Accepted Manuscript

Calves sex ratio in naturally and artificially bred cattle in central Ethiopia

E. Kefena, M. Alemayehu, A. Yohannis, S. Temesgen, T. Yahualaeshet

PII: S0093-691X(14)00208-8

DOI: 10.1016/j.theriogenology.2014.04.027

Reference: THE 12793

To appear in: Theriogenology

Received Date: 27 February 2014

Revised Date: 29 April 2014

Accepted Date: 29 April 2014

Please cite this article as: Kefena E, Alemayehu M, Yohannis A, Temesgen S, Yahualaeshet T, Calvessex ratio in naturally and artificially bred cattle in central Ethiopia, Theriogenology (2014), doi: 10.1016/j.theriogenology.2014.04.027.

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Calves sex ratio in naturally and artificially bred cattle in central Ethiopia

E. Kefenaa*

, M. Alemayehua, A. Yohannis

a, S. Temesgen

b, T. Yahualaeshet

b

a Ethiopian Institute of Agricultural Research, Holetta Agricultural Research Center, Holetta,

Ethiopia

b College of Veterinary Medicine, Nursing and Allied Health (CVMNAH), Department of

Pathobiology, Tuskegee University, USA

*Address for correspondence: Dr. Kefena Effa, Ethiopian Institute of Agricultural Research,

Holetta Agricultural Research Center, P.O. Box 2003, Addis Ababa, Ethiopia

E-mail: kefenaol@yahoo.com

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REVISED 1

1. Introduction 2

Probability and evolutionary equilibrium theories indicate that the secondary sex ratio (SSR), 3

which is defined as the ratio of male to female offspring at birth, should be 50:50 [1]. However, 4

there is compelling evidence that, in non-human species, many intrinsic and extrinsic factors can 5

significantly affect this universally accepted theory. Moreover, under certain circumstances, 6

theories are inconsistent with results. For instances, an extensive review made by Rosenfeld and 7

Roberts [2] and Demüral et al. [3] and references therein unanimously concluded that among 8

others factors, liter size, mother’s age, mother’s parity, sex of the preceding calf, year, breeding 9

season, mother’s fecundity, maternal nutrition, mother’s milk yield, maternal stress, habitat 10

quality, population demography, maternal dominance, paternal breeding success, time and type 11

of insemination can significantly affect calf sex ratio. 12

13

Some reports also associate male to female sex ratio to the proportion of X and Y-bearing 14

spermatozoa. For instances, Gutierrez-Adan et al. [4] provided strong evidences that the 15

proportion of X and Y- bearing spermatozoa can significantly affect male to female sex ratio. 16

They confirmed that the differential ability of X or Y- bearing spermatozoa to fertilize oocyte 17

depends either on the time of insemination or oocyte maturation state and the intrinsic 18

differences in the physiological activity of X or Y- bearing spermatozoa before fertilization. 19

20

An interesting theory that attracted several scholars into sex ratio investigations was the Trivers 21

and Willard [5] sex ratio allocation theory. They stated that in species in which reproductive 22

success varies more among one sex than the other, mothers in better physiological conditions 23

would give birth to more male calves. Such phenomenon has been reported in several species, 24

including human race [2]. The theory suggests that high-status individuals invest more in boys, 25

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and low-status individuals invest more in girls. In other words, mothers that experienced severed 1

environmental shocks and poor in body conditions give birth to female calves. Many research 2

results [1] conform to the theory of Trivers and Willard [5] suggesting that environmental 3

calamities can modify male to female sex ratio at birth. Indeed, there are multitudes of intrinsic 4

and extrinsic factors that potentially influence male to female sex ratio in large ruminants, 5

rodents, marsupial, primates and others species [2]. 6

7

Several studies that have been undertaken on calf sex ratio in cattle have variable and in some 8

cases conflicting outcomes. For instances, Berry and Cromie [6] reported that AI increases the 9

probability of male calves born in of beef cattle. On the contrary, Tadesse [7] observed that AI 10

didn’t significantly affect calf sex ratio. Perhaps, results of the reports may vary depending on 11

the analytical model used, size and quality of the data and many other factors. Whatever the case 12

is, the choice of sex of calves born at a farm primarily depends up on the production objective of 13

producers. Berry and Cromie [6] indicated that beef producers need more male calves for 14

increased growth rate and more efficient production of lean meat for economic reasons. On the 15

other hand, dairy farmers generally like to have female calves for replacement purposes or 16

lucrative sale while male calves entail increased production costs. Nowadays, breakthroughs in 17

reproductive biotechnologies like semen sexing enables dairy producers in technologically 18

advanced counties to obtain a desired calf sex. However this intervention seems expensive and is 19

not within the reach of smallholder dairy farmers that operate in the low-input low-output dairy 20

production systems. 21

22

In the tropics, the use of artificial insemination (AI) has already registered remarkable success 23

and likely to expand further for dairy cattle genetic improvement programs in the future. 24

Ethiopia, among many Sub-Sahara African countries has been engaged in delivering AI service 25

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to dairy farmers since long time. Despite its long history, most smallholder farmers are skeptical 1

towards AI services perceiving it to have calf sex bias. They complain that AI often results in 2

higher number of male calves. Such concerns need to be addressed by producing objective 3

evidences before large scale application of AI services in the country. Therefore, this study was 4

undertaken to identify some intrinsic and extrinsic factors that can affect calf sex ratio in 5

naturally and artificially bred cattle in Ethiopia. 6

7

2. Material and methods 8

9

2.1. Breeding plan and animal management 10

11

The dairy cattle crossbreeding and management experiments carried out at Holetta Agricultural 12

Research Center mimics the management practices implemented by smallholder dairy producers 13

in Ethiopia. All the cows were subjected to similar management practices regardless of their 14

breed type and level of exotic gene inheritances. They graze on natural pastures for 8 hours a day 15

during the day time. Up on return to the barn, they were provided with hay conserved from the 16

natural pasture. Milking cows were provided with an approximately 1-1.5 kg of concentrate 17

ration during milking. Moreover all the animals are supplemented with approximately 1-2 kg of 18

concentrate feeds during dry seasons of the year when feed supply is scarce. 19

20

Cows in estrus were detected by teaser bulls that run after the herd for 24 hours and herd 21

attendants. The assigned herd attendant recorded the date and time of onset of estrus and the 22

collected data were kept in the central dairy database system to arrange mating plan or 23

insemination. Cow services followed standard breeding procedures. Cows that showed estrus in 24

the afternoon or during the evening were bred the next day in the morning. On the contrary, cows 25

that showed estrus during the morning time were bred on the same day in the afternoon. Base 26

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dam populations that used in the crossbreeding program were selected from two well-known 1

local breeds known as Boran and Horro. Semen from three selected exotic sire breeds (Holstein 2

Friesian, Jersey and Simmental) was imported from temperate regions to crossbred with the 3

selected local dams. In most cases, local cows were artificially inseminated with imported semen 4

to produce first generation (F1) crossbred calves. Bull services were seldom used in case of 5

repeat breeders. First generation (F1) heifers/cows were inter se mated to selected F1 bulls to 6

produce second filial generations (F2) crossbred calves. Furthermore, crossbred animals with 7

higher level of exotic gene inheritances such as 75% exotic: 25% local were produced by 8

backcrossing F1 or F2 heifers/cows to pure exotic semen. 9

10

2.2. Data extraction and editing 11

12

Data on calf sex, service sire and dam breed, estrus and service time, service type, service date, 13

season and year of service and parity number of the dam were extracted from dairy database 14

platform established at Holetta Agricultural Research Center, central Ethiopia. The data used in 15

this study span over the years between 1974 and 2013. Calves with no identified sire were 16

removed as were records coded as abortions or still births. Records with no information on dam 17

parity and twin births were also excluded. Seasons of mating/service were obtained from the 18

effective mating/service date. Parity number of the cows were recorded from 1-8 and those 19

parities greater than 8 were merged together and denoted as 9+.The final dataset consisted of a 20

total of 4657 calving events. 21

22

A dichotomous variable was generated for service type, estrus time, calf sex and time of 23

insemination (Table 1). Service type received a value 0 for AI, otherwise 1 indicating natural 24

mating. Time of estrus detection was given a value 0 if estrus time was in late afternoon or 25

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during the evening, otherwise 1 indicating morning estrus and breeding on the same day in the 1

afternoon. Service time was given a value 0 if it was in the morning, otherwise 1 indicating 2

afternoon breeding. The most important and biologically plausible two-way interaction effects 3

such as genotype x estrus season interaction were found significant and included in the final 4

model. Four major season of the year were identified in the study area: kiremt or rainy season 5

(June through August), tsedey or spring season (September through November), bega or dry 6

season (December through February) and belg or short rainy season (March through May) were 7

used as additional explanatory variables. Summary statistics showing number of observations of 8

estrus by genotype and season is depicted in Table 2. 9

10

Table 1 here 11

Table 2 here 12

2.3. Statistical analysis 13

The probability of a female calf being born was modeled using generalized linear model in 14

PROC GENMOD of SAS [8] by fitting logit as a nonlinear link function. Initially, we performed 15

a series of univariate analyses where each effect was individually included in the model of 16

analysis to identify the effects that significantly affect calf sex ratio. Significance was based on 17

the Generalized Estimating Equation (GEE) scores statistic. 18

19

A probability of a female calf being born, P(X), was estimated using the solution from the 20

multiple regression models as: 21

( ) ( ) 1

1

ˆ( ˆ1−

−− ∑++= m

i ii XeXP βα 22

23

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Where P (X) is the probability (mean) of getting a female calf, α̂ is the predicted intercept of the 1

model, β̂ the predicted regression coefficient for independent variable i and Xi is the associated 2

design matrix for variable i. In logistic regression, probability of an event is equal to mean of 3

dependent variables. Odds ratios were calculated as the exponent of the model solutions. An 4

odds ratio compares opposing probabilities to determine the more likely result for a given 5

outcome; in this instance the outcome was the probability of a female calf. Initially, parity of the 6

cow was treated as quantitative variable to calculate the odds ratio as parity advances from 1 to 7

9+. In the second analysis, parity itself was considered as explanatory variable. With ln 8

representing the natural logarithm and P the probability of being a female calf, the odds ratio is 9

calculated using the following formula. 10

( ) ( )

−==

P

PPitodds

1lnloglog 11

A measure of deviation between the estimated and observed values for PROC GENMOD, known 12

as the deviance and was calculated as: 13

−−

−+

= ∑

iii

ii

ii

i

i i pnn

ynyini

pn

yyD log)(log{2 Where; 14

i=1, ….., number of observations 15

yi= number of female calves from a total of ni births for a given categorical variable 16

pi= the estimated probability of success for female calve for birth i. 17

18

Except estrus and service times, all the effects considered in this study were found to have 19

significant effect on calf sex ratio. This procedure was followed by multivariable logistic 20

regression model by allowing possible biologically plausible two-way interaction effects in the 21

model using maximum likelihood method of the LOGESTIC procedure of SAS [8]. The default 22

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forward selection model algorithm was invoked to develop the most parsimonious multiple 1

regression model. The threshold significance levels for entry and staying in the model were 2

p<0.05 and 0.1, respectively. Effects that were included in the multivariable logistic regression 3

analysis were genotype of the cows, parity of the cows, season of estrus, time of estrus, time of 4

insemination and type of insemination. 5

6

3. Results 7

Factors that presumed to influence calf sex ratio in naturally and artificially bred cattle are 8

presented in Table 3. The result showed that genotype of the cow, estrus season, parity, service 9

type and breed by estrus season interaction significantly affected sex ratio in naturally and 10

artificially bred cattle in central Ethiopia. On the contrary, estrus and service time didn’t 11

significantly affect sex ratio (χ2= 0.83 and 0.79, respectively). On the other hand, interaction 12

effect between genotype and season of estrus highly affected calf sex ratio (χ2=44.36). 13

14

Table 3 here 15

16

The primary focus of this study was to test whether the types of breeding (natural mating vs AI) 17

significantly affect calves sex ratio in in cattle in Ethiopia. Interestingly, the result showed that 18

AI didn’t significantly affect calf sex ratio from the expected 50:50 sex ratio (Table 4). However, 19

natural service was found to have significant effect on calf sex ratio. The odds ratio of 1.383 20

obtained in natural mating vs AI indicated that natural mating skewed towards producing more 21

female calves than AI. In other words, there was high likelihood (38.3%) of getting female 22

calves in naturally mated heifers/cows than those heifers/cows bred through AI. 23

24

Table 4 here 25

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Among all genetic groups of cows, Horro cows had the highest likelihood of giving birth to 1

female calves (χ2 =19.18) followed by crosses with 75% Friesian gene inheritance (χ2=9.83) and 2

F1 Friesian x Boran crossbred cows (χ2=6.18) (Table 4). Differences in the least squares means 3

showed that (data not shown), there were no differences in the calf sex ratio to heifers/cows that 4

showed estrus in rainy and spring seasons. However, significant calf sex ratio variation was 5

obtained between heifers/cows that showed estrus in the rainy and spring seasons versus dry and 6

short rainy seasons. Heifers/cows that showed estrus in the rainy and first month of spring season 7

produced more female calves than heifers/cows that showed estrus in the dry and short rainy 8

seasons. 9

10

However, estrus and service times didn’t influence calf sex ratio in any way. Briefly, this can 11

further be explained as follows: whether the heifers/cows showed estrus in evening or during the 12

day time, calves sex ratio didn’t deviate from the theoretically accepted vale. But, the interaction 13

effect between genotype and season of estrus significantly affected calf sex ratio as shown in 14

Table 3 (χ2=44.36). 15

16

The logit probability estimates of female calves born for genotype, parity, estrus season, estrus 17

time, service time and service type are depicted in Table 5. The ratio of female to male calves 18

varies from nearly 50:50 in F1 exotic x Horro and native Boran cows to 67.3:32.7 in native Horro 19

cows. Young heifers as well as cows in the second, fourth and fifth parities tended to produce 20

more female calves. We noticed that both estrus and service time skewed towards producing 21

female calves, but it must be noted that none of the effects differed between themselves on 22

outcome of calf sex ratio. 23

24

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Contrary to the negative perceptions by smallholder dairy producers in Ethiopia, our data clearly 1

showed that AI didn’t significantly affect calf sex ratio in artificially bred cattle in Ethiopia. In 2

this study, the female to male calf sex ratio obtained through of AI (50.4: 49.6) is strongly agreed 3

with the theoretically accepted value (50:50). Nevertheless, the female to male calf sex ratio in 4

naturally mated heifers/cows showed a significant deviation (χ2 <0.01) from the theoretically 5

accepted ratio. Natural mating appeared to produce more female than male calves (56.8:43.2, 6

respectively) (Table 5). The breed by estrus season interaction effect showed that Horro and F1 7

F x Bo heifers/ cows produced significantly more number of female calves when they showed 8

estrus during the rainy and first month of spring season. In Ethiopia, these months are 9

characterized by critical shortage of feed and other environmental calamities that seriously affect 10

dairy production. 11

12

Both univariate and multiple logistic regression models were concordant in identifying the 13

factors that affect sex ratio in both naturally and artificially bred cattle. Among all genotypes 14

considered in this study, only local Horro (OR= 2.64; CL= 1.49-4.67) and high-grade Friesian 15

cows (OR=1.21; CL 0.80-1.82) had higher odds of calving female calves when compared against 16

a reference breed (three breed crosses). The rest of the cows had odds ratio of less than unity. 17

Heifer/cows bred during the rainy and first month of spring seasons had also higher odds of 18

giving female calves (OR=1.07 and 1.26, respectively) as compared to the reference service 19

season (autumn). We considered the ninth parity as a reference parity to compare all the others 20

against it. The results showed that odds ratio value was greater than unity up until the 6th parity 21

and turned to be less than unity beyond the 6th parity. Heifers had the highest odds ratio of 22

calving female calves (OR=1.21; CL=0.82-1.77) as compared to reference parity. 23

24

Table 5 here 25

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4 Discussion 1

4.1. The effect of genetic groups 2

No doubt that AI is one of the oldest known assisted reproductive biotechnology tools that have 3

still been used worldwide in revolutionizing livestock genetic improvement programs. To our 4

knowledge, the data used in the present study is relatively large enough to identify some intrinsic 5

and extrinsic factors that affect calf sex ratio in naturally and artificially bred cattle in Ethiopia. 6

This study was carried with the core objective of providing evidence-based report to smallholder 7

dairy farmers who frequently complain that AI had calf sex ratio bias towards male calves. 8

9

Several previous studies that compared various breeds for calf sex ratio had mixed outcomes. For 10

instances, Berry and Cromie [6] reported the presence of remarkable differences between dairy 11

and beef type breeds in terms of calf sex ratio. They reported that beef type breeds produced 12

more male calves as compared to dairy type breeds. Nevertheless, the underpinning genetic 13

background is not clearly known. In our study, we found that Horro local cows and crosses with 14

higher proportion of Friesian gene inheritance (75% Friesian) produced more female calves as 15

compared to other dam genotypes considered in this study. One anonymous study suggests that, 16

unlike beef type breeds, dairy type breeds skewed towards producing more female calves. Berry 17

and Cromie [6] also noted that insemination from semen of beef type sire produce more male and 18

lesser female calves. With our present report, however, it is pointless and probably leading to 19

erroneous conclusions to implicate to a single reason the birth of more female calves to Horro 20

and high-grade Friesian cows. Such apparent differences probably occurred by chance and/or 21

attributed to insufficient sample size used in this study. 22

4.2. The effect of estrus season 23

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In the case of cattle, the influence of season on calf sex ratio has also been amply reported in 1

several studies [1]. In Ethiopia, the rainy months and the first month of spring are characterized 2

by critical feed scarcity both in terms of quantity and quality. Calving of more female calves to 3

cows that showed estrus during this seasons of the year is in agreement with the theory of Trivers 4

and Willard [5] who suggested that organisms that experienced negative energy balance or face 5

environmental shocks tend to give more births to female offspring. According to their theory, 6

gravid females subjected to environmental stresses spontaneously abort weak male embryos. 7

Aborting weak male embryos and fetuses increases the chances of grandchildren because, during 8

times of environmental stress, weak sons produce fewer offspring than weak daughters. Aborting 9

a weak male fetus presumably allows the mother to begin a new gestation that might yield either 10

a daughter or a more robust son [5]. Several experiments carried out on various species including 11

mice [2], human races [9] and dairy cattle [1] are in agreement with this theory. In dairy cattle 12

Roche and colleagues [1] reported that cows with negative energy balance and poor 13

physiological strength give more births to female calves than cows with positive energy balance. 14

In other studies, high-energy/carbohydrate feed produce more male offspring in roe deer [10] and 15

mice [2] than those that were grouped under low-energy diet. Based on these comprehensive 16

evidences, it is not surprising to observe more female calves from cows that showed estrus and 17

bred during the bad seasons of the year. 18

4.3. The effect of estrus and service times 19

Demüral et al. [3] followed a carefully designed experiment in Turkey and observed that 20

different times of post-estrus inseminations do not affect calves sex ratio. For instances, they 21

carried out insemination 6, 9, 12 and 15 hours after onset of estrus and didn’t observe differences 22

in calf sex ratio. Foote [11] also observed that time of insemination within the voluntary post-23

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estrus waiting period doesn’t affect calf sex ratio in dairy cattle. Our study also produced the 1

same result indicating that estrus and service time didn’t affect calf sex ratio. Therefore, dairy 2

farmers should not worry about morning or afternoon estrus and subsequent breeding times as far 3

as post-estrus voluntary waiting period is strictly followed. 4

5

4.4. The effect breeding methods 6

Many research results show that calf sex ratio skews towards female calf in naturally bred cattle 7

[7]. However, sex ratio in artificially bred cattle has mixed and in some cases controversial 8

outcomes. Berry and Cromie [6] showed that AI increases the probability of male calves in pure 9

dairy and beef cattle and buffaloes [12]. On the contrary, however, Tadesse [7] reported that AI 10

doesn’t change calf sex ration in dairy cattle. In this particular report, it is interesting to observe 11

that AI didn’t change calf sex ratio from the established 50:50 proportion regardless of the 12

negative perception by smallholder farmers in Ethiopia. Farmers’ complaint probably attributed 13

to insufficient sample size under their particular scenario and/or eagerness of the farmers to get 14

more female calves for economic reasons. In reality, however, AI didn’t change calf sex ratio 15

from the established 50:50 percent. 16

17

Some studies show that breeding bulls differ in the proportion of their X and Y-chromosomes 18

bearing spermatozoa. This obviously results in variation of calf sex ratio. For instances, Washbun 19

((http://www.cals.ncsu.edu/an_sci/extension/animal/repro/spw97-1.htm) indicated that calf sex 20

ratio has strong association with proportion of X- and Y-bearing spermatozoa of particular 21

ejaculate regardless of the types of breeding. Other report [13] indicated that the site of sperm 22

deposition in the female’s reproductive tracts can also influence calf sex ratio. Washbun 23

(http://www.cals.ncsu.edu/an_sci/extension/animal/repro/spw97-1.htm) used fluorescent DNA 24

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image analysis techniques to compare the proportion of X and Y-chromosomes from 10 bulls to 1

a standard preparation containing 50% Y-bearing spermatozoa. The analysis revealed a range in 2

percentages of Y-bearing spermatozoa from as low as 24% to as high as 84% for the 20 3

individual ejaculates. Thus, bulls that possess higher Y-chromosome bearing spermatozoa will 4

result in the birth of more male calves. In another study, Zobel et al. [13] carried out an 5

experiment to test two groups of semen deposition sites in an AI program. In the first group of 6

cows, semen was deposited in the uterine horns while in the second group semen was deposited 7

deeper into a uterine horn with a dominant follicle. The experimental result showed that 21% 8

more male calves were born to uterine horn deposited semen and 18% more females were born 9

to semen deposited deeper in the uterine horns with dominant follicle. Indeed, the authors 10

attributed the differences in calf sex ratio to differences in the timing of capacitation sex 11

chromosome bearing spermatozoa, motility and differential survival time. In fact, X chromosome 12

is bigger than Y chromosome and size differences may affect some of the physiological changes 13

that should occur before fertilization. But the underlying biological mechanism of that 14

physiological process is beyond the scope of this study. 15

16

5 Conclusion 17

Our study suggests that calf sex ratio is affected by multitudes intrinsic and extrinsic factors and 18

interactions of some factors. However, two critically important factors need considerable 19

attention. Firstly unlike natural mating, AI doesn’t affect calf sex ratio as widely perceived by 20

smallholder dairy producers in Ethiopia. Therefore, dairy farmers who started using AI to 21

improve their dairy genetics and those who have the tendency to use it in the future should not 22

bother about the effect of AI on calf sex ratio. Based on this hard evidence, AI should be 23

considered as one of the means that bring rapid genetic changes of the dairy sub-sector in 24

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Ethiopia. Secondly, cows that show estrus during the harsh seasons of the years produce more 1

female calves. Therefore, it should be noted that calf sex ratio can be modified by manipulating 2

management practices to standard level, particularly during harsh seasons of the year. 3

4

Acknowledgements 5

The first author is grateful to the Ethiopian Institute of Agricultural Research (EIAR) and its 6

dairy cattle research staff at Holetta Agricultural Research Center for their unreserved 7

commitments during long-term data collection and establishment an excellent database platforms 8

at the research center. I am also grateful to the staff of College of Veterinary Medicine, Nursing 9

and Allied Health (CVMNAH), Department of Pathobiology, Tuskegee University, USA for 10

their constructive comments and suggestion during the preparation of this manuscript. I also 11

extend my thanks to USDA for providing me Borlaug Fellowship Program during the synthesis 12

of this article at Tuskegee University. 13

14

6 References 15

[1] Roche JR, Lee JM, Berry DP. Climatic factors and secondary sex ratio in dairy cows. J Dairy 16

Sci. 2006; 89(8):3221-7. 17

[2] Rosenfeld CS, Roberts RM. Maternal diet and other factors affecting offspring sex ratio: a 18

review. Bio. Repro 2004; 71, 1063–70. 19

[3]. Demüral O, Mustafa N, Abay M, Beky T. The effect of artificial insemination timing on the 20

sex ratio of offspring and fertility in dairy cows. Turk. J. Vet. Anim. Sci 2007; 31(1): 21-4 21

22

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[4] Gutierrez-Adan A, Garnelo P, Granados J, Garde JJ, Pe´rez-Guzman M, Pintado B, De La 1

Fuente J. Relationship between sex ratio and time of insemination according to both time of 2

ovulation and maturational state of oocyte. Zygote 1999; 7:37–43. 3

[5] Trivers RL, Willard DE. Natural selection of parental ability to vary the sex ratio of 4

offspring. Science 1973; 179:90–2. 5

6

[6] Berry DP, Cromie AR. Artificial insemination increases the probability of a male calf in 7

dairy and beef cattle. Theriogenology 2006; 67: 346-52. 8

9

[7] Tadesse B. Calf sex ratios in artificially inseminated and natural mated female crossbred 10

dairy herd. Proceedings of the 13th annual conference of the Ethiopian Society of Animal 11

Production (ESAP) 2004; 225-30. August 25-27, Addis Ababa, Ethiopia. 12

[8] Statistical Analysis Systems Institute 2004. User’s Guide Version 9.2: Statistics. SAS 13

Institute, Cary NC, USA. 14

15

[9] Catalano R, Bruckner T. Secondary sex ratios and male lifespan: Damaged or culled cohorts. 16

Proc Natl Acad Sci USA 2006; 103 (5) 1639-43 17

18

[10] Wauters LA, Crombrugghe SA, Nour N, Matthysen E. Do female roe deer in good 19

condition produce more sons than daughters? Behav Ecol Sociobiol 1995; 37:189–93. 20

21

[11] Foote RH. Sex ratios in dairy cattle under various conditions. Theriogenology 1977; 8:349–22

56. 23

[12] Khan S, Qureshi MS, Chand N, Sultan A, Rafiullah I, Ihsanullah K, et al. Effect of breeding 24

method on calf sex and postpartum reproductive performance of cattle and buffaloes. Sarhad J 25

Agric 2012; 28 (3): 469-76. 26

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[ 13] Zobel R, Gereš D, Pipal I, Buić V, Gračner D, Tkalcic S. Influence of the semen deposition 1

site on the calves' sex ratio in Simmental dairy cattle. Reprod Domest Anim. 2011; 46(4):595-01. 2

Web reference: 3

[14] (http://www.cals.ncsu.edu/an_sci/extension/animal/repro/spw97-1.htm 4

Table 1. Summary statistics showing observation on estrus time, service time, type of 5

service/insemination and calf sex by genotype . 6

Table 2. Summary statistics showing number of observations of estrus by genotype and season. 7

8

9

Table 3. Factors affecting sex ratio in cattle 10

Table 4. Analysis of least squares means estimates for the effects with alpha fixed at 0.05. 11

Table 5. Ratio of female calves born as categorized by different explanatory variables 12

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Genotype Variables by genotypes

Estrus time Service time Type of insemination Calf sex

Morning Afternoon Morning Noon Natural mating AI M F

Boran 790 513 507 794 94 1255 673 687

Horro 58 30 27 59 52 107 55 113

F1 F x Bo 512 643 662 486 777 412 568 655

F1 J x Bo 111 96 100 102 203 44 131 138

F1 S x Bo 70 74 70 73 113 54 86 101

F1 Ho crosses 57 76 60 73 85 112 103 105

F2 F x Bo 162 198 198 162 372 17 181 221

F2 J x Bo 95 115 116 94 193 45 115 131

75% F crosses 79 73 75 77 130 42 67 109

75% J crosses 42 62 63 41 121 21 72 85

Three breed crosses 105 152 155 102 247 8 111 150

Total 2081 2032 2033 2063 2387 2117 2162 2495

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Genotype Rainy season Spring season Dry season Short rainy Total

Boran 432 171 415 330 1348

Horro 40 29 43 32 144

F1 F x Bo 338 201 441 235 1215

F1 J x Bo 57 30 88 79 254

F1 S x Bo 60 35 42 43 180

F1 Ho crosses 57 43 48 49 197

F2 F x Bo 121 48 116 112 397

F2 J x Bo 66 21 83 68 238

75% F crosses 41 23 58 51 173

75% J crosses 38 26 53 28 145

Three breed crosses 66 11 96 85 258

Total 1316 638 1483 1112 4549

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No. Effects DF Wald Chi-Squre Pr> χ2

1 Genotype 10 15.41 0.12

2 Estrus season 3 3.25 0.36

3 Estrus time 1 0.05 0.83

4 Parity 8 5.84 0.66

5 Service time 1 0.07 0.79

6 Service type 1 12.70 0.00

7 Breed*estrus season 30 44.36 0.04

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Effects DF Estimate ± SE χ2 Pr> χ2

1. Genotypes Boran cows (Bo) 1 0.0206±0.05 0.14 0.7042 Horro cows (Ho) 1 0.7201±0.16 19.18 0.0001 F1 F x Bo 1 0.1425±0.06 6.18 0.0129 F1 J x Bo 1 0.0521±0.12 0.18 0.6696 F1 S x Bo 1 0.1608±0.15 1.20 0.2732 F1 Exotic x Ho cross 1 0.0192±0.14 0.02 0.8897 F2 F x Bo 1 0.1997±0.10 3.97 0.0464 F2 J x Bo 1 0.1303±0.13 1.04 0.3080 >62.5% F inheritances 1 0.4867±0.16 9.83 0.0017 >62.5% J inheritances 1 0.1660±0.16 1.07 0.3001 Three breed crosses 0.3011±0.13 5.78 0.0162

2. Parity 1 1 0.2330±0.06 16.51 0.0001 2 1 0.1616±0.06 6.19 0.0128 3 1 0.0499±0.07 0.45 0.5030 4 1 0.2021±0.09 5.62 0.0177 5 1 0.1144±0.10 1.37 0.2418 6 1 0.0263±0.11 0.05 0.8186 7 1 0.0196±0.14 0.02 0.8886 8 1 -0.0172±0.18 0.01 0.9263 9+ 1 0.0000±0.18 0.00 1.0000

3. Estrus seasons 1. Rainy season 1 0.1921±0.06 12.03 0.0005 2. Spring season 1 0.2203±0.08 7.65 0.0057 3. Dry season 1 0.0810±0.05 2.43 0.1192 4. Short rainy season 1 0.1261±0.06 4.40 0.0359

4. Estrus time 1. Afternoon/evening 1 0.1321±0.04 8.82 0.0030 2. Morning 1 0.1735±0.04 15.54 0.0001

5. Service time 1. Morning 1 0.1251±0.04 7.92 0.0049 2. Afternoon 1 0.1770±0.04 16.02 0.0001

6. Service type 1. Natural mating 1 0.2750±0.04 44.26 0.0001 2. Artificial insemination

(AI) 1 0.0142±0.04 0.011 0.7444

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Genotype Value Parity Value Estrus season Value Estrus

time

Value Service

time

Value Service

type

Value

Boran 0.505 1 0.558 Rainy season 0.548 Afternoon 0.533 Morning 0.531 Natural 0.568

Horro 0.673 2 0.540 Spring season 0.555 Morning 0.543 Afternoon 0.544 AI 0.504

F1 F x Bo 0.536 3 0.512 Dry season 0.520

F1 J x Bo 0.513 4 0.550 Short rainy 0.531

F1 S x Bo 0.540 5 0.529

F1 Horro crosses 0.504 6 0.507

F2 F x Bo 0.550 7 0.504

F2 J x Bo 0.533 8 0.496

>62.5% F inheritances 0.619 9 0.500

>62.5% J inheritances 0.541

Three breed crosses 0.575