Genetic parameters for reproduction and growth traits in Boer goats in Brazil

Small Ruminant Research 136 (2016) 247–256

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Genetic parameters for reproduction and growth traits in Boer goats in Brazil L.M. Menezes a , W.H. Sousa b , E.P. Cavalcanti-Filho c , L.T. Gama d,∗ a

Programa de Pós-Graduac¸ão em Zootecnia, Universidade Federal da Paraíba, Brazil Empresa Estadual de Pesquisa Agropecuária da Paraíba, Brazil Departamento de Zootecnia, Universidade Federal da Paraíba, Brazil d CIISA-Faculdade de Medicina Veterinária, Universidade de Lisboa, Portugal b c

a r t i c l e

i n f o

Article history: Received 24 September 2015 Received in revised form 18 January 2016 Accepted 4 February 2016 Available online 8 February 2016 Keywords: Heritability Meat goats REML Variance components

a b s t r a c t Data collected over a period of 15 years in a herd of Boer goats in Brazil were used to estimate genetic parameters for reproductive and growth traits. The analyses included weights of about 1300 kids and nearly 750 reproductive records by 345 goats. The mixed model analyses of reproductive traits (kidding interval, litter size, litter weight at birth and weaning, doe weight at parturition) included the fixed effects of contemporary group and parity, and the random effects of additive genetic and permanent environmental effects. For growth traits (birth weight, weaning weight, average daily gain and relative growth rate) the fixed effects considered were contemporary group, sex, number born and parity, while the random effects were the direct and maternal genetic effects (allowing for their covariance), permanent environmental effect of the dam and litter common environmental effect. The mean performance in the Boer goats included in our study was 56.4 ± 11.5 kg for live weight at parturition, 456 ± 198 days for kidding interval, 1.70 ± 0.66 kids for litter size, 5.8 ± 2.2 and 23.4 ± 9.7 kg for litter weight at birth and weaning, respectively, while the kids had means for birth and weaning (112 days) weight of 3.4 ± 0.8 and 15.2 ± 4.7 kg, with average daily gain and relative growth rate of 105.2 ± 40.0 g and 1.3 ± 0.3%, respectively. The heritability (h2 ) estimate for reproductive traits was near zero for litter size and litter weight at birth, about 0.1 for kidding interval and litter weaning weight, and close to 0.4 for doe weight at parturition. The estimated repeatability was about 0.5 for doe weight at parturition and near 0.1 for all the other reproductive traits. The h2 of direct effects for growth traits was consistently higher than h2 of maternal effects. For birth weight, h2 estimates of direct and maternal effects were smaller than for the other traits, in the range of 0.05 to 0.08. For growth and weight traits measured after birth, h2 of direct effects ranged from 0.23 to 0.31, and h2 of maternal effects was about 0.13 for the various traits. There was a strong antagonism between direct and maternal effects, with a genetic correlation of −0.67 for birth weight, and about −0.8 to −0.9 for the other traits. Relative to the phenotypic variance, the influence of the permanent environmental effect of the dam represented about 0.11–0.15 and the common environmental effect of the litter corresponded to 0.32 for birth weight and ranged from 0.13 to 0.18 for the other traits. These results indicate that selection for some reproductive traits such as litter size may be difficult, given the low levels of genetic variability, but could be more successful for other traits like litter weaning weight and kidding interval. Selection for weight at and growth rate up to weaning should take into account the importance of direct and maternal genetic effects as well as the genetic antagonism between these two components. Factors which are seldom considered in mixed model analyses, such as the common litter effect, were found to be of major importance, and must be considered in the linear models used to estimate genetic parameters and predict breeding values in meat goats. © 2016 Published by Elsevier B.V.

1. Introduction

∗ Corresponding author. E-mail address: [email protected] (L.T. Gama). http://dx.doi.org/10.1016/j.smallrumres.2016.02.003 0921-4488/© 2016 Published by Elsevier B.V.

With a herd of around 9 million animals, goat production has gained a prominent position in the Brazilian agribusiness sector, particularly in the Northeast region, where over 90% of the total goat population is found (IBGE, 2013). Goat production in this

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region is mostly extensive, based on local native breeds that use the bushes and scrub available in the Caatinga eco-system (Leal et al., 2005) as their major source of feedstuffs. Currently, efforts have been developed towards the development of a well-structured supply chain, which can give support to a specialized activity and thus respond to the increased market demand for goat meat products. While native breeds are the ones that are better adapted to the harsh environmental conditions of the Caatinga biome, other breeds may be more suitable under a scenario of intensification of the production system. In extensive systems, poor reproductive performance is one of the major factors affecting the efficiency of production in goats, and represents one of the main limitations towards the optimization of production systems (Fonseca, 2006; Sousa, 2002). Several measures of reproductive efficiency have been used in goats, but the more common are litter size, fertility (often assessed by parturition interval) and litter weight at standard ages (Santos et al., 2013; Simplício et al., 2005; Sousa, 2002). In addition to reproductive characteristics, growth traits are important factors influencing the profitability of any meat goat production system (Zhang et al., 2009a), because rapid growth during the early part of life reduces maintenance costs and can be considered as an early indicator of post-weaning animal growth (Portolano et al., 2002; Hanford et al., 2006). One breed that was successfully imported to Brazil and showed good adaptation to these improved conditions is the Boer goat, which is known for its fast-growing ability, excellent meat quality, high fertility and prolificacy, and good maternal ability (Erasmus, 2000; Greyling, 2000; Malan, 2000). The breed was developed in South Africa from local goat populations (Campbell, 2004), and was first imported to Brazil in the 1990’s (Sousa, 2002). Boer goats quickly spread across the country, with the highest concentration of animals raised in the Northeast, where they are used in crossbreeding programs with local breeds of goats. To capitalize on the benefits of crossbreeding, the breeds involved should continue to be selected for the traits of interest, to ensure that production efficiency is improved over time. In the particular case of meat goats, the selection goals generally used are those associated with reproductive efficiency, growth rate and meat quality, with relative importance depending on the production environment and the breeds involved (Kosgey, 2004; Kosgey et al., 2006; Shrestha and Fahmy, 2007). Setting up a breeding program requires knowledge of the genetic parameters underlying the selected traits, to set up an appropriate genetic evaluation and selection program. However, estimates of genetic parameters for the different measures of productive and reproductive performance in meat goats, particularly for the Boer breed, are still limited and not always consistent (Schoeman et al., 1997; Al-Shorepy et al., 2002; Liu et al., 2002; Zhang et al., 2002; Matika et al., 2003; Bosso et al., 2007; Shrestha and Fahmy, 2007; Zhang et al., 2008; Zhang et al., 2009a; Zhang et al., 2009b). This study aimed to estimate variance components and genetic parameters for characteristics related to reproductive efficiency (weight of dam at kidding, kidding interval, litter size, litter weight at birth and at weaning) and pre-weaning growth (birth weight, weaning weight, average daily gain and relative growth rate), in a population of Boer goats raised in Brazil.

2. Material and methods

States. Another importation took place in 2000, where 150 embryos of Boer goats were imported from South Africa. In addition, 11 Boer sires from private Brazilian herds were used as breeders in this population between the years 2007 and 2012. In the period of data collection described in this study (1997–2012), 1826 kids were born in the herd, offspring of 70 bucks and 345 goats used in reproduction. 2.2. Data and Pedigree files The permission by the committee of animal welfare and ethics of animal use in experimentation was not required for this study, because the data were obtained from a data file already existing, belonging to the State Company of Agricultural Research of Paraíba—EMEPA/PB. The animals in this herd were raised under semi-intensive conditions in native and cultivated pastures (Andropogon gayanus Kunth e Cenchrus ciliaris L.), with minerals and concentrate supplements, throughout the year, as well as supplementation with conserved forage (hay and silage) during periods of low availability of pastures. In this equatorial region, only two seasons exist, such that the months between February and June are classified as rainy season and the months between July and January as dry season. Two mating seasons were used, i.e., in February–March and in September–October. The mating season lasted for 45 days, and goats were hand-mated by selected bucks after heat detection with vasectomized males. Therefore, sires were known for all registered offspring. Our study included animals born between the years 1997 and 2012. For each animal included in the analyses, the information of sire, dam, sex and birth date, were available. For imported animals, the information about sire and dam, as well as any additional ancestors, were included in the data file, making the pedigree of animals born in Brazil progressively more complete. The data were edited and validated for the consistency of pedigree information, gender, date of birth and duplicate records. Other sources of information were also available, including type of birth and the weight of kids at birth and at weaning, as well as the weight of breeding females at different stages of their life cycle. Kids were weaned at 112 days of age. 2.3. Reproductive performance To evaluate the reproductive efficiency of Boer goats, the following traits were analyzed: litter size (LS), defined as the total number of offspring born per parturition; kidding interval (KI), computed as the interval between two consecutive parturitions; litter weight at birth (LWB), calculated as the sum of the weight of kids at birth; litter weight at weaning (LWW), calculated as the sum of the weight of kids at weaning; doe weight at parturition (DWP), corresponding to the weight of the goat on the day that it produced offspring. 2.4. Pre-weaning growth To assess pre-weaning growth of young goats, the following traits were analyzed: birth weight (BW), weaning weight (WW), average daily gain (ADG) and relative growth rate (RGR). The ADG was calculated as: ADG =

(WW − BW) Ageat weaning

2.1. Animals The herd of Boer goats evaluated in this study was established in 1996 from a group of 92 live animals imported from the United

The ADG was used to compute by interpolation the estimated WW at 112 days of age for those kids where weaning took place before or after the standard age.

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The RGR, which is an estimate of the percentage daily weight gain, was computed as suggested by Fitzhugh and Taylor (1971): RGR =

 ln

(WW) − ln (BW) 112



× 1000

where ln represents the natural logarithm. 2.5. Mixed model analyses Contemporary groups were formed by concatenation of year (1997–2012) and season (dry and rainy) of parturition, and contemporary groups with less than 5 animals were deleted, as well as records considered as outliers for a given trait (outside the bounds of mean ± 3 standard deviations in a contemporary group). Females with parity order equal to or above 6 were grouped together. The fixed factors considered in the analyses of reproductive traits included contemporary group and parity order. The model used to estimate genetic parameters for preweaning growth traits included the fixed effects of contemporary group (combination year-season of birth), sex, type of birth (1–4 kids/parturition) and parity order (1–6). The random effects considered in the mixed model analyses differed, depending on the trait analyzed. For reproductive traits, the permanent environmental effect of the doe on its various parturitions was included in the model, in addition to its direct genetic effect. For growth traits, the random genetic effects considered included the direct genetic effect of the animal producing the record and the maternal genetic effect of its dam, and it was assumed that a correlation could exist between these two genetic effects. Also for growth traits, in addition to genetic effects, the permanent environmental effect of the doe on its various litters was also considered, as well as the common environmental effect shared by all kids born in the same litter.

fixed effects (contemporary group, sex, type of birth and parity order), associated with the incidence matrix X; Za is the incidence matrix of the direct genetic additive effects; a is the vector of direct genetic additive effects associated with the Za incidence matrix; Zm is the incidence matrix of maternal genetic additive effects; m is the vector of maternal genetic additive effects associated with the Zm incidence matrix; Zc is the incidence matrix relating each observation with the corresponding maternal permanent environmental effect; c is the vector of maternal permanent environmental effects associated with the Zc incidence matrix; Zl is an incidence matrix relating each observation with the corresponding common effect of litter; l is the vector of common effects of a litter, associated with the Zl incidence matrix; e is the vector of residual random effects associated with the observations. The (co) variance structure for the random effects associated with reproductive traits under model 1 was:

⎡ ⎤ a

y = Xˇ + Za a + Zc c + e

(1)

where y is the vector of observations for the dependent variable (LS, KI, LWB, LWW or DWP); X is the incidence matrix of fixed effects for the dependent variable and ˇ is the corresponding vector of fixed effects (contemporary group and parity order); Za is the incidence matrix of the direct additive genetic effects; a is the vector of direct additive genetic effects associated with the Za incidence matrix; Zc is the incidence matrix of the permanent effects of the dams; c is the vector of permanent environmental effects of dams associated with the Zc incidence matrix; e is the vector of residual random effects associated with the observations. The mixed model used to analyze growth traits was the following: y = Xˇ + Za a + Zm m + Zc c + Zl l + e

(2)

where y is the vector of observations for the dependent variable (BW, WW, ADG or RGR); X is the incidence matrix of fixed effects for the dependent variable and ˇ is the corresponding vector of

⎡ ⎢

Aa2

Var ⎣ c ⎦ = ⎣ 0 e

0

0

0

Ic2

0

0

Ie2

⎤ ⎥ ⎦

(3)

a2

where is the direct additive genetic variance, c2 is the variance of permanent environment, e2 is the residual variance, A is t relationship mrix and I is the identity matrix. For growth-traits analyzed with model 2, the (co) variance associated with the random effects was the following:

⎡ A 2 A 0 0 am a a ⎢ Aam A 2 0 0 m ⎢m⎥ ⎢ ⎢ ⎥ ⎢ 2 ⎢ ⎢ ⎥ Var ⎢ c ⎥ = ⎢ 0 0 Ic 0 ⎣l ⎦ ⎢ ⎣ 0 0 0 Il2 ⎡

⎤

e

2.6. Estimation of genetic parameters The components of variance were estimated by Restricted Maximum Likelihood (REML), through AI-REML algorithm (AI—average information) using the WOMBAT computer program, developed by Meyer (2007), employing single-trait animal models. The convergence criterion was adopted according to the default of the WOMBAT software (Meyer, 2007). At each convergence the program was restarted, using as initial values those obtained in the previous analysis, to ensure that convergence is achieved in a global maximum likelihood function (Boldman et al., 1995). In matrix notation, the model used in the mixed model analyses of reproductive performance traits was the following:

249

0

0

0

0

0

⎤

⎥ ⎥ ⎥ 0 ⎥ ⎥ ⎥ 0 ⎦ 0

(4)

Ie2

2 is the maternal additive genetic variance,  where m am is the covariance between additive direct and maternal effects, c2 is the variance of maternal permanent environmental effects, l2 is the variance of the common effect of litter and the other components are as defined in Eq. (3).

3. Results The number of observations and descriptive statistics for the various traits analyzed are presented in Table 1. Reproductive data were available for nearly 750 parturitions by about 350 goats, and pre-weaning growth information was available for nearly 1300 kids. The Boer goats included in our study had a mean live weight at parturition of 56.4 ± 11.5 kg, and a mean kidding interval of 456 ± 198 days. Overall, 80.44% of the animals were born in multiple births, such that the mean LS was 1.70 ± 0.66 kids, with mean weight of the litters of 5.8 ± 2.2 kg at birth and 23.4 ± 9.7 kg at weaning (112 days). The Boer kids born in the herd had a mean BW and WW of 3.4 ± 0.8 and 15.2 ± 4.7, respectively. The corresponding mean ADG and RGR were 105.2 ± 40.0 g and 1.3 ± 0. 3%, respectively. Variance components estimates are in Table 2 for reproductive traits and Table 3 for pre-weaning growth traits. For reproductive traits, the estimated a2 was larger than the estimated c2 for all traits except LWB and LS, but in these traits the a2 estimate was very small, while for LWW the estimated c2 was nearly non-existent. For growth traits, the direct genetic component was consistently higher than the maternal component, and a negative covariance between direct and maternal effects was observed for all traits. For the permanent environmental effects, the variance of com-

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Table 1 Number of observations (n) and descriptive statistics for the maternal and offspring traits analyzed in Boer goats. Group

Trait

Acronym

n

Mean

SD

Maternal traits

Kidding interval (days) Doe weight at parturition (Kg) Litter weight at birth (Kg) Litter weight at weaning (Kg) Litter size (no. kids) Birth weight (Kg) Weaning weight (Kg) Average daily gain (g) Relative growth rate (% × 10)

KI DWP LWB LWW LS BW WW ADG RGR

417 743 748 666 754 1305 1050 1050 1050

456.37 56.39 5.80 23.42 1.71 3.41 15.21 105.16 13.03

198.45 11.50 2.17 9.70 0.66 0.80 4.65 39.98 2.87

Offspring traits

SD—Standard Deviation. Table 2 Variance component estimates for the reproductive traits analyzed in Boer goatsa . Trait

a2

c2

p2

Kidding interval (days) Doe weight at parturition (Kg) Litter weight at birth (Kg) Litter weight at weaning (Kg) Litter size (no. kids)

5551.8 31.97 0.045 8.34 0.0004

213.24 14.26 0.39 0.007 0.037

53854.2 86.58 4.05 80.75 0.41

a a2 : variance of additive genetic direct effects; c2 : variance of permanent environmental effects of the dam; p2 : phenotypic variance.

Table 3 Variance component estimates for the offspring traits analyzed in Boer goatsa . Trait

a2

2 m

am

c2

l2

p2

Birth weight (Kg) Weaning weight (Kg) Average daily gain (g) Relative growth rate (% × 10)

0.08 0.23 0.31 0.28

0.05 0.12 0.13 0.13

−0.67 −0.89 −0.89 −0.81

0.07 1.73 137.2 0.76

0.14 2.97 188.0 0.88

0.44 16.53 1243.3 6.91

2 a a2 : variance of additive genetic direct effects; m : variance of additive genetic maternal effects; am : covariance between additive genetic direct and maternal effects; c2 : variance of permanent environmental effects of the dam; l2 : variance of common environmental effects of the litter; p2 : phenotypic variance.

mon litter effects was large, and always larger than the variance of common maternal permanent environmental effects. The ratios of the various variance components relative to phenotypic variance are presented in Table 4 for reproductive traits and Table 5 for growth traits. The heritability estimates for reproductive traits were near zero for LS and LWB, about 0.1 for KI and LWW, and close to 0.4 for DWP. On the other hand, the component due to permanent environmental effects of the goat was close to zero for KI and LWW, about 0.1 for LS and LWB, and 0.17 for DWP. As a result of these two components, the estimated repeatability was about 0.5 for DWP and near 0.1 for all the other reproductive traits. The heritability of direct effects for growth traits (BW, WW, ADG and RGR) was consistently higher than the heritability of maternal effects. For BW, both heritability estimates were smaller than for the other traits, in the range of 0.05–0.08. For growth traits measured after birth, the heritability of direct effects ranged from 0.23 to 0.31, while the heritability of maternal effects was about 0.13 for the various traits. The correlation between direct and maternal effects was strong and negative, of −0.67 for BW, and about −0.8 to −0.9 for the other traits. The component due to permanent environmental effect of the dam expressed relative to phenotypic variance ranged from about 0.11 to 0.15 for the various traits, while the common environmental effect of the litter was 0.32 for BW and ranged from 0.13 to 0.18 for the other traits. The solutions of fixed effects obtained in mixed-model analyses are graphically presented in Figs. 1–5. Sex of the kids (Fig. 1) had a significant effect on all weight traits except RGR. Thus, BW, WW and ADG were higher in males by about

0.3 kg, 1.6 kg and 12 g, respectively. Males also had a RGR higher by 0.07% relative to females, but the difference was not significant given the large standard error of the estimate. When the effect of type of birth was assessed for the weight traits analyzed (Fig. 2), the RGR was not affected by the number of kids born, except in quadruplets, which showed a marked increase (P < 0.05 relative to the other birth-types). However, the weights at different ages and growth rate tended to decline as litter size increased such that, when compared to singles, twin kids had lower BW by about 0.6 kg, WW by 3.3 kg and ADG by 24 g, and the corresponding difference for triplets was 1.0 kg, 4.5 kg and 32 g, respectively. Differences in quadruplets were less consistent, as a consequence of their reduced number. The order of parturition had a pronounced effect on kid weights at various ages but not on RGR (Fig. 3). A general trend was observed for an increase in the various weights as the order of parturition increased, with a decline in goats after the fifth kidding. However, some reduction in WW and ADG was also observed in goats at the fourth kidding. On the other hand, RGR tended to remain stable up to the third kidding and declined afterwards. The relationship of reproductive traits with order of parturition (Fig. 4) indicates a trend towards higher mean values in goats between the second and fifth kidding, except for KI. The effect was smaller in LWB, with a difference of about 1.3–1.6 kg when goats in the third to fifth parity were compared with first-parity goats. For LWW, the difference between third to fifth parity relative to first parity goats was about 4.6–5.2 kg. Litter size in second through sixth parity goats was about 0.2–0.3 kids higher than in first parity. The DWP increased linearly until the fourth parity, where the difference relative to first parity was about 13 kg, and stabilized afterwards. The relationship between parity and KI differed from the other reproductive traits, such that an increase of about 4–5 days relative to first parity was observed in second and third parity, with a decline after the fourth parity to values slightly lower than those obtained in first kidding. Year of parturition had a pronounced effect on all traits analyzed, with wide fluctuations from year-season to year-season (Fig. 5). No clear pattern could be established regarding the benefits of any of the kidding seasons for any of the traits analyzed, because none of the seasons could be considered consistently better across the period analyzed.

4. Discussion Due to the difficulties regarding the reliable measurement and obtaining information on large herds and databases, the number of studies reporting genetic parameters for reproductive traits in goats is limited, especially for the Boer breed (Browning et al., 2007; Zhang et al., 2009b). Reproductive and survival rates are undoubtedly the most important traits in all small ruminant production systems, whatever the environment considered (Matika et al., 2003). Given the

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251

Table 4 Estimated ratios of variance components and corresponding standard error for the reproductive traits analyzed in Boer goatsa . Trait

h2a

Kidding interval Doe weight at parturition Litter weight at birth Litter weight at weaning Litter size

0.10 0.37 0.01 0.10 0.00

a

c2 ± ± ± ± ±

0.09 0.10 0.05 0.07 0.06

re

0.00 0.17 0.10 0.00 0.09

± ± ± ± ±

0.10 0.09 0.07 0.07 0.07

0.11 0.53 0.11 0.10 0.09

± ± ± ± ±

0.07 0.04 0.04 0.05 0.04

h2a : heritability of additive genetic direct effects; c 2 : ratio of permanent environmental effects of the dam relative to phenotypic variance; re : repeatability.

Table 5 Estimated ratios of variance components and corresponding standard error for the offspring traits analyzed in Boer goatsa . Trait

h2a

Birth weight Weaning weight Average daily gain Relative growth rate

0.08 0.23 0.31 0.28

h2m ± ± ± ±

0.07 0.13 0.14 0.14

0.05 0.12 0.13 0.13

ram ± ± ± ±

−0.67 −0.89 −0.89 −0.81

0.08 0.09 0.09 0.10

c2 ± ± ± ±

0.63 0.23 0.20 0.24

0.15 0.11 0.11 0.11

l2 ± ± ± ±

0.07 0.06 0.06 0.07

0.32 0.18 0.15 0.13

± ± ± ±

0.05 0.06 0.05 0.05

a h2a : heritability of additive genetic direct effects; h2m : heritability of additive genetic maternal effects; ram : correlation between additive genetic direct and maternal effects; c 2 : ratio of permanent environmental effects of the dam; l2 : ratio of common environmental effects of the litter.

BW

WW

ADG

RGR

3.0

Male-Female soluon

2.5 2.0 1.5 1.0 0.5 0.0 -0.5 -1.0

Difference relave to single-born kids

Fig. 1. Differences between solutions for the fixed effect of sex (male–female solution) and corresponding standard error for birth weight (BW, kg), weaning weight (WW, kg), average daily gain (ADG, g/10) and relative growth rate (RGR, % × 10) in Boer goats.

5.0 4.0

BW

WW

ADG

RGR

3.0 2.0 1.0 0.0 -1.0 -2.0 -3.0 -4.0 -5.0 1

2

3

4

Birth type Fig. 2. Differences between solutions for the fixed effect of birth type (relative to single-born kids) for birth weight (BW, kg), weaning weight (WW, kg), average daily gain (ADG, g/10) and relative growth rate (RGR, % × 10) in Boer goats.

importance of reproductive rates and maternal ability for the success of meat production in small ruminants, the factors that interfere with them should be identified, in order to improve lifetime reproductive efficiency and thus reduce meat production costs (Wang and Dickerson, 1991). For example, recent results with sheep in Brazil indicate that increasing the number of offspring per female has a much larger impact than changing feed efficiency, lamb carcass weight or ewe mature weight, regardless of the production system (McManus et al., 2011).

Litter size in Boer goats estimated in our study (1.70) is somewhat lower than the means ranging between 1.93 (Casey and Van Niekerk, 1988) and 2.1 (Erasmus, 2000) reported for Boer goats in South Africa and 1.93 in Botswana (Seabo et al., 1996), but is in the range of 1.85 observed in Boer goats in the United States (Browning et al., 2006) and 1.75 in the Caribbean island of Reunion (Kimmes et al., cited by Erasmus, 2000). For native goats in Brazil, reported mean litter size is about 1.4 for Moxotó (Galvão et al., 2013), 1.4 in Gurgueia (Santos et al., 2013), 1.6 in Canindé and 1.5 in Marota

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Difference relave to first kidding

3.0

BW

2.5

WW

2.0

ADG

1.5

RGR

1.0 0.5 0.0 -0.5 -1.0 -1.5 1

2

3

4

5

6

Order of parturion Fig. 3. Differences between solutions for the fixed effect of order of parturition (relative to first kiddings) for birth weight (BW, kg), weaning weight (WW, kg), average daily gain (ADG, g/10) and relative growth rate (RGR, % × 10) in Boer goats.

(Simplício, 2008). Therefore, Boer goats in Brazil seem to be well adapted, with reproductive rates similar to those observed in other countries and higher than those reported for local goat breeds. In our study, we could not detect additive genetic influences on LS of Boer goats, as expressed by the heritability estimate of zero. This was somewhat unexpected as the heritability reported for LS in goats is generally small, but usually ranges between 0.05 and 0.10, with estimates per breed of 0.04 in Raeimi Cashmere goats (Mohammadi et al., 2012), 0.08 in Black Bengal (Mia et al., 2013), 0.11 in Creole (Gunia et al., 2011) and Polish (Bagnicka et al., 2007) goats, 0.12 in Boer goats in China (Zhang et al., 2009b) and 0.13 in Norwegian goats (Bagnicka et al., 2007). In sheep, heritability estimates for litter size tend to be slightly larger than in goats, as reported in the review of Safari et al. (2005), who indicate a mean estimate of 0.13. On the other hand, our estimate for the variance of common environmental effects of the doe was 0.09, which is above the estimate reported for the majority of the goat and sheep breeds reported in the literature. This could be a consequence of pedigrees not being too deep for imported animals or of insufficient connectedness among fixed effects, which would lead to an underestimate of heritability (Clement et al., 2001) while the correlation among repeated records would not be affected. A low heritability was also estimated for LWB (0.01), with a common environmental component of 0.10, probably for the same reasons discussed for LS. This heritability estimate for LWB (0.01) is smaller than the heritability of 0.14 reported for Boer goats in

16.0 14.0

China (Zhang et al., 2009b) and far below the estimates of 0.16 to 0.44 reported for different breeds of sheep (Lôbo et al., 2012; Mohammadi et al., 2012; Shiotsuki et al., 2014). The composite trait LWW is an indicator of overall productivity in goats that includes the cumulative contributions of litter size at birth, mortality up to weaning and weaning weight (Bromley et al., 2001), and is probably the most important selection criteria in goat breeding, given its economic impact on meat production efficiency (Sharma et al., 2004). Even though the genetic correlation of litter size at birth and LWW in sheep is reported to be between 0.41 and 0.83 (Safari and Fogarty, 2003), Luxford and Beilharz (1990) showed in a selection experiment with mice that direct selection for LWW was about three times more effective in improving LWW than indirect selection for LS. In our study, the heritability estimate for LWW was 0.10, which is in line with the estimate of 0.14 for Boer goats in China (Zhang et al., 2009b), and close to the mean estimate of 0.11 reported for sheep (Safari et al., 2005). The heritability of kidding interval was estimated to be 0.10 in the Boer herd analyzed, which is slightly higher than the estimates of 0.02–0.08 in American goat breeds (García-Peniche et al., 2012) and 0.09 for the same group of American breeds more recently ˜ et al., 2014), but is far above the estimates of (Castaneda-Bustos 0.02 and 0.03 in Polish and Norwegian goats, respectively (Bagnicka et al., 2007). These estimates, however, are not directly comparable, as seasonality of reproduction has an important influence on kidding interval, and the various breeds and countries mentioned

KI

0.30

LWB

0.25

LWW

0.20

10.0

DWP

8.0

0.15

LS

6.0

0.10

4.0 2.0

0.05

0.0

0.00

Lier Size

KI/LWB/LWW/DWP

12.0

-2.0 -0.05

-4.0 -6.0

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Fig. 5. Solutions for the fixed effect of year-season combinations (season: dry season − −䊉− ; rainy season ––). a) Birth weight (BW); b) weaning weight (WW); c) average daily gain (ADG); d) relative growth rate (RGR); e) kidding interval (KI); f) litter size (LS); g) litter weight at birth (LWB); h) litter weaning weight (LWW); i) doe weight at parturition (DWP).

certainly differ in seasonal effects on goat reproductive cycling (Chemineau et al., 2008), while seasonality is expected to be of minor importance in the region of Brazil assessed here, given its proximity with the Equator. The mean weight of mature does found in our study (DWP about 56.4 kg) is intermediate in the range reported for the breed worldwide, which shows an extremely large variability, with reported means of 35 kg in South African villages (Lusweti, 2000), 47 kg in the Caribbean island of Guadeloupe (Gunia et al., 2011), 58 kg in Malaysia (Ariff et al., 2010) and 80–100 kg in the United States (Lu, 2001). This could, however, be a consequence of the recognized variability of the original Boer breed, as different types are recognized by the South African Breeders Association (http://www.boerboksa.co.za/goat-breeds/sa-boergoat/). The estimated heritability of DWP in our study was rather large (0.37) with a large permanent environmental effect (0.17). This is, however,

not surprising, given the large genetic influence associated with mature weight, with heritability estimates of 0.32 in Cashmere (Mohammadi et al., 2012) and Creole (Gunia et al., 2011) goats, whereas in sheep the mean heritability estimate of mature weight ranges, depending on the breed-type, between 0.30 and 0.41 (Safari et al., 2005). The mean birth weight observed for Boer goats in this study (3.41 ± 0.80) was similar to that reported by Lu and Potchoiba (1988) and Zhang et al. (2009a) for Boer goats. As expected, in our study the Boer kids born in multiple births had lower mean weights at different stages (BW, WW and ADG) than those born as singletons. These results agree with those reported by other authors when evaluating growth in various small ruminant breeds (Gifford et al., 1990; Mourad and Anous, 1998; Allain and Roguet, 2003; Zhou et al., 2003; Portolano et al., 2002; Mandal et al., 2006; Zhang et al., 2008; Zhang et al., 2009a). For RGR, however, no effect of

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litter size could be detected, indicating that, even though absolute growth rate is higher in single kids, proportional growth rate is similar for various birth-types. On the other hand, it has been described (Zhang et al., 2009a) that Boer kids born from multiple births seem to have post-weaning development similar to those born as singletons, such that the advantage of single kids in the growth period before weaning may result from lower competition for nutrients, both during gestation and through the lactation period. In our analyses, male kids consistently had higher weights at birth and weaning, with differences of about 0.3 and 1.6 kg, respectively, and a higher ADG by nearly 12 g/days. This superiority of weights and growth rate in males is well known in all species, including goats (Boujenane and El Hazzab, 2008; Zhang et al., 2009a; Mohammadi et al., 2012). When RGR was compared among sexes, no significant differences were found between males and females, a result which has often been reported for beef cattle (Goyache et al., 2003; Bonilha et al., 2015) and sheep (Stobart et al., 1986) but, to our knowledge, has not been studied in goats. The influence of order of parturition on the weight of kids at different ages indicates that, with the exception of RGR, goats between the second and fifth parity tended to produce heavier kids, except for WW and ADG, which showed an unexpected drop in fourth kiddings. Thus, the difference in WW relative to first kiddings ranged from about 1.5 kg in second and fourth kiddings to about 2.5 kg in fifth parturitions, with a strong drop in sixth and later parities. No differences were observed in RGR between the first and third parity, but a slight decline was observed in kids from fourth and sixth parities. Similar results regarding the relationship between age of dam and weight of the kids at birth and weaning have been reported for Boer (Zhang et al., 2009a) and other goat breeds (Husain et al., 1995; Carnicella et al., 2008), confirming the better maternal performance of goats of intermediate ages. For reproductive traits, the effect of order of parturition was also important, with higher levels of performance for LS, LWB, LWW and DWP in goats between the third and fifth parity, with some reduction in goats in the second and in the sixth and later parities, which were nevertheless superior to first parity. This pattern has been consistently found in other studies with different goat breeds under various production systems (Silva and Araújo, 2000; Hirakawa et al., 2007; Browning et al., 2011). Kidding interval followed a somewhat different pattern, with an increase in second and third parity, and a decline in the fourth and later parities. Similar results have been reported in other studies with small ruminants, where some fluctuations in parturition interval as parity advances have been shown in goats (Odubote, 1996; Abubakar and Irfan, 2014) and sheep (Maria and Ascaso, 1999; Komprej et al., 2011), particularly in the beginning of reproductive life. The increase in parturition interval observed in the second and third parity could be a consequence of the loss of body condition score at parturition in young goats, which would have more difficulty in recovering body reserves after kidding and thus delay reproductive cycling. Furthermore, it is unclear how seasonality of reproduction could play a role in modulating kidding interval, but in the group of goats studied here seasonality is not expected to represent a problem. The wide variation between weights and growth rate among kids born in different years and seasons, can be the result of changes in environmental conditions from year to year, including weather conditions, which in turn affect feed availability and animal welfare (Gebrelul et al., 1994; Al-Shorepy et al., 2002; Zhang et al., 2008; Zhang et al., 2009a; Wang et al., 2013). Our estimates of heritability of direct and maternal effects for BW in Boer goats (0.08 and 0.05, respectively) are in the lower range that has been reported by other authors for the same breed, as well as for small ruminants in general. In Boer goats, Zhang et al. (2009a) obtained estimates of, respectively, 0.17 and 0.26 for direct and maternal effects, while for other breeds the estimated heritability

has ranged between 0.15 and 0.18 for direct effects and 0.13–0.18 for maternal effects (Schoeman et al., 1997; Al-Shorepy et al., 2002; Roy et al., 2008; Mohammadi et al., 2011). In sheep, the mean estimates for direct and maternal effects for meat breeds are 0.15 and 0.24, as reported in the review by Safari et al. (2005). In nearly all analysis of BW considering maternal effects in small ruminants (Safari et al., 2005) as well as in beef cattle (Koots et al., 1994), a negative estimate has been obtained for the correlation between direct and maternal effects, as was the case in our study, where the correlation was estimated to be −0.67 for BW. Our estimates of heritability for direct and maternal effects on WW (0.23 and 0.12) and ADG (0.31 and 0.13) are close to the results found in the literature for various goat breeds, where the heritability estimates have ranged from 0.15 to 0.34 for direct effects and 0.0 to 0.08 for maternal effects (Schoeman et al., 1997; Al-Shorepy et al., 2002; Roy et al., 2008). Similar results have been obtained in sheep (Safari et al., 2005) breeds, indicating that the magnitude of genetic differences in direct effects up to weaning is larger than for maternal effects. Still, a strong negative relationship exists between the two components, as indicated by a negative correlation of −0.89. This antagonism between direct and maternal effects for both WW and ADG has been consistently found in goats as well as in sheep (Safari et al., 2005) and beef cattle (Koots et al., 1994), and implies that selection for increased WW should take into account genetic merit of selection candidates for both direct and maternal effects (Van Vleck et al., 1977; Robison, 1981). In our analyses, the estimated influence of permanent environmental effects of the dam and of common litter effects was considered. While the first is frequently included in mixed model analyses of growth in livestock species, the latter is seldom considered, but it should be taken into account in species that produce several offspring per parturition, such as pigs (Hermescha et al., 2000) and goats (Santos et al., 2013). Our results indicate that the estimated influence of permanent environmental effects of the dam (c2 ) and of common litter effects (l2 ) was high and, depending on the trait considered, c2 ranged from 0.11 to 0.15, and l2 from 0.13 to 0.32. These estimates are in line with those reported by Santos et al. (2013) in Anglo-Nubian goats in Brazil, and confirm the influence that common litter effects have in goat weights at different ages, with more importance on BW when compared with WW and ADG. This indicates that prenatal effects are a stronger component of the common litter influence than preweaning components, a feature that has also been shown in mice (Rhees et al., 1999; Wolf et al., 2011). On the other hand, disentangling the various random components considered in our analyses (additive genetic direct and maternal effects, common litter effects, and permanent environmental effects of the dam) is highly dependent on the depth and reliability of pedigree information and the existence of appropriate genetic ties among the various levels of fixed effects. Further analyses, with larger amounts of data collected over a longer period of time, should be carried-out to confirm our results.

5. Conclusions The Boer herd analyzed in this study has performance levels similar to those found for the same breed in other parts of the world, confirming its good adaptation to the conditions of Brazil. The observed levels of genetic diversity were very small for litter size and litter weight at birth, but the heritability was about 0.1 for kidding interval and litter weaning weight, and close to 0.4 for doe weight at parturition, indicating that selection for these traits is feasible. The heritability of direct effects for growth traits ranged from 0.08 to 0.31 while for maternal effects it ranged from 0.05 to 0.13. A strong negative genetic correlation (−0.7 to −0.9) was found between direct and maternal genetic effects, indicating that selec-

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tion should weigh them appropriately. Additional random factors such as the permanent environmental effect of the dam and common litter effects must be taken in consideration when using mixed model analyses of performance in meat goats. Conflict of interest The authors declare that there are no conflicts of interest. Acknowledgements We thank Coordenac¸ão de Aperfeic¸oamento de Pessoal de Nível Superior—CAPES for the scholarship granted and Company for Agricultural Research of State of Paraiba—EMEPA/PB for providing the data used in this study. F.Q. Cartaxo and J.A. Viana are thanked for technical support. References Allain, D., Roguet, J.M., 2003. Genetic and non-genetic factors influencing mohair production traits within the national selection scheme of Angora goats in France. Livest. Prod. Sci. 82, 129–137. Abubakar, M., Irfan, M., 2014. An overview of treatment options to combat peste des petits ruminants (PPR). Res. J. Vet. Pract. 2 (1), 4–7. Al-Shorepy, S.A., Alhadrami, G.A., Abdulwahab, K., 2002. Genetic and phenotypic parameters for early growth traits in Emirati goat. Small Rumin. Res. 45, 217–223. Ariff, O.M., Hifzan, R.M., Zuki, A.B.M., Jiken, A.J., Lehan, S.M., 2010. Maturing pattern for body weight: body length and height at withers of Jamnapari and Boer goats. Pertanika J. Trop. Agric. Sci. 33, 269–276. Bagnicka, E., Wallin, E., Lukaszewicz, M., Adnoy, T., 2007. Reproduction traits and their heritability in Polish goats and Norwegian populations of dairy goat. Small Rumin. Res. 68, 256–262. Boldman, K.G., Kriese, L.A., Van Vleck, L.D., Van Tassel, C.P., Kachman, S.D., 1995. A manual for use of MTDFREML: a set of program to obtain estimates of variances and covariances. Agric. Res. Serv., 120p. Bonilha, S.F., Cyrillo, J.N., Dos Santos, G.P., Branco, R.H., Ribeiro, E.G., Mercadante, M.E., 2015. Feed efficiency, blood parameters, and ingestive behavior of young Nellore males and females. Trop. Anim. Health Prod. 47 (7), 1381–1389. Bosso, N.A., Cissé, M.F., van der Waaij, E.H., Fall, A., van Arendonk, J.A.M., 2007. Genetic and phenotypic parameters of body weight in West African Dwarf goat and Djallonké sheep. Small Rumin. Res. 67, 271–278. Boujenane, I., El Hazzab, A., 2008. Genetic parameters for direct and maternal effects on body weights of Draa goats. Small Rumin. Res. 80, 16–21. Bromley, C.M., Van Vleck, L.D., Snowder, G.D., 2001. Genetic correlations for litter weight weaned with growth, prolificacy, and wool traits in Columbia, Polypay, Rambouillet, and Targhee sheep. J. Anim. Sci. 79, 339–346. Browning, R., Payton, T., Donnelly, B., Leite-Browning, M.L., Pandya, P., Hendrixson, W., Byars, M., 2006. Evaluation of three meat goat breeds for doe fitness and reproductive performance in the southeastern United States. 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil. Browning, R., Leite-Browning, M.L., Donnelly, B., Byars, M., 2007. Doe reproductive and fitness traits among three meat goat breeds semi-intensively managed in the southeastern US. J. Anim. Sci. 85 (1), 626. Browning Jr., R., Leite-Browning, M.L., Byars Jr., M., 2011. Reproductive and health traits among Boer, Kiko, and Spanish meat goat does under humid, subtropical pasture conditions of the southeastern United States. J. Anim. Sci. 89, 648–660. Campbell, Q.P., 2004. The origin and description of southern Africa’s indigenous goats. South Afri. J. Ani. Sc. 4, 18–22. Casey, N.H., Van Niekerk, W.A., 1988. The Boer goat. I. Origin, adaptability, performance testing, reproduction and milk production. Small Rum. Res. 1, 291–302. Carnicella, D., Dario, M., Ayres, M.C.C., Laudadio, V., Dario, C., 2008. The effect of diet parity, year and number of kids on milk yield and milk composition in Maltese goat. Small Rumin. Res. 77, 71–74. ˜ Castaneda-Bustos, V.J., Montaldo, H.H., Torres-Hernández, G., Pérez-Elizalde, S., Valencia-Posadas, M., Hernández-Mendo, O., Shepard, L., 2014. Estimation of genetic parameters for productive life, reproduction, and milk-production traits in US dairy goats. J. Dairy Sci. 97, 2462–2473. Chemineau, P., Guillaume, D., Migaud, M., Thiéry, J.C., Pellicer-Rubio, M.T., Malpaux, B., 2008. Seasonality of reproduction in mammals: intimate regulatory mechanisms and practical implications. Reprod. Domest. Anim. 43 (2), 40–47. Clement, V., Bibe, B., Verrier, E., Elsen, J.M., Manfredi, E., Bouix, J., Hanocq, E., 2001. Simulation analysis to test the influence of model adequacy and data structure on the estimation of genetic parameters for traits with direct and maternal effects. Gen. Sel. Evol. 33, 369–395. Erasmus, J.A., 2000. Adaptation to various environments and resistance to disease of the improved Boer goat. Small Rumin. Res. 36, 179–187.

255

Fitzhugh Jr., H.A., Taylor, C.S., 1971. Genetic analysis of degree of maturity. J. Anim. Sci. 33, 717–725. Biotecnologias da reproduc¸ão em ovinos e caprinos. Sobral: Embrapa Caprinos, 30 p. (Embrapa Caprinos. Documentos, 64). Galvão, M.A.A., Braga, A.M.N., Alves, A.A.C., Porciúncula, J.A., Silva, K.M., Lôbo, R.N.B., 2013. Prolificidade de um Rebanho da Rac¸a Moxotó no Semiárido Nordestino. VIII Congresso Nordestino de Produc¸ão Animal. Fortaleza, Ceará, Brazil. García-Peniche, T.B., Montaldo, H.H., Valencia-Posadas, M., Wiggans, G.R., Hubbard, S.M., Torres Vázquez, J.A., Shepard, L., 2012. Breed differences over time and heritability estimates for production and reproduction traits of dairy goats in the United States. J. Dairy Sci. 95, 2707–2717. Gebrelul, S., Sartin, L.S., Iheanacho, M., 1994. Genetic and non-genetic effects on the growth and mortality of Alpine, Nubian and crossbred kids. Small Rumin. Res. 13, 169–176. Gifford, D.R., Ponzoni, R.W., Ellis, N.J.S., Levinge, F.C.R., Milne, M.L., 1990. Genetic parameters for production characteristics of Australian Cashmere goats. Proc. Aust. Assoc. Anim. Breed. Genet. 8, 461–465. Goyache, F., Gutiérrez, J.P., Álvarez, I., Fernández, I., Royo, L.J., Gomez, E., 2003. Factors affecting actual weaning weight: preweaning average daily gain and relative growth rate in Asturiana de los Valles beef cattle breed. Arch. Tierz. 46, 235–244. Gunia, M., Phocas, F., Arquet, R., Alexandre, G., Mandonnet, N., 2011. Genetic parameters for weight, reproduction and parasite resistance traits in Creole goat. J. Anim. Sci. 89, 3443–3451. Greyling, J.P.C., 2000. Reproduction traits in the Boer goat doe. Small Rumin. Res. 36, 171–177. Hanford, K.J., van Vleck, L.D., Snowder, G.D., 2006. Estimates of genetic parameters and genetic trend for reproduction weight, and wool characteristics of Polypay sheep. Livest. Sci. 102, 72–82. Hermescha, S., Luxfordb, B.G., Graser, H.U., 2000. Genetic parameters for lean meat yield, meat quality, reproduction and feed efficiency traits for Australian pigs: 1. Description of traits and heritability estimates. Livest. Prod. Sc. 65 (3), 239–248. Hirakawa, M., Arasaki, Y., Sunagawa, K., Shinjo, A., 2007. The season of delivery, litter size and body measurement in Boer Goats. Nihon Chik. Gak. 78 (1), 15–20. Husain, S.S., Horst, P., Islam, A.B.M.M., 1995. Effect of different factors on pre-weaning survivability of Black Bengal kids. Small Rumin. Res. 18, l–5. IBGE, 2013. Produc¸ão Pecuária Municipal, Rio de Janeiro, 41 p. 1–108. Koots, K.R., Gibson, J.P., Smith, C., Wilton, J.W., 1994. Analyses of published genetic parameter estimates for beef production traits. 1. Heritability Anim. Breed. Abstr. 62, 311–338. Komprej, A., Gorjanc, G., Kompan, D., Kovaˇc, M., 2011. Genetic and environmental dispersion parameter estimation by test interval method in dairy sheep. Acta Agric. Slov. 98, 5–13. Kosgey, I.S., 2004. Breeding objectives and breeding strategies for small ruminants in the tropics. In: PhD Thesis. Wageningen University, The Netherlands, pp. 272 pp. Kosgey, I.S., Baker, R.L., Udo, H.M.J., Van Arendonk, J.A.M., 2006. Successes and failures of small ruminant breeding programmes in the tropics: a review. Small Rumin. Res. 61, 13–28. Leal, I.R., Silva, J.M., Tabarelli, M., Lacher Jr., T.E., 2005. Changing the course of biodiversity conservation in the caatinga of Northeastern Brazil. Conserv. Biol. 19, 701–706. Liu, G.Q., Yang, L.G., Jiang, X.P., Ding, J.T., Zhao, W.K., Shen, W.M., 2002. Estimation of genetic parameters of production traits in Boer goat. J. Nanjing Agric. Univ. 25, 77–79. Lôbo, R.N.B., Fernandes Júnior, G.A., Lôbo, A.M.B.O., Facó, O., 2012. Genetic (co) variance components for ratio of lamb weight to ewe metabolic weight as an indicator of ewe efficiency. Livest. Sci. 143, 214–219. Lu, C.D., 2001. Boer goat production: progress and perspective. In: Proceedings of the 2001. Conference on Boer goats, Beijing, China, October, pp. 20–25. Lu, C.D., Potchoiba, M.J., 1988. Milk feeding and weaning of goat kids. Small Rum. Res. 1, 105–112. Lusweti, E.C., 2000. A survey of goat production in the developing areas of the North West province of South Africa. Short Commun. South Afr. J. Anim. Sci. 30, 34–35. Luxford, B.G., Beilharz, R.G., 1990. Selection response for litter size at birth and litter weight at weaning in the first parity in mice. Theor. Appl. Genet. 80, 625–630. Malan, S.W., 2000. The improved Boer goat. Small Rumin. Res. 36, 165–170. Mandal, A., Neser, F.W.C., Rout, P.K., Roy, R., Notter, D.R., 2006. Estimation of direct and maternal (co) variance components for pre-weaning growth traits in Muzaffarnagari sheep. Livest. Sci. 99, 79–89. Maria, G.A., Ascaso, M.S., 1999. Litter size, lambing interval and lamb mortality of Salz, Rasa Aragonesa, Romanov and F1 ewes on accelerated lambing management. Small Rumin. Res. 32 (2), 167–172. Matika, O., van Wyk, J.B.G., Erasmus, J., Baker, R.L., 2003. Genetic parameter estimates in Sabi sheep. Livest. Prod. Sci. 79, 17–28. Meyer, K., 2007. WOMBAT—a tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). J. Zhejiang Univ. 8 (11), 815–821. McManus, C., Pinto, B.F., Martins, R.F.S., Louvandini, H., Paiva, S.R., Neto, J.B., Paim, T.P., 2011. Selection objectives and criteria for sheep in Central Brazil. Revista Brasileira de Zootecnia 40 (12), 2713–2720.

256

L.M. Menezes et al. / Small Ruminant Research 136 (2016) 247–256

Mia, M.M., Khandoker, M.A.M.Y., Husain, S.S., Faruque, M.O., Notter, D.R., 2013. Estimation of genetic and phenotypic parameters of some reproductive traits of Black Bengal does. Iran J. Appl. Anim. Sci. 3, 829–837. Mohammadi, K., Rashidi, A., Mokhtari, M.S., Beigi Nassiri, M.T., 2011. The estimation of (co) variance components for growth traits and Kleiber ratios in Zandi sheep. Small Rumin. Res. 99, 116–121. Mohammadi, H., Shahrebabak, M.M., Shahrebabak, H.M., 2012. Genetic parameter estimates for growth traits and prolificacy in Raeini Cashmere goats. Trop. Anim. Health Prod. 44, 1213–1220. Mourad, M., Anous, M.R., 1998. Estimates of genetic and phenotypic parameters of some growth traits in common African and Alpine crossbred goats. Small Rumin. Res. 27, 197–202. Odubote, I.K., 1996. Genetic Analysis of the reproductive performance of West African dwarf goats in the humid tropics. In: Proc. 3th Biennial Congress of the African Small Ruminant Research Network. UICC, Kampala, Uganda., International Livestock Research Institute, Nairobi, Kenya, pp. 33–36. Portolano, B., Todaro, M., Finocchiaro, R., van Kaam, J.H.B.C.M., 2002. Estimation of the genetic and phenotypic variance of several growth traits of the Sicilian Girgentana goat. Small Rumin. Res. 45, 247–253. Rhees, B.K., Ernst, C.A., Miao, C.H., Atchley, W.R., 1999. Uterine and postnatal maternal effects in mice selected for differential rate of early development. Genetics 153, 905–917. Robison, O.W., 1981. The influence of maternal effects on efficiency of selection: a review. Livest. Prod. Sci. 8, 121–137. Roy, R., Mandal, A., Notter, D.R., 2008. Estimates of (co) variance components due to direct and maternal effects for body weights in Jamunapari goats. Animal 2, 354–359. Safari, A., Fogarty, N.M., 2003. Genetic parameters for sheep production traits: estimates from the literature. Tech. Bull., 49, NSW Agriculture, Orange, Australia. Safari, E., Fogarty, N.M., Gilmour, A.R., 2005. A review of genetic parameter estimates for wool growth, meat and reproduction traits in sheep. Livest. Prod. Sci. 92, 271–289. Santos, N.P.S., Sarmento, J.L.R., Pimenta Filho, E.C., Campelo, J.E.G., Figueiredo Filho, L.A.S., Sousa Junior, S.C., 2013. Aspectos ambientais e genéticos da prolificidade em caprinos utilizando modelos bayesianos de limiar e linear. Arquivo Brasileiro de Medicina Veterinária e Zootecnia 65, 885–893. Schoeman, S.J., Els, J.F., van Niekerk, M.M., 1997. Variance components of early growth traits in the Boer goat. Small Rumin. Res. 26, 15–20. Seabo, D., Aganga, A.A., Mosienyane, M., 1996. Reproductive performance of Tswana ewes and Boer does in south-eastern Botswana. In: Lebbie, S.H.B., Kagwini, E. (Eds.), Small Ruminant Research and Development in Africa, Proceedings of the Third Biennial Conference of the African Small Ruminant Research Network. UICC, Kampala, Uganda. Sharma, R.C., Arora, A.L., Mishra, A.K., Kumar, S., Singh, V.K., 2004. Breeding Prolific Garole with Malpura sheep for increased reproductive efficiency in semi arid tropics of India. Asian–Aust. J. Anim. Sci. 17 6, 737–742.

Shrestha, J.N.B., Fahmy, M.H., 2007. Breeding goats for meat production 2: crossbreeding and formation of composite population. Small Rumin. Res. 67, 93–112. Shiotsuki, L., Oliveira, D.P., Lôbo, R.N.B., Facó, O., 2014. Genetic parameters for growth and reproductive traits of Morada Nova sheep kept by smallholder in semi-arid Brazil. Small Rumin. Res. 120, 204–208. Silva, F.L.R., Araújo, A.M., 2000. Desempenho Produtivo em Caprinos Mestic¸os no Semi-árido do Nordeste do Brasil. Revista Brasileira de Zootecnia 29, 1028–1035. Simplício, A.A., Freitas, V.J.F., Santos, D.O., 2005. Biotécnicas da Reproduc¸ão em Caprinos. Revista Ciências Agrárias, 43. Simplício, A.A., 2008. Estratégias de manejo reprodutivo como ferramenta para prolongar o período de oferta de carnes caprina e ovina no Brasil. Tecnologia & Ciência Agropecuária 2 (3), 29–39. Sousa, W.H., 2002. Programa de Melhoramento dos caprinos de corte no Nordeste do Brasil e suas perspectivas. Simpósio nacional de melhoramento animal, Campo Grande, Mato Grosso do Sul, Campo Grande, Brazil. Stobart, R.H., Bassett, J.W., Cartwright, T.C., Blackwell, R.L., 1986, 1986. An analysis of body weights and maturing patterns in western range ewes. J. Anim. Sci. 63, 729–740. Van Vleck, L.D., St Louis, D.G., Miller, J.I., 1977. Expected phenotypic response in weaning weight of beef calves from selection for direct and maternal genetic effect. J. Anim. Sci. 44, 360–367. Wang, C.T., Dickerson, G.E., 1991. Simulated effects of reproductive performance on life-cycle efficiency of lamb and wool production at three lambing intervals. J. Anim. Sci. 69, 4338–4347. Wang, Z., Wang, R., Zhang, W., Wang, Z., Wang, P., Liu, H., Gao, L., Bai, K., Meng, R., Zhou, J., Zhang, Y., Li, J., 2013. Estimation of genetic parameters for fleece traits in yearling inner Mongolia Cashmere goats. Small Rumin. Res. 109, 15–21. Wolf, J.B., Leamy, L.J., Roseman, C.C., Cheverud, J.M., 2011. Disentangling prenatal and postnatal maternal genetic effects reveals persistent prenatal effects on offspring growth in mice. Genetics 189, 1069–1082. Zhang, H.P., Li, L., Xu, G.Y., Zhu, X.D., Li, G.Q., Hang, H.Q., 2002. Estimation of genetic parameters of reproduction traits in Boer goat. Anim. Husb. Vet. Med. 34, 1–2. Zhang, C.Y., Yang, L.G., Shen, Z., 2008. Variance components and genetic parameters for weight and size at birth in the Boer goat. Livest. Sci. 115, 73–79. Zhang, C.Y., Zhang, Y., Xu, D.Q., Li, X., Su, J., Yang, L.G., 2009a. Genetic and phenotypic parameter estimates for growth traits in Boer goat. Livest. Sci. 124, 66–71. Zhang, C.Y., Chen, S.L., Li, X., Xu, D.Q., Zhang, Y., Yang, L.G., 2009b. Genetic and phenotypic parameter estimates for reproduction traits in the Boer dam. Livest. Sci. 125, 60–65. Zhou, H.M., Allain, D., Li, J.Q., Zhang, W.G., Yu, X.C., 2003. Effects of non-genetic factors on production traits of inner Mongolia cashmere goats in China. Small Rumin. Res 47, 85–89.