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Livestock Science

Livestock Science 152 (2013) 11–15

1871-14

http://d

n Corr

E-m

ghiasei@

journal homepage: www.elsevier.com/locate/livsci

Selection strategies for fertility traits of Holstein cows in Iran

Heydar Ghiasi a,n, Ardeshir Nejati-Javaremi b, Abbas Pakdel b,Oscar Gonzalez-Recio c

a Department of Agricultural Science, Payame Noor University, Iranb Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iranc Departamento de Mejora Genetica, Instituto Nacional de Investigaciones Agrarias, Madrid, Spain

a r t i c l e i n f o

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

13/$ - see front matter & 2012 Elsevier B.V

x.doi.org/10.1016/j.livsci.2012.12.009

esponding author. Tel.: þ98 9127869073.

ail addresses:

gmail.com, ghiasi@ut.ac.ir (H. Ghiasi).

a b s t r a c t

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 andfunctional traits. The functional traits are animal char-acteristics that increase efficiency by reducing productioncosts (Forabosco, 2005). Improvement of fertilityincreases net returns through decreasing calving interval,involuntary culling rate, and replacement cost, and byincreasing milk production (Bagnato and Oltenacu, 1994).Milk production is the major source of income on dairy

. All rights reserved.

farms. However, because of negative genetic correlationsthat exist between milk production and functional traits,selection based on milk production alone could increasethe cost of production (Rauw et al., 1998; Young, 1970).In the past decades in many breeding programs that arecarried out in most countries around the world, the majoremphasis has been placed on milk production. However,beginning in 2000, because of quota-based milk market-ing systems, price constraints, labor costs and deteriora-tion of the functional traits, most breeding companiesaround the world shifted their emphasis from milkproduction alone to include functional traits (Miglioret al., 2005). The dairy cattle population in Iran hasundergone a strong selection for milk production. Iranian

H. Ghiasi et al. / Livestock Science 152 (2013) 11–1512

Holstein breeders have been using semen from dairy bullssourced mainly from North American Holstein sire espe-cially USA and Canada. The fertility performance has notbeen included in Iranian Holstein population breedinggoal and the national genetic evaluations are carried outonly for milk production and linear type traits (Sadeghi-Sefidmazgi et al., 2012).

The average relative emphasis placed on productionand reproduction traits in the selection indices in 2003were 59.5% and 12.6%, respectively. Across countries, theDanish S-Index placed most emphasis (37%) on health andreproductive performance (Miglior et al., 2005). A pre-vious study (Gonzalez-Recio et al., 2006) reported that itis unlikely to improve fertility by selecting for productionand profitability, but genetic degradation of fertility couldbe decelerated by including fertility in the total meritindex. The objective of the present study was to evaluatethe consequence of alternative selection strategies toimprove the fertility performance of Holstein cows in Iran.

Table 2Estimated 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 Value economic value ($/cow per year)

M (kg) 0.193

INS �82

CI (day) �2.08

SB (%) �1.27

DCD �6.63

MCD �2.48

2. Materials and methods

2.1. Alternative selection strategies to improve reproductive

performance

Different selection strategies were used to improvereproductive performance in the Iranian Holstein dairycow. Selection indices in these strategies were variouscombinations of fertility traits and milk production basedon selection index and restricted selection index theory(Cunningham et al., 1970; Hazel, 1943). The aggregategenotype was as follows:

H¼ V1 �Mð Þþ V2 � INSð Þþ V3 � DFSð Þþ V4 �MCDð Þþ V5 � DCDð Þ

where Vi is the economic value of ith trait, M is the milkproduction, INS is the number of inseminations perconception, DFS is the number of days from calving tofirst insemination, MCD is the maternal calving difficultyand DCD is the direct calving difficulty. Genetic para-meters (Table 1) were estimated using a total of 72,124records of parities 1 to 6 from 27,113 cows collected from1981 to 2007 in 15 large Iranian Holstein herds reportedby Ghiasi et al. (2011) and Economic values (Table 2) wereestimated using 10 Iranian Holstein herds reported by

Table 1Heritabilities (bold), genetic correlations (above diagonal) and phenotypic corr

Traitsa INS CI DFS IF

INS 0.046 (0.004)b 0.69 0.10 0.

CI 0.70 0.074 0.77 0.

DFS �0.09 0.42 0.058 0.

IFL 0.87 0.80 �0.007 0.DO 0.73 0.95 0.45 0.

M 0.12 0.18 0.08 0.

DCD 0.12 0.018 0.15 0.

MCD 0.04 0.012 0.06 0.

a INS¼number of inseminations to conception, CI¼calving interval, DFS¼

insemination, DO¼days open, M¼305 days milk production, MCD: maternal cb Standard errors of all estimates were below 0.0069.

Ghiasi et al. (2011); unpublished doctoral thesis) wereused in this study.

2.2. Strategy 1

In this strategy different selection indices were devel-oped based on the aggregate genotype. The selectionindices were as follows:

I1 ¼ b1 �Mð Þþ b2 � IFLð Þþ b3 � DFSð Þ

I2 ¼ b1 �M

I3 ¼ b1 �Mð Þþ b2 � DOð Þ

I4 ¼ b1 �Mð Þþ b2 � DFSð Þþ b3 � INSð Þ

I5 ¼ b1 �Mð Þþ b2 � DFSð Þ

where b1, b2 and b3 are index weights; IFL is the intervalbetween first and last insemination, DO is the number ofdays open, DFS, INS and M are defined in the previoussection.

Selection index weights were derived in the standardway as follows:

b¼ P�1G v ð1Þ

where P is the phenotypic variance–covariance matrixbetween the traits in the selection index, b is the vector ofindex weights, G is the genetic variance–covariancematrix between traits in the selection index and traits inthe aggregate genotype and v is the vector of economicvalues.

elations (below diagonal) for traits that used in this study.

L DO M DCD MCD

92 0.72 0.19 0.42 0.34

92 0.99 0.37 0.6 0.53

58 0.76 0.17 0.52 0.21

044 0.94 0.28 0.39 0.35

87 0.076 0.24 0.61 0.56

12 0.16 0.32 �0.38 �0.15

13 0.033 0.11 0.041 �0.43

068 0.08 0.08 1 0.012

days from calving to first service, IFL¼ interval between first and last

alving difficulty and DCD: direct calving difficulty.

H. Ghiasi et al. / Livestock Science 152 (2013) 11–15 13

2.3. Strategy 2

In this strategy selection indices weights were derivedbased on restricted selection index theory. Response formilk production was restricted to zero. The selectionindices used in this strategy were the same as those instrategy 1. Restricted selection indices were derived basedon the method presented by Cunningham et al. (1970) asfollows:

Index weights for the restricted selection indices werederived by adding the jth column of G for the restrictedtrait j with a zero in the final position as a row andcolumn to P, adding a dummy variable (bd) to the vector band a row of zeroes to G as follows:

P Gj

G0j 0

" #b

bd

" #¼

G

0

� �½v� ð2Þ

Eq. (2) was solved for b and bd using Eq. (1).

2.4. Strategy 3

In this strategy, simultaneous multiple restrictionswere applied. Selection response on INS and DFS wererestricted to zero without restriction on milk production.In this strategy a few selection index restrictions wereapplied on INS alone, because the new matrix P was notpositive definite. Methods and selection indices used inthis strategy were the same as those in strategy 2. Toobtain the index weights in simultaneous multiplerestricted indices, the corresponding row and column ofG for each of the restricted traits with a zero in the finalposition were added to the P and for each of the restrictedtraits a dummy variable (xi) and a row of zeroes wereadded to the vectors b and G, respectively, as follows:

P G1 � � � Gn

G01^

0 . . .0

^

G0n 0 . . . 0

266664

377775U

b

x1

^xn

26664

37775¼ G

0

� �½v� ð3Þ

2.5. Strategy 4

The objective of this strategy was to improve simulta-neously DFS, INS and milk production although theunfavorable genetic correlation exists among these traits.

Table 3Selection indices and expected genetic gains in strategy 1.

Index Index weightsa Expected gene

M

I1 0.193�M�1.816� IFL�1.53�DFS 435

I2 0.150�M 465

I3 0.193�M�1.7�DO 423

I4 0.193�M�2.101�DFS�73.41� INS 418

I5 0.166�M�1.96�DFS 445

a M¼305 days milk production, INS¼number of inseminations to conceptio

and last insemination, DO¼days open, MCD¼maternal calving difficulty and D

In this strategy, proportional restriction indices werederived. Selection index weights were derived so thatrestrict the genetic gain in some traits in the aggregategenotype to be proportional according to a vector ofproportionalities (K). Vector K was obtained so that aratio of genetic in milk and DFS to genetic gain in INS instrategy 4 to be same proportionality in strategy 1, butwith a negative sign (increase in milk and favorabledecrease in INS and DFS) as follows.

K¼DGmilk

DGINS, �

DGINS

DGINS, �

DGDFS

DGINS

� �

where DGmilk, DGINS and DGDFS were the genetic gains inmilk, INS and DFS, respectively, in strategy 1. The methoddescribed by Lin (2005) was used to obtain proportionalselection indices. Index weights in the proportionalrestricted selection indices were obtained as Eq. (3) byadding a vector K with zero in the first and final positionto the P as follows:

P Gi . . . Gn 0

G0i 0 :: 0 �K

^ . . . . . . . . . ^

G0n 0 . . . 0 ^

0 �K . . . . . . 0

26666664

37777775

b

x1

^xn

26664

37775¼ G

0

� �½v� ð4Þ

Selection response for a given trait j in the aggregategenotype was predicted as follows:

DGj ¼b0 Gj

sIi, sI ¼

ffiffiffiffiffib0

pP b

where Gj is the jth column of G; sI is the standarddeviation of the index and i is selection intensity, whichequals one.

3. Results and discussion

3.1. Strategy 1

Expected genetic gains in each trait in the breedingobjective obtained using different selection indices arepresented in Table 3. Based on strategy 1, the genetic gainfor milk production was favorable. Although INS and DFSare included in the breeding objective, undesirablegenetic gains for fertility traits (INS and DFS) were alsoobtained in this strategy. The second and fourth selectionindices had the highest and lowest genetic gains for milkproduction, respectively, among the other selection

tic gain

INS DFS MCD DCD $

0.126 0.55 �0.03 �0.11 74

0.172 3.69 �0.014 �0.079 70

0.076 0.173 �0.03 �0.11 76

0.039 0.518 �0.025 �0.11 77

0.171 0.551 �0.019 �0.092 73

n, DFS¼days from calving to the first service, IFL¼ interval between first

CD¼direct calving difficulty.

H. Ghiasi et al. / Livestock Science 152 (2013) 11–1514

indices. However, the decline in fertility performance andprofit was the opposite. The observed decline in fertilitytraits was because of the unfavorable genetic correlationthat exists between milk production and fertility traits(INS and DFS). Similar results were reported by Gonzalez-Recio et al. (2006). Although improvement in fertilitytraits (INS and DFS) cannot be obtained using strategy 1,reduction of fertility performance may be slowed by usingthis strategy in comparison to the situation in which thesetraits are not included in the breeding objective. Thegenetic gains for MCD and DCD in all selection indicesin this strategy were the same and favorable.

3.2. Strategy 2

The results obtained for the first strategy indicatedthat, by increasing milk production, fertility performancewill be decreased. One of the solutions to overcome thisproblem is to restrict the genetic gain for milk productionto zero as was applied in the second strategy. Expectedgenetic gain for the various traits and gain in profit fordifferent selection indices in the second strategy arepresented in Table 4. By restricting the genetic gain formilk production to zero, considerable favorable geneticgain for fertility performance, as well as for MCD and DCD,was obtained. In this strategy, the third selection indexwas the best. In the fourth selection index, which includedmilk production and DFS, the unfavorable genetic gainwas 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 produc-tion. However, in the second strategy, in which restrictionwas imposed, the expected genetic gain in profit due to

Table 4Selection indices and expected genetic gains in strategy 2.

Index Index weightsa Expected genet

M INS

I1 0.04�M�1.82� IFL�1.53�DFS 0 �0

I2 0.042�M�1.71�DO 0 �0

I3 0.044�M�2.123�DFS�73.41� INS 0 �0

I4 0.015�M�1.96�DFS 0 0

a M¼305 days milk production, INS¼number of inseminations to conceptio

and last insemination, DO¼days open, MCD¼maternal calving difficulty and D

Table 5Selection indices and expected genetic gains in strategy 3.

Index Index weightsa Expected genet

M I

I1 0.188�M�4.96� IFL�0.43�DFS 336 0

I2n 0.184�M�2.64�DO 349 0

I3 0.195�M�2.83�DFS�91.72� INS 395 0

I4n 0.194�M�1.78�DFS 419 0

n Restriction was applied only on INS.a M¼305 days milk production, INS¼number of inseminations to conceptio

and last insemination, DO¼days open, MCD¼maternal calving difficulty and D

milk was zero. Thus, approximately 98% of the expectedgain in profit in the second strategy was due to improve-ment in fertility performance. The results of the secondstrategy indicated that, if milk production is at a satisfac-tory level and fertility is a problem in the herd, thisstrategy can be used to improve fertility performancewithout undesirable effects on milk production. In addi-tion, this strategy is suitable in milk production quotasystems.

3.3. Strategy 3

The expected genetic gains and selection indices basedon strategy 3 are presented in Table 5. In this strategy, thegenetic gains for INS and DFS were restricted to zero. Theresults indicated that favorable genetic gains can beobtained for milk production without undesirable effectson fertility performance traits. Genetic gains for milkproduction varied among the different selection indices.The fourth and third selection indices had the highest andlowest genetic gains for milk production, respectively. Thegenetic gains for MCD and DCD were similar for allselection indices. In the fourth selection index, whichplaced restrictions only on DFS, the unfavorable geneticgain was observed for INS, but in the second index, whenrestriction was applied only on INS, favorable geneticgains were obtained for DFS. Although the genetic gainfor milk production was higher in the first strategy than instrategy 3, fertility performance decreased in strategy 1.However, in the third strategy fertility performanceremained at an average level. Except for the first selectionindex, genetic gains for profit in this strategy were similarto those in the first strategy. Genetic gains obtained for

ic gain

DFS MCD DCD $

.103 �8.43 �0.045 �0.10 25.7

.197 �7.85 �0.037 �0.091 31.2

.269 �6.5 �0.028 �0.089 33

.023 �10.5 �0.016 �0.058 20

n, DFS¼days from calving to the first service, IFL¼ interval between first

CD¼direct calving difficulty.

ic gain

NS DFS MCD DCD $

0 �0.048 �0.134 65

�2.37 �0.036 �0.120 73

0 �0.027 �0.114 77

.071 0 �0.029 �0.111 76

n, DFS¼days from calving to the first service, IFL¼ interval between first

CD¼direct calving difficulty.

Table 6Selection indices and expected genetic gains in strategy 4.

Index Index weightsa Expected genetic gain

M INS DFS MCD DCD $

I1 0.147�M�5.248� IFL�0.375�DFS 226 �0.075 �1.43 �0.05 �0.130 52

I2 0.162�M�2.93�DO 283 �0.05 �3.95 �0.039 �0.121 67

I3 0.165�M�2.57�DFS�106� INS 372 �0.034 �0.451 �0.029 �0.117 76

a M¼305 days milk production, INS¼number of inseminations to conception, DFS¼days from calving to the first service, IFL¼ interval between first

and last insemination, DO¼days open, MCD¼maternal calving difficulty and DCD¼direct calving difficulty.

H. Ghiasi et al. / Livestock Science 152 (2013) 11–15 15

milk production using I2 in strategy 1 was 116 kg greaterthan I2 in strategy 3; however, the genetic gain for profitwas greater in the third strategy than in the first strategy.These results indicated that fertility is an economicallyimportant trait.

3.4. Strategy 4

The genetic gains expected using strategy 4 are shownin Table 6. The results obtained for the first strategyindicated that it is not possible to improve fertility andmilk production simultaneously. However, results for thefourth strategy showed that considerable genetic gainscan be achieved in milk production, INS and DFS simulta-neously. In this strategy, the third selection index had thelargest and the first selection index had the lowestexpected genetic gain for milk production and profit,respectively. The first and third selection indices had thehighest and lowest genetic gains, respectively, for INS. Inthose selection indices that included milk production, INSand DFS (the first and fourth strategies), the genetic gainobtained for profit was the same. However, in the fourthstrategy favorable genetic gain was obtained for both milkproduction and fertility, whereas in the first strategyfertility performance declined.

In all strategies, those selection indices that containedmilk production, DFS and INS were the best selectionindices. Moreover, among all strategies, strategy 1 had thelargest genetic gain for milk production. However, whenusing this strategy, fertility performance was reduced. Onthe other hand, by restricting the genetic gain for milkproduction to zero in the second strategy, the lowestexpected gain for profit was obtained but considerablegenetic gains can be obtained for INS and DFS. In order toimprove milk production without any side effect tofertility performance, strategy 3 can be used althoughgenetic gain for milk production was lower than instrategy 1 but differences in genetic gains in profit waslittle. Strategy 4 can be used to improve milk productionand fertility simultaneously although the unfavorablegenetic correlation exists between milk production andfertility. Milk production and type traits are included inthe Iranian Holstein breeding objective and Iranian Herdsare using semen from different countries especially fromthe USA and Canada and there is large variation amongIranian herds according to fertility performance and milkproduction. Therefore determination of the strategy thatis the best depends on the herd conditions. For example, ifthe average herd milk production is at a satisfactory level

and higher milk production is not necessary, the secondstrategy that restricts milk production gain to zero can beused, because considerable genetic gains can be obtainedfor INS and DFS. On the other hand, if the fertilityperformance of the herd is suitable and the objective isto improve milk production without undesirable effectson fertility performance, the third strategy can be used. Inthose herds where the objective is to improve both milkproduction and fertility performance simultaneously, thefourth strategy can be applied. In this strategy, theweights for the selection index were derived such thatmilk production increases and favorable genetic gains forINS and DFS can also be obtained.

4. Conclusion

Results of the current study indicated that it is notpossible to improve fertility performance in Iranian Holsteincow by selection indices derived using the ordinary selec-tion index theory. Therefore, three alternative strategieswere suggested to improve fertility performance thatdepends on herds conditions and breeding objective in Iran.

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