Skip to main content
PMC home page
Veterinary Medicine and Science logoLink to Veterinary Medicine and Science
. 2021 Sep 4;8(1):387–397. doi: 10.1002/vms3.628

Phenotypic and genetic parameter estimates for early growth, growth rate and growth efficiency‐related traits of Fogera cattle in Ethiopia

Demelash Kassahun 1,2, Mengistie Taye 1,3,✉, Damitie Kebede 1, Mekonen Tilahun 2, Assemu Tesfa 2, Addisu Bitew 2, Adebabay Kebede 2, Mulugeta Meseret 2, Eyasu Lakew 2, Tewodros Bimrow 2, Aynalem Haile 4
PMCID: PMC8788963  PMID: 34480429

Abstract

Background

Understanding the phenotypic and genetic parameter estimates of growth traits is important for an effective livestock genetic improvement programme.

Objectives

In this study, we evaluated the phenotypic performances and estimated genetic parameters for birthweight (BWT), weaning weight (WWT), pre‐weaning average daily gain (PADG), pre‐weaning Kleiber ratio (PKR), pre‐weaning growth efficiency (PGE) and pre‐weaning relative growth rate (PRGR) of Fogera cattle in Ethiopia.

Methods

Growth data collected from 2000 to 2018 in Andassa Livestock Research Center were used for the study. General linear model of SAS 9.1 was used to estimate the least squares mean (LSM) ± standard error (SE) for phenotypic performances, and AI‐REML of Wombat software combined with a series of five single‐trait animal models to estimate phenotypic variance and its direct, maternal and residual components. Calf sex, calf birth season and calf birth year were the fixed effects considered.

Results

The overall LSM ± SE BWT, WWT, PADG, PKR, PGE and PRGR were 21.28 ± 0.05 kg, 97.99 ± 0.67 kg, 320.29 ± 2.79 g, 10.10 ± 0.04, 3.51 ± 0.35 and 1.95 ± 0.00, respectively. All the fixed effects considered significantly (p < 0.001) affected all the traits. The direct heritability estimates for BWT, WWT, PADG, PKR, PGE and PRGR were 0.21 ± 0.07, 0.26 ± 0.01, 0.55 ± 0.19, 0.53 ± 0.18, 0.33 ± 0.00 and 0.50 ± 0.00, respectively. The genetic correlations among the traits ranged from negative (−0.20 ± 0.04; BWT‐PKR) to positive (0.99 ± 0.00; BW‐PGE, BW‐GR, WWT‐PGE, WWT‐PGR, ADG‐PGR, PKR‐PGR, PKR‐PGE and PGE‐PGR). Similarly, the phenotypic correlations ranged from −0.03 ± 0.20 to 0.99 ± 0.01; BWT‐PGE, BWT‐PRGR, WWT‐PGE, WWT‐PRGR, PKR‐PGE, PKR‐PRGR and PGE‐PRGR).

Conclusion

The positive and larger phenotypic and genetic correlations between most of the traits implied that selection based on one trait could improve the other traits. However, the negative phenotypic and genetic correlation between BWT‐PKRA implies that selection of Fogera calves based on either of the traits has an adverse effect on the other. Therefore, caution should be taken when designing the selection criteria for growth improvement.

Keywords: Fogera cattle, genetic parameter estimates, growth efficiency, growth performance, Klieber ratio

The phenotypic and genetic parameter of birthweight (BWT), weaning weight (WWT), pre‐weaning average daily gain (PADG), pre‐weaning Kleiber ratio (PKR), pre‐weaning growth efficiency (PGE) and pre‐weaning relative growth rate (PRGR) were estimated for Fogera cattle in Ethiopia using data collected from 2000 to 2018 in Andassa Livestock Research Center. Positive and negative correlations were obtained between the traits for both genetic and phenotypic parameters. The positive and larger phenotypic and genetic correlations between most of the traits implied that selection based on one trait could improve the other traits.

graphic file with name VMS3-8-387-g001.jpg

1. INTRODUCTION

Growth performance traits, primarily expressed and described by body weight and growth rate (Menale et al., 2011), are important factors that need to be considered in any breed improvement programme (Kumar et al., 2017; Pires et al., 2016). The growth performance of cattle determines the profitability of a farm which, in turn, is influenced by genetic and environmental factors. Accurate estimation of genetic parameters is critically important to implement sound breeding programmes and to assess the progress of ongoing genetic improvement programmes (Demeke et al., 2004). Similar to the genetics of the animal, management (feeding, health care) plays a determinant role on the performance of cattle.

The early growth rate of cattle has a strong implication on both reproductive and production performances (Zeleke et al., 2016). Early growth performance traits such as birthweight (BWT) and weaning weight (WWT) are the basis for selection in genetic improvement programmes for meat production due to their strong association with each other and mature body weight (Pires et al., 2016; Tesfa & Garikipati 2014). Thus, evaluation of the growth performances of indigenous cattle breeds is essential to ascertain the potential of the breeds and design genetic improvement programmes for a particular purpose. Moreover, growth rate and efficiency‐related traits including Kleiber ratio (KR), growth efficiency (GE) and relative growth rate (RGR) are very important for their indirect evaluation of growth. KR, defined as the weight gain per unit of metabolic body weight (Kleiber, 1961), is an important measure of GE (Kleiber, 1947; Köster et al., 1994). KR has an association with growth traits so that it can also be used as selection criteria for growth traits (Abegaz et al., 2005; Köster et al., 1994; Shoja & Sarain, 2016). GE‐related traits are economically important for genetic improvement programmes in the tropics (Mokhtari et al., 2019). Similar to KR, GE and RGR have a strong positive genetic correlation with growth traits (Ghafouri‐Kesbi & Gholizadeh 2017), and therefore, selection for better GE traits could improve the growth performance of the breed.

Fogera cattle are among the Zenga cattle of Ethiopia adapted to the belt area of Lake Tana, north‐western Ethiopia. The breed is known for its adaptation to the waterlogged marshy grazing areas of Lake Tana wetlands, better resistance to internal parasites and flies infestations, and for its traction power in marshy fields (Tesfa et al., 2016). Moreover, meat and milk production, and the high draught power of the breed are traits perceived by Fogera breed keepers in the belt (Bitew et al., 2010; Tesfa et al., 2016).

Phenotypic performance evaluation (Bekele et al., 2016; Menale et al., 2011; Tesfa et al., 2016) and genetic parameter estimates for growth traits (Bekele et al., 2016) for Fogera cattle have been carried out at Andassa Livestock Research Center (ALRC) and Metekel Fogera cattle ranch. Both genetic parameter estimates and phenotypic performance traits are imperative in determining the method of selection and formulating any suitable breeding plan (Goshu et al., 2014; Kumar et al., 2017; Pires et al., 2016) and need to be worked out and updated every time. In this study, we used additional data and additional traits including pre‐weaning average daily gain (PADG), pre‐weaning KR (PKR), pre‐weaning GE (PGE) and pre‐weaning RGR (PRGR). With this regard, there is a dearth of information on PADG, PKR, PGE and PRGR traits and their associations with other growth traits of indigenous cattle in Ethiopia. Therefore, the objective of this study was to estimate the genetic parameters, phenotypic performances and phenotypic & genotypic correlations of growth and efficiency‐related traits of Fogera cattle at ALRC.

2. MATERIAL AND METHODS

2.1. Description of ALRC

Growth data collected from Fogera cattle maintained at ALRC were used in this study. ALRC is found at 587 km away from Addis Ababa, which is the capital city of Ethiopia, and 22 km from Bahir Dar city, the capital of Amhara regional state, on the way to the Blue Nile Falls. Its geographic location is 11O 29′ North and 37O 29′ East. ALRC has an elevation of 1730 m above sea level. The centre receives an average annual rainfall of 1150 mm, and temperature ranging from 6.5to 30°C.

ALRC owns a total of 365 hectares of land of which 310 hectares are covered with natural pastureland. Offices, bushes, animal housing and other infrastructures occupy the remaining 55 hectares. The centre has a topography ranging from a gentle slope to flat. Andassa River, a big and year‐round flowing river, crosses the centre. As the soil is predominantly characterised as vertisol, it holds water during the rainy season, and it cracks during the dry season when it dries. The main grass species found in the natural pastureland of the centre include Cynodon, Cetaria, Hyperhenia, Elusin, Andropogon, Paspalum, Eragrostis, Sporobulus and Trifolium species (Denekew et al., 2005).

2.2. Herd management at Andassa Livestock Research Center

The research centre, on average, owns more than 530 Fogera cattle of which about 300 are breeding cows. Natural mating is the major breeding system implemented in the centre. For this purpose, the cowherd is divided into groups of about 40–50 cows, and one bull is assigned to each group considering pedigree information to avoid the mating of closely (Son to Dam, and Sire to Daughter) related animals.

The general cattle management system at the centre is semi‐intensive. During the dry season, in addition to grazing, cattle are supplemented with hay harvested from the natural pasture and are rarely supplemented with concentrate during the mid‐dry season, January–May. Seasons are grouped into wet season (June–September) and dry season (October–May) (Bitew et al., 2010). The herd's water source is from the Andassa River. However, tape water is provided for young and sick indoor animals. Health management practices focus on prevention through vaccination. Vaccination for major prevalent diseases including blackleg, anthrax and pasteurellosis are provided to the entire herd once every 6–8 months. Internal and external parasite control measures are also provided twice a year, at the start and end of the rainy season. Calves suckle their dams for the first 4 days to ensure enough colostrum consumption. After 4 days, calves are separated from their dams during the day‐ and night‐time. During milking time in the morning and evening, calves partially suckle (two teats) their dams until weaning age of 8 months.

2.3. Data source and management

Data and traits (BWT and WWT) collected and recorded in a herd book from 2000 to 2018 were used for this study. The data were entered, filtered, cleaned and organised using MS excel software and arranged for analysis (Table 1). Information related to pedigree, traits analysed and sample sizes are presented in Table 2. After cleaning, growth rate and efficiency‐related traits: PADG, PKR, PGE and PRGR were calculated from BWT and WWT data for each individual animal record.

TABLE 1.

Descriptive statistics of the data set used for the analysis

Traits analysed N Mean SD Minimum Maximum Range
Birthweight, kg 1475 21.3 3.07 12 33 21
Weaning weight, kg 1154 98.0 22.9 33 170 137
Pre weaning average daily weight gain, g 1154 320 94.9 50 608 558
Kleiber ratio 1154 10.1 1.38 3.63 13.4 9.76
Growth efficiency 900 35.1 1.05 1.08 7.46 6.38
Relative growth rate 900 1.94 0.09 1.69 2.01 0.32
Open in a new tab

TABLE 2.

Information related to pedigree, traits analysed and sample sizes

Number of observation in each trait
Animals BWT WWT PADG PKR PGE PRGR
Number of animals 1475 1154 900 900 900 900
Number of sires 54 48 48 48 48 48
Numbers of dams 683 581 581 581 581 581
No of animals with unknown dams 71 71 71 71 71 71
No of animals with unknown sire 152 6 218 218 218 218
Dam with records and progeny 270 156 156 156 156 156
Animals with unknown sire with records 3 6 5 5 5 5
Animals with unknown dam with records 247 247 247 247 247 247
Animals with both parents unknown 3 3 2 2 2 2
Progeny per sire 21 19 19 19 19 19
Progeny per dam 2 2 2 2 2 2
Animals with paternal grand sire 161 79 78 78 78 78
Animals with paternal grand dam 114 79 78 78 78 78
Animals with maternal grand sire 245 211 210 210 210 210
Animals with maternal grand dam 190 151 153 153 153 153
Open in a new tab

PADG is growth rate to weaning age, calculated as PADG = (WWT – BWT)/240, where 240 is the number of days between birth and weaning.

PKR is the ratio of PADG to the metabolic body weight to a certain period. Here, we calculated to WWT at 8 months of age (240 days). PKR is calculated as PKR = PADG/WWT0.75 (Kleiber, 1947).

PGE is an indirect measure of GE, calculated as PGE = (WWT−BWT/BWT) × 100 (Ghafouri‐Kesbi & Gholizadeh, 2017).

PRGR = Log (WWT) – Log (BWT)/240, for 8 months (240 days) of weaning age (Ghafouri‐Kesbi & Eskandarinasab, 2018; Ghafouri‐Kesbi & Gholizadeh, 2017).

2.4. Data analysis

Phenotypic data were analysed using the general linear model (GLM) procedures of the statistical analysis system, SAS 9.1 (SAS, 2002). The fixed effects used in the model included calf birth year (2000–2018), calf birth season (dry, wet) and calf sex (male, female). Tukey Cramer test was used to separate significantly different means. The traits analysed were BWT, WWT, PADG, PKR, PGE and PRGR.

The statistical model used for the analysis of variances of fixed effects was:

Yijk=μ+Yi+Sj+Qk+eijk,

where Yijk = the observation on BWT, WWT, PADG, PKR, PGE and PRGR; μ = overall mean, Yi  = fixed effect of the ith birth year (2000–2018), Sj  = fixed effect of the jth birth season (dry, wet), Qk  = fixed effect of the kth calf sex (male, female), eijk  = residual associated with each observation.

The genetic parameters were estimated with single‐trait animal models using an average information restricted maximum likelihood (AI‐REML) method in WOMBAT software (Meyer, 2012). The log‐likelihood ratio test was performed to determine significant random effects and consequently the most appropriate model for each trait. Genetic and phenotypic correlations between the traits were estimated using pairwise trait analyses.

The statistical models for BWT, WWT, PADG, PKR, PGE and PRGR were:

Model1:y=Xb+Z1a+eModel2:y=Xb+Z1a+Z2m+e(cova,m=0)Model3:y=Xb+Z1a+Z2m+e(cova,m≠0)Model4:y=Xb+Z1a+Z2m+Z3c+e(cova,m=0)Model5:y=Xb+Z1a+Z2m+Z3c+e(cova,m≠0)

where y is the vector of records; b is the vector of fixed effects; X is an incidence matrix of fixed effects; a is a vector of direct additive genetic effect; m is a vector of maternal additive genetic effects; c is a vector of permanent environmental effects; Z 1 is an incidence matrix of direct additive genetic effects; Z 2 is an incidence matrix of maternal additive genetic effects; Z 3 is an incidence matrix of permanent environmental effects; e is a vector of residuals.

The heritabilities, genetic correlations and phenotypic correlations were estimated as h 2 = σ 2 a /σ 2 p , rG  = σaij √σ 2 aiσ 2 aj and rP  = σpij /√σ2 piσ2 pj , respectively, where h 2 is heritability, rG is genetic correlations, rP is phenotypic correlations, σ 2 a is the additive genetic variance, σ 2 p is the total phenotypic variance, σaij is the additive genetic covariance between traits i and j, σpij is the phenotypic covariance between traits i and j, σ 2 ai is the additive genetic variance for trait i, σ 2 aj is the additive genetic variance for trait j, σ 2 pi is the phenotypic variance for trait i and σ 2 pj is the phenotypic variance for trait j.

3. RESULTS

3.1. Growth performance

The overall mean BWT of Fogera calves at ALRC is presented in Table 3. The mean BWT (21.3 ± 0.05 kg) was significantly affected (p < 0.001) by calf sex; male Fogera calves were heavier than their female counterparts. Similarly, calf birth season significantly affected (p < 0.001) the BWT of Fogera calves; calves born during the wet season outweighed those born in the dry season. Calf birth year also affected (p < 0.001) the BWT of calves. The heaviest calves were born in 2006, whereas the smallest BWT was recorded in the year 2008.

TABLE 3.

Least squares means and standard errors (LSM ± SE) of growth traits of Fogera calves at Andassa Livestock Research Center, Ethiopia

BWT (kg) WWT (kg) PADG (g/day) PKR PGE PRGR
Factors N LSM ± SE N LSM ± SE N LSM ± SE N LSM ± SE N LSM ± SE N LSM ± SE
Overall 1475 21.3 ± 0.05 1154 98.0 ± 0.67 1154 320 ± 2.79 1154 10.1 ± 0.04 900 3.51 ± 0.35 900 1.95 ± 0.00
Sex of calf *** *** *** *** *** ***
Male 675 21.7 ± 0.12 536 97.8 ± 1.05 536 317 ± 4.36 536 10.0 ± 0.06 417 3.73 ± 0.47 483 1.97 ± 5.61
Female 800 20.9 ± 0.10 618 101 ± 0.85 618 334 ± 3.53 618 10.3 ± 0.05 483 3.26 ± 0.51 417 1.94 ± 6.59
Birth Season *** *** *** *** *** NS
Dry 584 21.0 ± 0.13 438 94.3 ± 1.15 438 304 ± 4.78 438 9.85 ± 0.06 326 3.46 ± 0.59 326 1.93 ± 5.02
Wet 891 21.7 ± 0.09 716 104 ± 0.82 716 347 ± 3.41 716 10.5 ± 0.05 574 3.54 ± 0.44 574 1.94 ± 6.13
Birth Year *** *** *** *** *** ***
2000 147 20.7 ± 0.24ed 137 95.1 ± 1.87e 137 310 ± 7.54f 137 10.0 ± 0.11de 114 3.61 ± 0.09d 114 1.96 ± 0.00fg
2001 79 21.9 ± 0.25cd 68 102 ± 2.37de 68 335 ± 9.75def 68 10.3 ± 0.12cd 49 3.56 ± 0.11de 49 1.98 ± 0.01defg
2002 119 21.3 ± 0.20cd 80 112 ± 2.23abc 80 376 ± 9.26bc 80 10.8 ± 0.10b 60 3.90 ± 0.09cd 60 2.01 ± 0.00bcd
2003 98 22.0 ± 0.16cd 93 84.7 ± 1.80fg 93 261 ± 7.47gh 93 9.21 ± 0.12f 80 2.84 ± 0.08g 80 1.91 ± 0.00h
2004 122 22.0 ± 0.13bc 112 87.6 ± 2.48f 112 274 ± 10.3g 112 9.24 ± 0.17f 73 2.72 ± 0.10g 73 1.89 ± 0.01h
2005 58 24.2 ± 0.22a 45 104 ± 3.41d 45 332 ± 14.1def 45 10.1 ± 0.18de 37 3.20 ± 0.13ef 37 1.99 ± 0.01cdef
2006 75 24.4±0.21a 34 118 ± 2.99ab 34 392 ± 12.5b 34 10.9 ± 0.14b 19 3.53 ± 0.12de 19 2.04 ± 0.00ab
2007 45 21.9 ± 0.50bc 25 99.4 ± 3.40de 25 325 ± 13.8ef 25 10.2 ± 0.19cd 20 3.55 ± 0.19de 20 1.97 ± 0.01efg
2008 66 16.9 ± 0.44h 61 78.7 ± 1.99g 61 258 ± 7.64gh 61 9.67 ± 0.11e 54 3.84 ± 0.12cd 54 1.88 ± 0.01h
2009 108 19.5 ± 0.18gf 95 103 ± 2.72bcd 95 349 ± 11.5cde 95 10.6 ± 0.14bc 55 3.73 ± 0.14d 55 1.95±0.01g
2010 67 20.0 ± 0.30efg 54 101 ± 1.29de 54 339 ± 5.21def 54 10.6 ± 0.07bc 51 4.15 ± 0.09c 51 1.99 ± 0.00cde
2011 64 19.4 ± 0.29fg 64 124 ± 1.94a 64 438 ± 8.13a 64 11.7 ± 0.08a 38 4.99 ± 0.15a 38 2.05 ± 0.01a
2012 66 19.0 ± 0.17g 97 106 ± 0.85cd 97 365 ± 3.34bcd 97 11.0 ± 0.03b 82 4.58 ± 0.04b 81 2.01 ± 0.00bc
2016 113 24.4 ± 0.27a 83 101 ± 2.28de 83 319 ± 9.39ef 83 9.86 ± 0.12de 71 2.94 ± 0.08fg 71 1.97 ± 0.00efg
2017 68 21.9 ± 0.41bc 63 80.8 ± 1.64fg 63 245 ± 6.70hg 63 9.00 ± 0.11f 56 2.74 ± 0.10g 56 1.89 ± 0.00h
2018 120 21.2 ± 0.23cd 43 80.0 ± 2.32g 43 241 ± 10.3h 43 8.89 ± 0.19f 41 2.71 ± 0.15g 41 1.88 ± 0.012h
Open in a new tab

N: number of observations.

***

p < 0.001: means within the same column and effect with different letters are significantly different.

Abbreviations: BWT, birthweight of calves; LSM, Least squares mean, NS, non‐significant; PADG, pre‐weaning average daily body weight gain of calves; PGE, pre‐weaning growth efficiency; PKR, pre‐weaning Kleiber ratio; PRGR, pre‐weaning relative growth rate; SE, standard error; WWT, weaning weight of calves.

The overall mean WWT was 98.0 ± 0.67 kg (Table 3). The mean WWT of female Fogera calves was greater than the WWT of their male counterparts (104 vs. 98.5 kg). The effect of calf birth season on the WWT of Fogera calves in this study was significant (p < 0.001); calves born in the wet season were heavier than those born in the dry season (105 ± 1.15 vs. 95.3 ± 0.82). Calf's birth year significantly (p < 0.001) affected the WWT of Fogera calves; the smallest WWT was recorded in 2016, and the largest was in 2011.

The overall mean PADG of Fogera calves in this study was 320 ± 2.79 (Table 3). The study showed that sex significantly (p < 0.001) affected the PADG of Fogera calves. The mean PADG of female calves was greater than for male calves (334 vs. 317 g). The effect of calf birth season on PADG of Fogera calves in this study was significant (p < 0.001). Calves born in the wet season had larger PADG than those born in the dry season (347 ± 3.41 vs. 304 ± 4.78). Similarly, calf birth year showed a significant effect on the PADG of Fogera calves.

The overall mean PKR of Fogera calves in this study (10.1 ± 0.04; Table 3) was affected by the sex of calf (p < 0.001), calf birth season (p < 0.001) and calf birth year (p < 0.001). Female calves and calves born during the wet season had superior PKR.

The overall mean PGE was 3.51 ± 0.35. Male calves and calves born in the wet season had higher (p < 0.001) PGE than female and dry season born calves, respectively. Year of birth has also affected PGE; the highest and lowest PGEs were observed in 2011 and 2018 (4.99 ± 0.15 vs. 2.71 ± 0.15), respectively.

The overall mean PRGR (1.95 ± 0.00) is presented in Table 3. Sex of calf and birth year had a significant effect on PRGR. Female calves had a higher RGR than males and the highest and the lowest PRGR of Fogera calves were recorded in 2011 and 2018 (2.05 ± 0.01 vs. 21.9 ± 0.012), respectively.

3.2. Genetic parameter estimates

Co(variance) components and heritabilities of Fogera cattle estimated using five animal models for BWT, WWT, PADG, PKR, PGE and PRGR are presented in Table 4. Based on the log‐likelihood ratio tests applied to choose the most appropriate model for each of the traits (Meyer, 2012), Model 2 was appropriate for estimating the heritability of BWT. In Model 2, the direct additive genetic and maternal genetic effects had a significant impact on BWT. The direct heritability of BWT for Fogera calves obtained in this study (0.21 ± 0.07; Table 4) was higher when the maternal effect was removed from the model.

TABLE 4.

Estimates of variance components and heritability for growth traits of Fogera cattle at Andassa Livestock Research Center, Ethiopia

Trait Models σ 2 a σ 2 m cov ( a , m ) σ 2 pe σ 2 e σ 2 p h 2 a m 2 a ram c 2 Log L
BWT 1 0.16 ± 0.13 – – – 0.34 ± 0.12 0.51 ± 0.06 0.31 ± 0.25 – – – −1746
2 1.21 ± 0.43 0.39 ± 0.21 – – 4.11 ± 0.31 5.71 ± 0.25 0.21 ± 0.07 0.06 ± 0.04 – – −1744
3 1.18 ± 0.50 0.03 ± 0.33 0.026 ± 0.33 0.37±0.32 4.12 ± 0.34 5.71 ± 0.25 0.21 ± 0.08 0.003 ± 0.05 0.04 ± 0.51 0.06 ± 0.3 −1744
4 1.21 ± 0.44 0.38 ± 0.30 – 0.001 ± 0.29 4.11 ± 0.33 5.71 ± 0.25 0.21 ± 0.07 0.066 ± 0.05 – 0.07 ± 0.2 −1744
5 1.18 ± 0.50 0.03 ± 0.30 0.26 ± 0.33 0.001 ± 0.29 4.12 ± 0.36 5.71 ± 0.25 0.21 ± 0.08 0.004 ± 0.06 0.04 ± 0.51 0.07 ± 0.3 −1744
WWT 1 93.4 ± 23.0 – – – 149 ± 18.0 242 ± 12.9 0.38 ± 0.08 – – – 2875
2 95.2 ± 26.2 94.5±12.4 – – 147 ± 18.2 242.4 ± 13 0.39 ± 0.09 0.00 ± 0.05 – – −2874
3 1.10 ± 0.27 1.04 ± 0.03 0.77 ± 0.18 1.05 ± 0.02 3.98 ± 0.32 0.27 ± 0.01 0.26 ± 0.01 – 0.72 ± 0.02 3.89 ± 0.00 −2866
4 95.2±26.2 0.00 ± 20.9 – 0.001 ± 21.1 147 ± 19.1 242 ± 13.1 0.39 ± 0.09 0.000 ± 0.09 – 4.13 ± 0.20 −2874
5 222 ± 71.5 74.6 ± 45.5 122 ± 47 5.04 ± 24.0 82.7 ± 38.8 262 ± 20.1 0.18 ± 0.21 0.28 ± 0.16 −0.94 ± 0.13 0.01 ± 0.21 −2866
PADG 1 0.10 ± 0.00 – – – 0.20 ± 0.00 0.30 ± 0.00 0.33 ± 0.00 – – – −20,026,444
2 0.00 ± 0.00 0.10 ± 0.01 – – 0.23 ± 0.00 0.51 ± 0.003 0.34 ± 0.13 0.193 ± 0.16 – – 1246
3 0.20 ± 0.00 0.10 ± 0.00 0.10 ± 0.001 – 0.202 ± 0.00 0.60 ± 0.00 0.33 ± 0.00 0.16 ± 0.00 0.71 ± 0.001 – −37,449,745
4 0.15 ± 0.00 0.2 ± 0.001 – 0.002 ± 0.12 0.20 ± 0.00 0.7 ± 0.00 0.20 ± 0.00 0.267 ± 0.00 – 0.33 ± 0.00 −32,095,9986
5 0.003 ± 0.01 0.002 ± 0.10 0.18 ± 0.01 0.001 ± 0.01 0.16 ± 0.01 0.006 ± 0.00 0.55 ± 0.19 0.32 ± 0.21 −0.70 ± 0.20 0.17 ± 0.01 1948
PKR 1 0.10 ± 0.00 – – – 0.20 ± 0.00 0.30 ± 0.00 0.33 ± 0.00 – – – −200,264,436
2 0.002 ± 0.00 0.001 ± 0.00 – – 0.001 ± 0.0 0.005 ± 0.004 0.33 ± 0.01 0.33 ± 0.01 – – −98,806
3 0.54 ± 0.20 0.30 ± 0.13 −0.35 ± 0.14 – 0.52 ± 0.11 1.02 ± 0.64 0.53 ± 0.18 0.30 ± 0.12 −0.30 ± 0.15 – −440
4 0.01 ± 0.00 0.13 ± 0.00 – 0.014 ± 0.00 0.014 ± 0.00 0.053 ± 0.00 0.205 ± 0.01 0.25 ± 0.03 – 0.26 ± 0.00 −9327
5 0.99 ± 0.02 0.06 ± 0.03 0.02 ± 0.01 – 0.15 ± 0.01 0.41 ± 0.016 0.24 ± 0.05 0.14 ± 0.07 0.24 ± 0.23 – −792
PGE 1 0.23 ± 0.007 – – – 046 ± 0.005 0.7 ± 0.003 0.33 ± 00.01 – – – −47,822
2 0.001 ± 0.00 0.001 ± 0.00 – – 0.001 ± 0.00 0.003 ± 0.00 0.33 ± 0.00 0.33 ± 0.00 – – −13,419,833
3 0.02 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 – 0.02 ± 0.00 0.06 ± 0.00 0.33 ± 0.002 0.16 ± 0.00 0.7 ± 0.08 – −648,328
4 0.002 ± 0.00 0.002 ± 0.00 – 0.002 ± 0.01 0.002 ± 0.00 0.007 ± 0.04 0.28 ± 0.001 0.28 ± 0.01 – 0.28 ± 0.01 −6,027,690
5 0.002 ± 0.07 0.002 ± 0.00 0.001 ± 0.00 0.002 ± 0.00 0.003 ± 0.05 0.01 ± 0.00 0.21 ± 0.01 0.15 ± 0.001 0.57 ± 0.04 0.2 ± 0.00 −4,458,638
PRGR 1 0.1 ± 0.00 – – – 0.3 ± 0.00 0.40 ± 0.00 0.25 ± 0.00 – – – −16,764,024
2 0.01 ± 0.00 0.001 ± 0.00 – – 0.01 ± 0.00 0.02 ± 0.00 0.50 ± 0.00 0.05 ± 0.00 – – −1,645,196
3 0.02 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 – 0.02 ± 0.00 0.06 ± 0.00 0.33 ± 0.00 0.17±0.00 0.71 ± 0.01 – −648,328
4 0.001 ± 0.00 0.002 ± 0.00 – 0.002 ± 0.00 0.002 ± 0.00 0.008 ± 0.00 0.12 ± 0.00 0.25 ± 0.00 – 0.25 ± 0.00 −6,027,690
5 0.002 ± 00 0.001 ± 0.00 0.001 ± 0.00 0.002 ± 0.00 0.003 ± 0.00 0.01 ± 0.00 0.20 ± 0.00 0.10 ± 0.00 0.58 ± 0.00 0.2 ± 0.00 −4,458,638
Open in a new tab

Abbreviations: σ 2 a , direct additive genetic variance; σ 2 m , maternal additive genetic variance; σ 2 pe , permanent maternal environmental variance; σ 2 e , residual variance; σ 2 p , phenotypic variance; h 2, direct additive heritability; m 2, maternal genetic heritability; c 2, the fraction of total variance to maternal permanent environmental effect; Log L, log‐likelihood ratio.

Note. Models in bold face are those models selected based on Log values.

The model that included the direct additive and maternal genetic effects with non‐zero covariance between direct and maternal effects (cov a , m ≠ 0), Model 3, was appropriate to explain the variation in WWT. Accordingly, the direct additive heritability value of WWT for Fogera cattle was 0.27 ± 0.00 (Table 4). The direct genetic heritability estimates obtained in this study were higher than the corresponding maternal heritabilities. The inclusion of maternal and permanent environmental effects in the model reduced the heritability of WWT from 0.39 ± 0.09 to 0.27 ± 0.00. The direct maternal genetic correlation (0.72 ± 0.02) was high and positive.

Models 5 and 3 were appropriate to estimate the heritability of PADG and PKR, respectively. Accordingly, the estimates of direct heritability (h 2 a ) for ADG and PKR were 0.55 ± 0.18 and 0.53 ± 0.17, respectively.

Model 2, which included random direct additive and maternal genetic effects, had the highest log‐likelihood value for PGE, whereas Model 4, which included random direct additive, maternal additive genetic and permanent environmental effects, was selected as the most appropriate model for PRGR. The direct heritability estimates for PGE and PRGR were 0.33 ± 0.00 and 0.12 ± 0.00, respectively.

3.3. Phenotypic and genetic correlations

Phenotypic and genetic correlations estimated from a bivariate animal model are presented in Table 5. Small and negative (−0.20 ± 0.04: BWT‐PKR) to large and positive (0.99 ± 0.00: BWT‐PGE, BWT‐PRGR, WWT‐PGE, WWT‐PRGR, PKR‐ PGE, PKR‐PRGR and PGE‐PRGR) genetic correlations were found in this study. The genetic correlations between BWT‐ADG, WWT‐PADG, PGE‐PADG and PRGR‐PADG were moderate and positive. Similar to genetic correlations, small and negative (−0.03 ± 0.20: BWT‐PKR) to large and positive (0.99 ± 0.00: BWT‐PGE, BWT‐PRGR, WWT‐PGE, WWT‐PRGR, PKR‐ PGE, PKR‐PRGR and PGE‐PRGR) phenotypic correlations were found. The phenotypic correlations between BWT‐WWT, BWT‐PADG, WWT‐PADG and PADG‐PGE traits were moderate and positive.

TABLE 5.

Estimates of genetic (above diagonal) and phenotypic (below diagonal) correlations between the studied traits

BWT WWT PADG PKR GE PRGR
BWT *** 0.22 ± 0.05 0.41 ± 0.01 −0.20 ± 0.04 0.99 ± 0.00 0.99 ± 0.00
WWT 0.49 ± 0.23 *** 0.41 ± 0.00 0.27 ± 0.01 0.98 ± 001 0.99 ± 0.00
PADG 0.41 ± 0.01 0.41 ± 0.01 *** 0.33 ± 0.02 0.40 ± 0.001 0.54 ± 0.07
PKR −0.03 ± 0.20 0.27 ± 0.03 0.30 ± 0.10 *** 0.99 ± 0.01 0.99 ± 0.01
GE 0.99 ± 0.00 0.99 ± 0.01 0.41 ± 0.005 0.99 ± 0.02 *** 0.99 ± 0.01
PRGR 0.99 ± 0.00 0.99 ± 0.01 0.96 ± 0.00 0.99 ± 0.00 0.99 ± 0.02 ***
Open in a new tab

Abbreviations: BWT, birthweight; PADG, pre‐weaning average daily weight gain; PGE, pre‐weaning growth efficiency; PKR, pre‐weaning Kleiber ratio; PRGR, pre‐weaning relative growth rate; WWT, weaning weight.

4. DISCUSSION

4.1. Growth performance

The mean BWT found in this study (21.3 ± 0.08) is similar to previous reports for the same breed (Bekele et al., 2016; Bitew et al., 2010; Tesfa and Garikipati 2014), and Ogaden cattle by Mekuriaw et al. (2009). Nevertheless, it is smaller than the values reported for Ethiopian and Kenyan Boran and Barka cattle breeds (Demeke et al., 2003; Haile, Ayalew, et al., 2011a; Haile, Joshi, et al., 2011b), and greater than the BWT of the same breed reported by Menale et al. (2011) and Horro cattle (Abera et al., 2012; Demeke et al., 2003).

A similar effect of calf sex on BWT was previously reported for Fogera and other Ethiopian cattle breeds (Bekele et al., 2016; Mekuriaw et al., 2009; Menale et al., 2011). The male superiority in BWT of calves may be attributed to hormonal differences. Male fetuses have higher androgen concentration than females, which, in turn, affects sex‐based differences in skeletal muscles. Unlike the current study, Gunawan and Jakaria (2011) reported a non‐significant effect of sex on the BWT of Bali Cattle.

Calf's birth season had a significant effect on the BWT of Fogera calves; calves born during the wet season outweighed those born in the dry season. Similar results were reported previously for the same breed (Bekele et al., 2016; Menale et al., 2011), and Bali cattle in Indonesia (Gunawan & Jakaria, 2011). This may be due to the reason that feed availability is better in the wet season than in the dry season, which helps cows to get better nutrition in the last trimester of their pregnancy. Better nutrition (protein) in the last trimester of pregnancy is indicated to improve the BWT of animals (Miguel‐Pacheco et al., 2017). The significant effect of birth year on BWT of Fogera calves may be due to the fluctuation in rainfall patterns across years, in the country in general, which, in turn, affect feed availability. It may also indicate the inconsistent and subsistent cattle management and husbandry practices in the centre. Previously, several scholars reported the effect of birth year on calf BWT (Bekele et al., 2016; Bitew et al., 2010; Menale et al., 2011).

The mean WWT (98.0 ± 0.67) was greater than other findings elsewhere in Ethiopia and in the tropics (Bekele et al., 2016; Demeke et al., 2003; Gunawan & Jakaria, 2011; Mekuriaw et al., 2009; Menale et al., 2011; Praharani 2009; Sukmasari et al., 2002) (Table 6). The study confirmed that the mean WWT of female Fogera calves was greater than the WWT of male Fogera calves (104 vs. 98.5 kg). A similar effect of calf sex on the WWT of Fogera calves was reported by previous scholars (Bekele et al., 2016). However, in contrast to the current finding, Menale et al. (2011) reported a non‐significant effect of calf sex on WWT. The unusual superiority in the WWT of Fogera female calves in this study may be due to the preferential treatment given to female calves in the centre considering them as future replacements for the herd.

TABLE 6.

Phenotypic and genetic parameter estimates of growth traits of local cattle breeds in Ethiopia

Phenotypic performances Heritability
Breed BWT (kg) WWT (kg) PADG (g) PKR PGE PRGR BWT WWT PADG PKR PGE PRGR Source
Fogera 21.3 ± 0.05 98.0 ± 0.67 320 ± 2.79 10.1 ± 0.04 3.51 ± 0.35 1.95 ± 0.00 0.21 ± 0.07 0.27 ± 0.01 0.55 ± 0.19 0.53 ± 0.18 0.33 ± 0.00 0.50 ± 0.00 Current study
Fogera 21.0 ± 0.03 88.6 ± 0.33 280 ± 0.00 – – – 0.06 ± 0.02 0.08 ± 0.03 0.06 ± 0.03 – – – (Bekele et al., 2016)
Fogera 20.7 ± 0.11 88.6 ± 1.30 297 ± 3.63 – – – – – – – – – (Menale et al., 2011)
Fogera 21.3 ± 0.09 102 ± 0.77 – – – – 0.24 ± 0.09 0.18 ± 0.05 – – – – (Tesfa & Garikipati, 2014)
Horro 22.9 ± 0.3 95.2 ± 1.3 401.4 ± 7.1 – – – 0.14 ± 0.03 0.08 ± 0.03 0.07 ± 0.02 – – – (Demeke et al., 2003)
Boran 22.9 ± 0.30 95.2 ± 1.30 – – – – – – – – – – (Demeke et al., 2003)
Boran 23.3 ± 0.36 – – – – – 0.25 ± 0.05 0.43 ± 0.04 – – – – (Haile, Ayalew, et al., 2011a; Haile, Joshi, et al., 2011b)
Barka 22.6 ± 0.50 92.0 ± 1.90 – – – – – – – – – – (Demeke et al., 2003)
Ogaden 21.5 ± 0.29 91.6 ± 1.67 – – – – – – – – – (Mekuriaw et al., 2009)
Open in a new tab

Abbreviations: BWT, birthweight; HF, Holstein Friesian; PADG, pre‐weaning average daily weight gain; PGE, pre‐weaning growth efficiency; PKR, pre‐weaning Kleiber ratio; PRGR, pre‐weaning relative growth rate; WWT, weaning weight.

The significant (p < 0.001) effect of calf birth season on the WWT of Fogera calves may be due to differences in feed availability. The better WWT of calves born during the wet season may be related to better grazing feed availability for cows that, in turn, affect milk availability for the calves at an early age. This result is similar to previous reports of Fogera cattle and elsewhere in Ethiopia as indicated in Table 6. Calf's birth year significantly (p < 0.001) affected the WWT of Fogera calves; the smallest WWT was recorded in 2016, and the largest was in 2011. This effect of birth year on calf WWT is in line with previous findings (Bekele et al., 2016; Menale et al., 2011). Similar to the BWT, the variation in WWT of calves across years may be related to the variable rainfall distribution, which, in turn, affects feed availability, and inconsistent cattle feeding management practices in the centre.

The mean PADG of Fogera cattle in this study (320 ± 2.79) was greater than the findings of the same breed in Ethiopia (Bekele et al., 2016; Menale et al., 2011). Similarly, the current result was lower than the results of Horro and Horro*Holstein Friesian crossbreeds (Abera et al., 2012; Demeke et al., 2003), respectively.

The study showed that sex significantly affected the PADG of Fogera cattle. The mean PADG of female Fogera calves was greater than for male Fogera calves (334 vs. 317 g). The current result is similar to the previous findings of (Abera et al., 2012). However, it differs from other reports (Bekele et al., 2016; Demeke et al., 2003; Menale et al., 2011; Wasike et al., 2006) (Table 6). Calf birth season affected PADG of Fogera calves; calves born in the wet season had larger PADG than those born in the dry season (347 ± 3.41 vs. 304 ± 4.78) which was in line with previous results (Bekele et al., 2016; Menale et al., 2011). Similarly, calf birth year showed a significant effect on the PADG of Fogera calves. The effect of birth year on calf WWT is in line with previous findings (Abera et al., 2012; Menale et al., 2011).

The mean PKR of Fogera cattle in this study (10.1 ± 0.04; Table 3) was affected by sex of calf, calf birth season and calf birth year. The effect of birth season and year may be associated with changes in climatic conditions, feeding and health management practices. Similar to this study, scholars reported the effect of year, season and sex of the animal on PKR in Herford cattle (Köster et al., 1994) and in sheep (Ghafouri‐Kesbi & Gholizadeh, 2017).

The mean PGE (3.51 ± 0.35) was affected by the sex of calf and season of birth that female calves and calves born in the wet season had higher (p < 0.001) GE than male and dry season born calves, respectively. Even though female calves were superior for WWT and PADG, male superiority for PGE may be due to the fact that testosterone enhances better weight gain similar to growth hormone (Zung et al., 1999). Estrogen limits the growth of bones in female calves which finally affects growth rate and GE. Similarly, calves born in the wet season had higher GE than those born in the dry season which may be due to the effective utilisation of surplus feed available in the wet season. Year of birth also affected GE, and the highest and lowest PGEs were observed in 2011 and 2018 (4.99 ± 0.15 vs. 2.71 ± 0.15), respectively. Similar to the current study, the effect of sex of animals has been reported somewhere in the literature (Ghafouri‐Kesbi & Gholizadeh 2017).

The mean PRGR (1.95 ± 0.00; Table 3) was significantly affected by the sex of calf and birth year. Female calves had a higher PRGR than males and the highest and the lowest PRGR of Fogera calves were recorded in 2011 and 2018 (2.05 ± 0.01 vs. 21.88 ± 0.012), respectively. Similar to this result, scholars (Ghafouri‐Kesbi & Gholizadeh, 2017; Kesbi & Tari, 2015) reported the effect of sex and birth year of animals on PRGR of sheep.

4.2. Genetic parameter estimates

Co(variance) components and heritability of Fogera calves estimated using five animal models for BWT, WWT, PADG, PKR, PGE and PRGR are presented in Table 4. Based on the log‐likelihood ratio tests (Meyer, 1992), Model 2 was the best model selected to estimate the heritability of BWT. The direct heritability of BWT for Fogera cattle obtained in this study was 0.21 ± 0.07. This result is in line with other findings (Haile, Ayalew, et al., 2011a; Haile, Joshi, et al., 2011b; Tesfa & Garikipati, 2014). However, the value is lower than the report by Schoeman and Jordaan (1999) for multi‐breed beef cattle in South Africa, and larger than the value previously reported for the same breed (Bekele et al., 2016; Zeleke et al., 2016) and Horro cattle by Demeke et al. (2003). The heritability estimates for the BWT of Fogera cattle recorded in this study confirmed some scope of selection responses for BWT, and it also indicated the presence of modest variation within the study population.

Model 3, which included direct and maternal additive genetic effects with non‐zero covariance between direct and maternal effects, was selected to estimate the heritability (0.27 ± 0.01) of WWT. The direct heritability estimates were higher than the corresponding direct maternal heritabilities. The lower direct maternal heritability compared to direct additive heritability for WWT could be explained by high environmental pressures and low level of management existed at WWT. The direct maternal genetic correlation was high (0.77 ± 0.18) which indicates the possibility of selection based on direct genetic effect in addition to improving the management. Similar heritability value of WWT was reported for Fogera*Holstein Friesian crosses (0.24 ± 0.07) by Zeleke et al. (2016). However, larger (Haile, Ayalew, et al., 2011a; Haile, Joshi, et al., 2011b; Schoeman & Jordaan, 1999) and smaller (Abera et al., 2012; Bekele et al., 2016; Demeke et al., 2003) direct heritability estimates of WWT were reported in the literature.

Models 5 and 3 were appropriate to estimate the heritability of PADG (0.55 ± 0.19) and PKR (0.53 ± 0.18), respectively. Unlike the current study, smaller heritability estimates of PADG (Bekele et al., 2016; Demeke et al., 2003) and PKR (Köster et al., 1994; Steyn et al., 2014) were reported. A similar larger PKR heritability value was reported by Schoeman and Jordaan (1999) for a multi‐breed beef cattle herd in South Africa. The larger direct heritability estimates of PADG and PKR indicate that these traits could be used as a guide during selection programmes for GE.

Model 3 also provided the best fit for the estimation of the heritability (0.33 ± 0.00) for PGE. In contrast to this study, smaller PGE heritability values were reported (Ghafouri‐Kesbi & Gholizadeh, 2017).

The direct heritability value of PRGR (0.50 ± 0.00) as estimated by Model 2 was larger than the corresponding maternal heritability estimate. This could indicate that PRGR depends more on individual animal performance than on maternal ability of their dams. The model included the direct additive and maternal genetic effect as a random factor. Similar larger heritability values of PRGR have been reported in the literature (Schoeman & Jordaan, 1999).

4.3. Phenotypic and genetic correlations

Phenotypic and genetic correlations measure the strength of the relationship between two performance traits. Phenotypic correlations provide an observable measure of the relationship between two traits (Bourdon, 2000; Schoeman & Jordaan, 1999; Singh et al., 2016).

The genotypic correlations estimated from a bivariate animal model ranged from small and negative (−0.20 ± 0.04) to large and positive (0.99 ± 0.00). The genetic correlations between BWT‐ADG, WWT‐PADG, PGE‐PADG and PRGR‐PADG were moderate and positive, indicating that selection for one trait would improve the other (Bourdon, 2000).

Similar to genetic correlations, small and negative (−0.03 ± 0.20) to a large and positive (0.99 ± 0.00) phenotypic correlations were found in this study. The phenotypic correlations among BWT‐WWT, BWT‐PADG, WWT‐PADG and PADG‐PGE traits were moderate and positive.

The negative and moderate phenotypic and genetic correlations between BWT‐PKRA imply that selection of Fogera cattle based on BWT will not improve the PKR and vice versa. On the other hand, larger phenotypic correlations between traits indicate the possibility of correlated selection responses. The genetic correlation between BWT and WWT was higher than the phenotypic correlation. This result was higher than the previous findings (Haile, Ayalew, et al., 2011a; Haile, Joshi, et al., 2011b; Zeleke et al., 2016). Previously, scholars reported smaller (Haile, Ayalew, et al., 2011a; Haile, Joshi, et al., 2011b; Tesfa & Garikipati, 2014), even negative (Singh et al., 2010) values of genetic correlations between BWT‐WWT. Similar (del Carmen Chin‐Colli et al., 2016; Pires et al., 2016) and higher (Schoeman & Jordaan 1999; Zeleke et al., 2016) genetic correlations have been previously reported for Fogera cattle and elsewhere in the world. The result implies that selection based on individual performance at BWT will be effective to increase PGE and PRGR traits. In contrast to the current study, Schoeman and Jordaan (1999) reported a smaller and negative genetic correlation between BWT‐PRGR and no association between BWT‐PKR. A higher correlation between PKR and feeding efficiency has been reported on lambs (Eskandarinasab et al., 2010; Talebi 2012).

5. CONCLUSIONS

The BWT and WWT performances of Fogera cattle in this study are comparable with other indigenous cattle breeds in Ethiopia. The effects of sex of calf, birth season and year of birth were significant for all the traits studied except for PRGR that was not affected by season of birth.

The moderate additive genetic variation in growth traits and large additive genetic variation in GE‐related traits indicate an opportunity for genetic improvement for these traits through selective breeding. Moreover, significant maternal effects on pre‐weaning growth and efficiency‐related traits indicated the importance of including maternal effects in genetic evaluation of traits measured early in life.

The large phenotypic and genetic correlations between most of the traits indicate the potential use of these traits to improve the growth rate of Fogera cattle. The negative phenotypic and genetic correlation between BWT‐PKRA implies that the selection of Fogera calves based on either of the traits could have an adverse effect on the other. Therefore, caution should be taken when designing the selection criteria for growth improvement.

AUTHOR CONTRIBUTIONS

DK: Conceptualisation (lead); writing—original draft (lead); formal analysis (lead); writing—review and editing (equal); MT: Conceptualisation (lead); writing—original draft (lead); formal analysis (lead); writing—review and editing (equal); DK: Conceptualisation (supporting); writing—original draft (supporting); formal analysis (supporting); writing—review and editing (equal); MT: Conceptualisation (supporting); writing—original draft (supporting); formal analysis (equal); AT: Conceptualisation (supporting); writing—original draft (supporting); formal analysis (supporting); AB: Conceptualisation (supporting); writing—original draft (supporting); formal analysis (supporting); AK: Conceptualisation (supporting); writing—original draft (supporting); formal analysis (supporting); writing—review and editing (supporting); MM: Conceptualisation (supporting); writing—original draft (supporting); formal analysis (supporting); EL: Conceptualisation (supporting); writing—original draft (supporting); formal analysis (supporting); TB: Conceptualisation (supporting); writing—original draft (supporting); formal analysis (supporting); AH: Writing—original draft (supporting); formal analysis (supporting); writing—review and editing (equal).

CONFLICT OF INTEREST

Authors clarify that there is no conflict of interest with any financial, personal or other relationships with other people or organisations related to the material discussed in the manuscript.

PEER REVIEW

The peer review history for this article is available at https://publons.com/publon/10.1002/vms3.628.

ACKNOWLEDGEMENT

The first author would like to acknowledge the Amhara Region Agricultural Research Institute (ARARI) for allowing him to work on the data through his MSc study. The authors appreciate Andassa Livestock Research Center for facilitating and supporting the research work.

Kassahun, D. , Taye, M. , Kebede, D. , Tilahun, M. , Tesfa, A. , Bitew, A. , Kebede, A. , Meseret, M. , Lakew, E. , Bimrow, T. , & Haile, A. (2022). Phenotypic and genetic parameter estimates for early growth, growth rate, and growth efficiency‐related traits of Fogera cattle in Ethiopia. Veterinary Medicine and Science, 8, 387–397. 10.1002/vms3.628

Demelash Kassahun and Mengistie Taye contributed equally to this work.

Funding information

This research received no external funding.

DATA AVAILABILITY STATEMENT

The data used for analysis during study are available from the corresponding author on reasonable request.

REFERENCES

  1. Abegaz, S. , van Wyk, J. B. , & Olivier, J. J. (2005). Model comparisons and genetic and environmental parameter estimates of growth and the Kleiber ratio in Horro sheep. South African Journal of Animal Science, 35(1), 30–40. [Google Scholar]
  2. Abera, H. , Abegaz, S. , & Mekasha, Y. (2012). Influence of non‐genetic factors on growth traits of Horro (Zebu) and their crosses with Holstein Friesian and Jersey cattle. International Journal of Livestock Production, 3(7), 72–77. [Google Scholar]
  3. Bekele, A. , Wuletaw, Z. , Haile, A. , Gizaw, S. , & Mekuriaw, G. (2016). Genetic parameter estimation of pre weaning growth traits of Fogera cattle at Metekel Ranch, Northwest Ethiopia. International Journal of Scientific Research in Science and Technology, 2(5), 15–21. [Google Scholar]
  4. Bitew, A. , Taye, M. , Kebede, A. , Mekuriaw, G. , Tassew, A. , Mulugeta, T. , & Goshu, G. (2010). Milk yield and calf growth performance of cattle under partial suckling system at Andassa Livestock Research Centre, North West Ethiopia. Livestock Research for Rural Development, 22(8). [Google Scholar]
  5. Bourdon, R. M. (2000). Understanding animal breeding. Prentice Hall. [Google Scholar]
  6. del Carmen Chin‐Colli, R. , Estrada‐León, R. J. , Monforte, J. M. , Correa, J. C. S. , & Domínguez, R. N. (2016). Genetic parameters for growth and reproductive traits of Brown Swiss cattle from Mexico. Ecosistemas y Recursos Agropecuarios, 3(7), 11–20. [Google Scholar]
  7. Demeke, S. , Neser, F. , & Schoeman, S. (2003). Variance components and genetic parameters for early growth traits in a mixed population of purebred Bos indicus and crossbred cattle. Livestock Production Science, 84(1), 11–21. [Google Scholar]
  8. Demeke, S. , Neser, F. , & Schoeman, S. (2004). Estimates of genetic parameters for Boran, Friesian, and crosses of Friesian and Jersey with the Boran cattle in the tropical highlands of Ethiopia: Milk production traits and cow weight. Journal of Animal Breeding and Genetics, 121(3), 163–175. [Google Scholar]
  9. Denekew, Y. , Tamir, B. , & Melaku, S. (2005). Effect of harvesting date on composition and yield of natural pasture in northwestern Ethiopia. Tropical Science, 45(1), 19–22. [Google Scholar]
  10. Eskandarinasab, M. , Ghafouri‐Kesbi, F. , & Abbasi, M. A. (2010). Different models for evaluation of growth traits and Kleiber ration in an experimental flock of Iranian fat‐tailed Afshari sheep. Journal of Animal Breeding and Genetics, 127, 26–33. [DOI] [PubMed] [Google Scholar]
  11. Ghafouri‐Kesbi, F. , & Eskandarinasab, M. (2018). Heritability of relative growth rate and its relationship with growth‐related traits in Afshari sheep. Gene Reports, 12, 225–229. [Google Scholar]
  12. Ghafouri‐Kesbi, F. , & Gholizadeh, M. (2017). Genetic and phenotypic aspects of growth rate and efficiency‐related traits in sheep. Small Ruminant Research, 149, 181–187. [Google Scholar]
  13. Goshu, G. , Singh, H. , Petersson, K.‐J. , & Lundeheim, N. (2014). Heritability and correlation among first lactation traits in Holstein Friesian cows at Holeta Bull Dam Station, Ethiopia. International Journal of Livestock Production, 5(3), 47–53. [Google Scholar]
  14. Gunawan, A. , & Jakaria. (2011). Genetic and non‐genetics effect on birth, weaning, and yearling weight of Bali cattle. Media Peternakan, 34(2, 93–98. [Google Scholar]
  15. Haile, A. , Ayalew, W. , Noah, K. , Tadelle, D. , & Tegegne, A. (2011). Breeding strategy to improve Ethiopian Boran cattle for meat and milk production. ILRI (aka ILCA and ILRAD).
  16. Haile, A. , Joshi, B. , Ayalew, W. , Tegegne, A. , & Singh, A. (2011). Genetic evaluation of Ethiopian Boran cattle and their crosses with Holstein Friesian for growth performance in central Ethiopia. Journal of Animal Breeding and Genetics, 128(2), 133–140. [DOI] [PubMed] [Google Scholar]
  17. Kesbi, F. G. , & Tari, A. R. (2015). Relative growth rate in sheep: Heritability and relationship with absolute growth rate and body weight. Songklanakarin Journal of Science & Technology, 37(1), 21–27. [Google Scholar]
  18. Kleiber, M. (1947). Body size and metabolic rate. Physiological Reviews, 27(4), 511–541. [DOI] [PubMed] [Google Scholar]
  19. Kleiber, M. (1961). The fire of life. An introduction to animal energetics. Wiley. [Google Scholar]
  20. Köster, E. , Van der Westhuizen, J., & Erasmus, G. (1994). Heritability estimates for different Kleiber ratios obtained from growth performance data in a Hereford herd. South African Journal of Animal Science, 24(2), 71–72. [Google Scholar]
  21. Kumar, S. , Kumar, V. , Gangaraju, G. , Nath, S. , & Thiruvenkadan, A. (2017). Estimates of direct and maternal (co) variance components as well as genetic parameters of growth traits in Nellore sheep. Tropical Animal Health and Production, 49(7), 1431–1438. [DOI] [PubMed] [Google Scholar]
  22. Mekuriaw, G. , Ayalew, W. , & Hegde, P. (2009). Growth and reproductive performance of Ogaden cattle at Haramaya University, Ethiopia. Ethiopian Journal of Animal Production, 9(1), 13. [Google Scholar]
  23. Menale, M. , Mekuriaw, Z. , Mekuriaw, G. , & Taye, M. (2011). Pre‐weaning growth performances of Fogera calves at Metekel cattle improvement and multiplication ranch, North West Ethiopia. Livestock Research for Rural Development, 23(9). [Google Scholar]
  24. Meyer, K. (1992). Variance components due to direct and maternal effects for growth traits of Australian beef cattle. Livestock Production Science, 31(3–4), 179–204. [Google Scholar]
  25. Meyer, K. (2012). WOMBAT, A program for mixed model analyses by restricted maximum likelihood. User notes. Animal Genetics and Breeding Unit, University of New England. [Google Scholar]
  26. Miguel‐Pacheco, G. , Curtain, L. , Rutland, C. , Knott, L. , Norman, S. , Phillips, N. , & Perry, V. (2017). Increased dietary protein in the second trimester of gestation increases live weight gain and carcass composition in weaner calves to 6 months of age. Animal, 11(6), 991–999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Mokhtari, M. , Razmkabir, M. , Ghiasi, H. , & Mohammadi, Y. (2019). Genetic evaluation of growth rate and efficiency related traits in Raeini Cashmere Goat. Iranian Journal of Applied Animal Science, 9(2), 275–282. [Google Scholar]
  28. Pires, B. C. , Tholon, P. , Buzanskas, M. E. , Sbardella, A. P. , Rosa, J. O. , da Silva, L. O. C. , de Almeida Torres, R. A. , Munari, D. P. , & de Alencar, M. M. (2016). Genetic analyses on body weight, reproductive, and carcass traits in composite beef cattle. Animal Production Science, 57(3), 415–421. [Google Scholar]
  29. Praharani, L. (2009). Estimation of direct and maternal weights in Bali cattle effect for weaning and yearling. Indonesian Journal of Agriculture, 2, 74–81. [Google Scholar]
  30. SAS. (2002). SAS user's guide version 9.1: Statistics, Cary, NC: SAS Institute Inc. [Google Scholar]
  31. Schoeman, S. , & Jordaan, G. (1999). Multitrait estimation of direct and maternal (co) variances for growth and efficiency traits in a multibreed beef cattle herd. South African Journal of Animal Science, 29(3). [Google Scholar]
  32. Shoja, J. , & Sarain, R. (2016). Genetic analysis and the estimates of genetic and phenotypic correlation of growth rates, Kleiber ratios, and fat‐tail dimensions with birth to yearling live body weight traits in Makuie sheep. Tropical Animal Health and Production, 48(3), 667–672. [DOI] [PubMed] [Google Scholar]
  33. Singh, H. , Pannu, U. , Narula, H. K. , Chopra, A. , Naharwara, V. , & Bhakar, S. K. (2016). Estimates of (co)variance components and genetic parameters of growth traits in Marwari sheep. Journal of Applied Animal Research, 44(1), 27–35. [Google Scholar]
  34. Singh, R. R. , Dutt, T. , Kumar, A. , & Singh, M. (2010). Estimation of direct additive genetic and maternal variance for growth traits in Vrindavani cattle. Journal of Applied Animal Research, 38(1), 145–148. [Google Scholar]
  35. Steyn, Y. , van Marle‐Köster, E. , & Theron, H. E. (2014). Residual feed intake as selection tool in South African Bonsmara cattle. Livestock Science, 164, 35–38. [Google Scholar]
  36. Sukmasari, A. H. , Noor, R. R. , Martojo, H. , & Talib, C. (2002). The estimation of breeding values and genetics trends of body weight in Bali cattle improvement project. Hayati, 9, 109–113. [Google Scholar]
  37. Talebi, M. A. (2012). Feed intake, feed efficiency, growth and their relationship with Kleiber ratio in Lori‐Bakhtiari lambs. Archiva Zootechnica, 15(4), 33–39. [Google Scholar]
  38. Tesfa, A. , & Garikipati, D. (2014). Genetic and non‐genetic parameter estimates of dairy cattle in Ethiopia: A review. Online Journal of Animal and Feed Research, 4(3), 83–90. [Google Scholar]
  39. Tesfa, A. , Kumar, D. , Abegaz, S. , Mekuriaw, G. , Bimerew, T. , Kebede, A. , Bitew, A. , Ferede, Y. , Mazengia, H. , & Tilahun, M. (2016). Growth and reproductive performance of Fogera cattle breed at Andassa Livestock Research Center. Livestock Research for Rural Development, 28(1), 1–17. [Google Scholar]
  40. Wasike, C. , Indetie, D. , Irungu, K. , Ojango, J. , & Kahi, A. (2006). Environmental effects on variation in growth and reproductive performance of Boran cattle in ASAL of Kenya. Bulletin of Animal Health and Production in Africa, 54(3), 156–167. [Google Scholar]
  41. Zeleke, B. , Kebede, K. , & Kumar, B. (2016). Estimation of genetic parameters for reproductive traits of Fogera and Holstein Friesian crossbred cattle at Metekel Ranch, Amhara region, Ethiopia. Online Journal of Animal and Feed Research, 6, 90–95. [Google Scholar]
  42. Zung, A. , Phillip, M. , Chalew, S. , Palese, T. , Kowarski, A. , & Zadik, Z. (1999). Testosterone effect on growth and growth mediators of the GH‐IGF‐I axis in the liver and epiphyseal growth plate of juvenile rats. Journal of Molecular Endocrinology, 23(2), 209–222. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data used for analysis during study are available from the corresponding author on reasonable request.

Articles from Veterinary Medicine and Science are provided here courtesy of Wiley

RESOURCES