively. The genetic gains for MCD and DCD were similar for all selection indices. In the fourth selection index, which placed restrictions only on DFS, the unfavorable genetic gain was observed for INS, but in the second index, when restriction was applied only on INS, favorable genetic gains were obtained for DFS. Although the genetic gain for milk production was higher in the first strategy than in strategy 3, fertility performance decreased in strategy 1. However, in the third strategy fertility performance remained at an average level. Except for the first selection index, genetic gains for profit in this strategy were similar to those in the first strategy. Genetic gains obtained for Table 4 Selection indices and expected genetic gains in strategy 2. I M 1.82 IFL 1.53 DFS I M 1.71 DO I M DFS INS I M 1.96 DFS Table 5 Selection indices and expected genetic gains in strategy 3. I M 4.96 IFL 0.43 DFS n I M 2.64 DO I M 2.83 DFS INS n I M 1.78 DFS n Restriction was applied only on INS.

5 H. Ghiasi et al. / Livestock Science 152 (2013) Table 6 Selection indices and expected genetic gains in strategy 4. I M IFL DFS I M 2.93 DO I M 2.57 DFS 106 INS milk production using I 2 in strategy 1 was 116 kg greater than I 2 in strategy 3; however, the genetic gain for profit was greater in the third strategy than in the first strategy. These results indicated that fertility is an economically important trait Strategy 4 The genetic gains expected using strategy 4 are shown in Table 6. The results obtained for the first strategy indicated that it is not possible to improve fertility and milk production simultaneously. However, results for the fourth strategy showed that considerable genetic gains can be achieved in milk production, INS and DFS simultaneously. In this strategy, the third selection index had the largest and the first selection index had the lowest expected genetic gain for milk production and profit, respectively. The first and third selection indices had the highest and lowest genetic gains, respectively, for INS. In those selection indices that included milk production, INS and DFS (the first and fourth strategies), the genetic gain obtained for profit was the same. However, in the fourth strategy favorable genetic gain was obtained for both milk production and fertility, whereas in the first strategy fertility performance declined. In all strategies, those selection indices that contained milk production, DFS and INS were the best selection indices. Moreover, among all strategies, strategy 1 had the largest genetic gain for milk production. However, when using this strategy, fertility performance was reduced. On the other hand, by restricting the genetic gain for milk production to zero in the second strategy, the lowest expected gain for profit was obtained but considerable genetic gains can be obtained for INS and DFS. In order to improve milk production without any side effect to fertility performance, strategy 3 can be used although genetic gain for milk production was lower than in strategy 1 but differences in genetic gains in profit was little. Strategy 4 can be used to improve milk production and fertility simultaneously although the unfavorable genetic correlation exists between milk production and fertility. Milk production and type traits are included in the Iranian Holstein breeding objective and Iranian Herds are using semen from different countries especially from the USA and Canada and there is large variation among Iranian herds according to fertility performance and milk production. Therefore determination of the strategy that is the best depends on the herd conditions. For example, if the average herd milk production is at a satisfactory level and higher milk production is not necessary, the second strategy that restricts milk production gain to zero can be used, because considerable genetic gains can be obtained for INS and DFS. On the other hand, if the fertility performance of the herd is suitable and the objective is to improve milk production without undesirable effects on fertility performance, the third strategy can be used. In those herds where the objective is to improve both milk production and fertility performance simultaneously, the fourth strategy can be applied. In this strategy, the weights for the selection index were derived such that milk production increases and favorable genetic gains for INS and DFS can also be obtained. 4. Conclusion Results of the current study indicated that it is not possible to improve fertility performance in Iranian Holstein cow by selection indices derived using the ordinary selection index theory. Therefore, three alternative strategies were suggested to improve fertility performance that depends on herds conditions and breeding objective in Iran. References Bagnato, A., Oltenacu, P., Phenotypic evaluation of fertility traits and their association with milk production of Italian Friesian cattle1. J. Dairy Sci. 77, Cunningham, E., Moen, R., Gjedrem, T., Restriction of selection indexes. Biometrics, Forabosco, H., Breeding for Longevity in Italian Chianina Cattle. University of Wageningen. Ghiasi, H., Pakdel, A., Nejati-Javaremi, A., Mehrabani-Yeganeh, H., Honarvar, M., Gonzalez-Recio, O., Carabano, M.J., Alenda, R., Genetic variance components for female fertility in Iranian Holstein cows. Livest. Sci. 139, Gonzalez-Recio, O., Alenda, R., Chang, Y., Weigel, K., Gianola, D., Selection for female fertility using censored fertility traits and investigation of the relationship with milk production. J. Dairy Sci. 89, Hazel, L.N., The genetic basis for constructing selection indexes. 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