Estimate of Genetic Parameters for Pre-Weaning Growth Traits and Kleiber Ratio in Palestinian Sheep Breeds

Abstract

Data on 1440 Awassi (AW), 2114 Assaf (AF), 261 Crossbred (XB), and 439 Improved Awassi (IA) lamb genotypes from 689 AW, 1009 AF, 105 XB, and 195 IA dams, and 152 sires, obtained from three geographical districts in the West Bank and collected between 2010 and 2015, were analyzed to estimate (co)variance components and genetic parameters for pre-weaning growth traits, including birth weight (BW), weaning weight (WW), pre-weaning average daily gain (PADG), and Kleiber ratio (KR). Explanatory statistical analyses employed the least squares method of the following factors: lamb genotype, mating type (natural mating or artificial insemination), lamb sex, and birth type, on lamb pre-weaning growth traits. The estimation of genetic parameters was performed by an animal model in Restricted Maximum Likelihood (REML) The heritability estimates for lamb traits showed that AF lambs had the highest heritability for KR 0.62, while IA lambs had a relatively high heritability for PADG 0.42. In terms of genetic correlations, the correlation between birth weight and PADG was strongest in AW 0.80, and IA lambs had a negative genetic correlation between BW and WW −0.28. For maternal genetic correlations, AF lambs exhibited a high maternal correlation between BW and WW 0.78, and AW lambs had a strong maternal correlation between PADG and KR 0.57. Phenotypic correlations were particularly strong in XB lambs between BW and KR 0.79. It was concluded that IA lambs had higher BW, and AF lambs excelled in WW and PADG. These results indicated the potential for genetic improvement in feed efficiency among AF lambs, whereas AW lambs exhibited a stronger environmental influence on growth traits.

1. Introduction

Sheep farming is an important activity in the economy and social life in rural areas of Palestine. However, it faces various challenges, including high fodder prices, complicated marketing issues, and poor management practices. Meanwhile, low sheep breed productivity is the main factor for low profitability; a solution to this challenge may be achieved through sheep breeding programs [1]. A few decades ago, Awassi sheep constituted the majority breed in Palestine, representing 68% of the total population, while Assaf and various other genotypes made up the remaining 32% [1]. Awassi sheep are characterized by their resilience in various environments, demonstrating traits such as disease resistance, long-distance grazing ability, strong flock mentality, and adaptability to different management systems [2]. Furthermore, Awassi are remarkable for their ability to thrive in tough environmental conditions, showing a strong adaptability to both limited food resources and elevated temperatures. They are also highly valued for their production of meat, milk, and wool. However, in Mediterranean regions, there are notable differences in their productivity and reproductive characteristics [3]. They are the most dominant sheep breed in Syria and other Middle Eastern countries [4,5,6] and play a significant role in Turkey’s sheep production, representing 3.5% of the total population [7]. The heavy selection process for the Awassi breed has led to an Improved Awassi genotype, which achieved the second-highest milk production after the East Friesian breed [5]. This improved dairy type of Awassi was developed during the 1930s and 1940s through intensive within-breed selection, ultimately reaching a remarkable potential of over 500 L of milk per ewe each year in an intensive production system [4]. The Assaf breed was produced by crossbreeding Improved Awassi and East-Friesian sheep [8]. This breed comprises 5/8 Improved Awassi and 3/8 East Friesian [9]. The prolificacy of Improved Awassiand Assaf ewes is relatively low to moderate: 1.28 and 1.60 lambs born per ewe lambing, respectively [10]. In recent years, the hybrid genotype has become increasingly dominant in Palestine. This genotype results from random mating between Assaf and Awassi sheep, though there it is limited information available about its production in the region [1].

Estimating genetic parameters for pre-weaning growth traits and the Kleiber ratio in Palestinian sheep is crucial for enhancing the genetic improvement of these indigenous genotypes. Pre-weaning growth traits, such as birth weight, weaning weight, and average daily gain, are essential indicators of sheep’s overall productivity and health. These traits are influenced by both genetic and environmental factors, making it imperative to understand their heritability and genetic correlations to improve breeding strategies effectively [3]. The Kleiber ratio, which reflects the efficiency of growth relative to metabolic body weight, serves as a valuable measure of growth performance in livestock [11]. A higher Kleiber ratio in sheep indicates better growth efficiency, which is vital for optimizing production systems [9]. The growth performance of sheep is crucial for the efficiency and profitability of meat production. An effective strategy to improve sheep production efficiency is to select animals based on their feed utilization efficiency. [10]. The profitability of sheep meat production can be affected by various factors, including early growth characteristics, growth rate, and the Kleiber ratio [12]. As a result, these characteristics are commonly employed as criteria for selection to enhance economic efficiency. Notably, the growth rate trait has been predominantly utilized as the primary selection criterion in various breeding programs aimed at increasing meat production [13]. In any breeding program, a crucial strategy to maximize returns in sheep breeding is to improve growth traits through selection. This can be achieved by incorporating traits such as pre-weaning growth traits and the Kleiber ratio into selection protocols [14].

Previous studies have demonstrated that genetic parameters, including heritability estimates and genetic correlations among growth traits, can significantly influence selection decisions and breeding programs [15]. For instance, moderate heritability estimates for growth traits have been reported in various sheep breeds, suggesting a potential for genetic improvement through selective breeding [16]. The estimation of variance components is influenced by various factors, including the available data size, the statistical model used, and the method employed [17]. Maternal effects are a significant source of variation in the early life traits of sheep, especially growth traits. Moreover, maternal effects that mostly account for the uterine environment provided by the dam along with nursing behavior till weaning have been given due importance apart from maternal genetic effects. However, emphasis is also given to the maternal genetic effect and its covariance with direct additive effects to understand the relationship and causal factors for the kind of correlation [18].

Incorporating dam and other environmental effects into models can lead to a more accurate estimation of variance components, as these models provide a more reliable framework for analyzing genetic data by accounting for both genetic and environmental factors, enhancing the precision of predictions [19]. To achieve optimal genetic progress in a selection program, both direct and maternal genetic components should be considered, particularly when there is an antagonistic relationship between them. Research on various sheep breeds has demonstrated that both direct and maternal genetic factors play a significant role in lamb growth, [20].

In the context of Palestinian sheep, which are adapted to local environmental conditions, understanding the genetic architecture of these traits can lead to more effective breeding programs aimed at improving growth performance and overall productivity [1]. There is no published research regarding estimates of genetic parameters, genotypic and phenotypic correlations for pre-weaning growth traits, and the Kleiber ratio in Palestinian sheep. Thus, our objective was to estimate these parameters to improve the efficiency of genetic selection.

2. Materials and Methods

2.1. Animal and Experminat Procedure

Growth traits included birth weight (BW), weaning weight (WW), pre-weaning average daily gain (PADG), and Kleiber ratio (KR). The dataset included a total of 1440 Awassi (AW), 2114 Assaf (AF), 261 Crossbred (XB), and 439 Improved Awassi (IA) lamb genotype birth records collected from 689 AW, 1009 AF, 105 XB, and 195 IA, DMA, and 152 sires between 2010 and 2015, located in rural sheep farms in three geographic regions representing the governorates of West Bank, Palestine. The crossbred genotype used in this study was a mix of Assaf and Awassi, with the crossings carried out randomly by local farmers (

Table 1. Distribution of sires, ewes, and lambs across different sheep genotypes and regions in the West Bank.

Genotype	No. of Sire	No. of Dams	Lambs per Genotype	Records per Sires	Records per Dam	Records per Family
AW	44	689	1440	32.7	2.09	1.442
AF	70	1009	2114	30.2	2.095	1.66
XB	18	105	261	14.5	2.48	1.407
IA	20	195	439	21.95	2.25	1.39
Total	152	1998	4254	27.98	2.13	1.53

AW: Awassi; AF: Assaf; XB: Crossbred; IA: Improved Awassi.

).

After birth, the lambs were immediately cleaned by licking and kept warm. They were then encouraged to suckle colostrum, the first milk produced by the ewe. The lambs were allowed to freely suckle from their mothers until weaning began. At 15 days old, creep feeding was introduced. Two weeks before weaning, the lambs gradually transitioned from relying on their mother’s milk to eating fodder. The decision to wean was based on the lambs’ weights and ages. They were weaned either when they reached 16 kg of body weight or when they were approximately 2 months old (55–65 days). The lambs were weighed at birth and weaning. The age of lambs at weaning was recorded (WA), and the PADG was calculated by subtracting BW from WW and then multiplying the difference by weaning age. The KR is an indirect selection method for measuring feed conversion. KR for pre-weaning was calculated as follows [15]: KR: PADG/WW^0.75. All records were meticulously validated and stored in an Excel spreadsheet.

2.2. Data Validation Protocols

Records and calculations for lamb growth were rigorously validated to ensure accuracy and consistency. Validation protocols included range checks to confirm values were within acceptable limits and consistency checks for logical alignment of related data points. Cross-validation compared data against historical sources to verify PADG values. Mathematical checks confirmed the accuracy of KR calculations. Data type validation ensured correct formatting, while completeness checks confirmed all required fields were filled. Outlier detection identified significant deviations, and logical consistency ensured that weight and age data were reasonable and accurate. The data validation protocol for birth weight (BW) and weaning weight (WW) was carefully designed with specific thresholds based on standard deviations (SD). For BW, data points outside 3 SD above the mean and 2 SD below the mean were excluded, while for WW, any data exceeding 3 SD above or below the mean were removed.

2.3. Statistical Analysis

The effects of environmental factors on BW, WW, PADG, and KR were analyzed using the least squares method via the LSMEANS statement in the GLM procedure. Tukey’s multiple comparison method was employed to test the significance of differences among group means. The analysis model included the effects of lamb genotype (AW, AF, XB, and IA), farm locations (Mid-West Bank, North West Bank, and South West Bank), mating type (natural mating or artificial insemination), lamb sex [F (female), M (male)], and birth type [1 (single), 2 (twin)]. BW and WA were included as covariates in the analysis of WW. In addition, BW, WA, and WW were included as covariates in the analysis of (PADG and KR. Data were analyzed using SPSS for Windows with the following model:

Y_ijklm: μ + LG_i + LOC_j + MT_k + Sex_l + TB_m1 + b1(BW) + b2(WA) + b3(WW) + e_ijklm

where Y_ijklm represents lamb growth traits, LG_i is the lamb genotype (AW, AF, XB, and IA), LOC_j is the farm location (Mid-West Bank, North West Bank, and South West Bank), MT_k represents the mating type (natural mating or artificial insemination), Sex_l is lamb sex (F: female, M: male), TB_m is the birth type (1: single, 2: twin), b1, b2b, and b3 are the regression coefficients for BW, WA, and WW, respectively, and e_ijklm are the random errors.

The fixed effects that demonstrated a significant impact during the exploratory analysis were incorporated as fixed factors into the statistical model used for estimating genetic parameters. Estimates of variance-covariance components and genetic parameters were obtained by a maternal animal model in derivative-free restricted maximum likelihood, using the software MTDFREML (Boldman et al., 1995) [21]. This model accounts for the genetic correlation between the two traits and includes maternal genetic effects. The general models for the genetic parameter estimates can be described in matrix form as

Y – Xb + Zu + Wm + e

where Y is the vector of observations (BW, WW, PADG, and KR); X is the incidence matrix relating observations to fixed effects; b is the vector of fixed effects; Z is the incidence matrix relating observations to animal genetic effects; u is the vector of random animal genetic effects (direct genetic effects); W is the incidence matrix relating observations to maternal effects; m is the vector of random maternal effects; e is the vector of residual effects. The combined model in matrix notation is

$$\left(\begin{array}{c}Y1\\ Y2\end{array}\right):\left(\begin{array}{cc}X1& 0\\ 0& X2\end{array}\right)\left(\begin{array}{c}b1\\ b2\end{array}\right)+\left(\begin{array}{cc}Z1& 0\\ 0& Z2\end{array}\right)\left(\begin{array}{c}u1\\ u2\end{array}\right)+\left(\begin{array}{cc}W1& 0\\ 0& W2\end{array}\right)\left(\begin{array}{c}m1\\ m2\end{array}\right)+\left(\begin{array}{c}e1\\ e2\end{array}\right)$$

The random effects u (direct genetic effects), m (maternal effects), and e (residual effects) are assumed to follow a multivariate normal distribution with mean zero and specific variance-covariance structures. A bi-variate model with covariance specification estimated direct and maternal genetic variances and their covariance for each trait. Then, these estimations were used to calculate their correlations.

3. Results

The factors that had a significant effect on all growth traits were genotype, geographical location, mating type, sex of the lamb, birth type, birth weight, weaning weight, and age at weaning, all showing significance with p < 0.01 or p < 0.05, depending on the trait. In contrast, genotype did not have a significant effect on average daily gain (p > 0.05). Therefore, all effects were included in the model to obtain estimates of variance components and genetic parameters (

Table 2. The influence (p-values) of the fixed-effect factors on lamb’s birth weight (BW), weaning weight (WW), pre-weaning average daily gain (PAGD), and Kleiber ratio (KR).

Factor	Trait (p-Value)
	BW	WW	PADG	KR
LG	0.000	0.000	0.413	0.000
LOC	0.000	0.000	0.000	0.000
MT	0.000	0.000	0.000	0.014
Sex	0.000	0.012	0.049	0.007
TB	0.000	0.000	0.000	0.000
BW		0.000	0.000	0.000
WW		0.045	0.000	0.000
WA			0.000	0.000

LG: Lamb Genotype; LOC: Location; MT: Mating Type; SEX: Lamb Sex; TB: Type of Birth; BW: Birth Weight; WW: Weaning Weight; WA: Weaning Age; BW: Birth Weight; WW: Weaning Weight; PADG: Pre-Weaning Average Daily Gain; KR: Kleiber Ratio.

).

When examining the lamb growth traits, the average birth weight across genotypes was 4.24 ± 0.03 kg for AF, 4.44 ± 0.07 kg for XB, and 4.16 ± 0.05 kg for AW, and the highest BW of 5.39 ± 0.07 kg was recorded for IA lambs. Geographically, lambs from the Middle region had the highest average BW of 4.84 ± 0.06 kg, while those from the North and South averaged 4.47 ± 0.03 kg and 4.37 ± 0.06 kg, respectively. Lambs born through artificial insemination were heavier (4.75 ± 0.06 kg) compared to lambs born by natural mating (4.37 ± 0.03 kg). Males had a higher BW (4.63 ± 0.04 kg) compared to females (4.49 ± 0.04 kg). Single-born lambs also had a higher average BW of 4.9 ± 0.04 kg compared to twins at 4.21 ± 0.05 kg (

Table 3. Least squares mean ± SE of lamb growth traits, and Klieber ratio by lamb genotype, location, mating type, sex, and type of birth.

LG	N	AF	N	XB	N	AW	N	IA
BW	2114	4.24 a ± 0.03	261	4.44 b ± 0.07	1440	4.16 a ± 0.05	439	5.39 c ± 0.07
WW	1797	18.5 a ± 0.12	240	17.6 b ± 0.27	1296	16.2 c ± 0.20	417	16.7 d ± 0.26
PADG	1797	0.238 a ± 0.00	240	0.238 a ± 0.001	1296	0.237 a ± 0.001	417	0.238 a ± 0.001
KR	1797	0.026 a ± 0.0	240	0.026 a ± 0.0	1296	0.024 b ± 0.0	417	0.025 c ± 0.0
LOC	N	Middle	N	North	N	South
BW	410	4.84 a ± 0.06	2419	4.47 b ± 0.03	1425	4.37 b ± 0.06
WW	362	16 a ± 0.28	2132	18.7 b ± 0.10	1256	17.12 c ± 0.20
PADG	362	0.238 a ± 0.001	2132	0.236 b ± 0.00	1256	0.239 a ± 0.001
KR	362	0.024 a ± 0.0	2132	0.026 b ± 0.0	1256	0.025 c ± 0.0
MT	N	Artificial	N	Natural
BW	561	4.75 a ± 0.06	3693	4.37 b ± 0.03
WW	497	16.7 a ± 0.23	3253	17.82 b ± 0.12
PADG	497	0.239 a ± 0.001	3253	0.238 b ± 0.001
KR	497	0.025 a ± 0.0	3253	0.025 b ± 0.0
Sex	N	Male	N	Female
BW	2336	4.63 a ± 0.04	1918	4.49 b ± 0.04
WW	2060	17.41 a ± 0.16	1690	17.16 b ± 0.16
PADG	2060	0.238 a ± 0.000	1690	0.237 b ± 0.00
KR	2060	0.025 a ± 0.0	1690	0.025 b ± 0.0
TB	N	1	N	2
BW	2916	4.9 a ± 0.04	1338	4.21 b ± 0.05
WW	2571	17.61 a ± 0.16	1179	16.63 b ± 0.17
PADG	2571	0.238 a ± 0.00	1179	0.237 b ± 0.00
KR	2571	0.025 a ± 0.00	1179	0.025 a ± 0.0

BW: Birth Weight; WW: Weaning Weight; PADG: Pre-Weaning Average Daily Gain; KR: Kleiber Ratio; N: Number of Observations; LG: Lamb Genotype; LOC: Location; MT: Mating Type; SEX: Lamb Sex; TB: Type of Birth; AW: Awassi; AF: Assaf; IA: Improved Awassi.; XB: Crossbred; Means in the same row with different letters are significantly different (p < 0.05).

).

In terms of weaning weight (WW), AF lambs had the highest average WW of 18.5 ± 0.12 kg, followed by XB (17.6 ± 0.27 kg), IA (16.7 ± 0.26 kg) and AW lambs with the lowest average WW of 16.2 ± 0.20 kg. Lambs from the North had the highest average WW of 18.7 ± 0.10 kg, while lambs from the Middle and South regions averaged 16 ± 0.28 kg and 17.12 ± 0.20 kg, respectively. Artificial insemination resulted in a higher WW (16.7 ± 0.23 kg) compared to natural mating (17.82 ± 0.12 kg). Male lambs had a higher WW (17.41 ± 0.16 kg) compared to females (17.16 ± 0.16 kg), and single-born lambs had a higher WW (17.61 ± 0.16 kg) compared to twin-born lambs (16.63 ± 0.17 kg) (Table 3).

Regarding the pre-weaning average daily gain (PADG), there were no notable differences among the genotypes, with AF, XB, AW, and IA lambs all averaging 0.238 ± 0.001 kg/day. Geographically, lambs from the Middle and South regions showed similar PADG (0.238 ± 0.001 kg/day), while lambs from the North had a slightly lower PADG (0.236 ± 0.001 kg/day). Artificial insemination led to the production of lambs with slightly higher PADG (0.239 ± 0.001 kg/day) compared to lambs produced from natural mating (0.238 ± 0.001 kg/day). Males had a slightly higher PADG (0.238 ± 0.000 kg/day) compared to females (0.237 ± 0.000 kg/day), and single-born lambs had a marginally higher PADG (0.238 ± 0.000 kg/day) compared to twins (0.237 ± 0.000 kg/day) (Table 3).

As for the Kleiber ratio (KR), AF and XB lambs showed similar averages of 0.026 ± 0.000, while AW lambs averaged 0.024 ± 0.000 and IA lambs averaged 0.025 ± 0.000. Lambs from the Middle region had an average KR of 0.024 ± 0.000, while those from the North and South regions averaged 0.026 ± 0.000 and 0.025 ± 0.000, respectively. Artificial insemination led to a slightly higher KR (0.025 ± 0.000) compared to natural mating (0.025 ± 0.000). Males and single-born lambs had a higher KR (0.025 ± 0.000) compared to females and twins, which averaged 0.025 ± 0.000 and 0.025 ± 0.000, respectively (Table 3).

In our study, the variance components and heritability estimates for BW, WW, PADG, and KR across different genotypes were analyzed. The estimates of additive genetic variance (σa²), maternal genetic variance (σm²), and the environmental proportion of total variance (c²), along with heritability estimates, are provided in

Table 4. Estimates of genetic parameters for birth weight (BW), weaning weight (WW), pre-weaning average daily gain (PADG), and Kleiber ratio (KR) across lamb genotypes.

Trait	Genotype	Parameter
		σa2	σm2	σam	σp2	ram	h2	hm2	c2
BW	AW	1.67 ± 0.06	1.48 ± 0.25	0.98 ± 0.06	5.04 ± 0.19	0.58 ± 0.02	0.33 ± 0.01	0.29 ± 0.01	0.06 ± 0.01
	AF	±0.57 ± 0.02	1.32 ± 0.02	1.95 ± 0.04	2.45 ± 0.03	0.22 ± 0.02	0.23 ± 0.01	0.54 ± 0.01	0.15 ± 0.01
	IA	0.31 ± 0.02	1.26 ± 0.05	0.10 ± 0.05	2.14 ± 0.06	0.16 ± 0.02	0.15 ± 0.02	0.59 ± 0.03	0.22 ± 0.02
	XB	1.95 ± 0.06	0.55 ± 0.04	0.23 ± 0.02	1.71 ± 0.06	0.36 ± 0.044	0.43 ± 0.04	0.32 ± 0.045	0.11 ± 0.047
WW	AW	2.37 ± 0.07	2.93 ± 0.09	1.14 ± 0.06	14.33 ± 0.08	0.43 ± 0.014	0.17 ± 0.009	0.20 ± 0.010	0.55 ± 0.016
	AF	4.57 ± 0.05	2.36 ± 0.03	1.54 ± 0.03	14.92 ± 0.09	0.47 ± 0.012	0.31 ± 0.08	0.16 ± 0.01	0.43 ± 0.014
	IA	7.54 ± 0.11	2.93 ± 0.07	1.95 ± 0.07	18.10 ± 0.22	0.41 ± 0.03	0.42 ± 0.03	0.16 ± 0.02	0.31 ± 0.02
	XB	1.78 ± 0.09	14.33 ± 0.25	1.43 ± 0.08	22.92 ± 0.32	0.28 ± 0.02	0.08 ± 0.01	0.63 ± 0.03	0.23 ± 0.02
PADG	AW	1.68 ± 0.24	1.88 ± 0.035	1.42 ± 0.11	9.40 ± 0.35	0.95 ± 0.01	0.18 ± 0.01	0.15 ± 0.01	0.51 ± 0.01
	AF	3.07 ± 0.03	1.39 ± 0.02	0.80 ± 0.02	3.98 ± 0.05	0.39 ± 0.01	0.46 ± 0.01	0.21 ± 0.01	0.21 ± 0.01
	IA	68.53 ± 0.35	30.17 ± 0.23	−19.09 ± 0.17	15.99 ± 0.50	−0.17 ± 0.02	0.45 ± 0.02	0.20 ± 0.02	0.40 ± 0.02
	XB	3.27 ± 0.09	2.77 ± 0.08	2.43 ± 0.07	14.11 ± 0.18	0.81 ± 0.028	0.23 ± 0.046	0.20 ± 0.046	0.40 ± 0.043
KR	AW	1.52 ± 0.06	1.41 ± 0.06	1.37 ± 0.05	8.83 ± 0.07	0.84 ± 0.01	0.17 ± 0.01	0.16 ± 0.01	0.51 ± 0.01
	AF	14.20 ± 0.04	1.69 ± 0.03	1.08 ± 0.02	23.49 ± 0.43	0.22 ± 0.01	0.60 ± 0.01	0.07 ± 0.00	0.28 ± 0.01
	IA	2.29 ± 0.06	1.80 ± 0.05	1.27 ± 0.05	7.13 ± 0.10	0.62 ± 0.02	0.31 ± 0.02	0.25 ± 0.02	0.25 ± 0.02
	XB	3.19 ± 0.10	1.78 ± 0.09	1.10 ± 0.07	6.82 ± 0.18	0.56 ± 0.03	0.32 ± 0.03	0.26 ± 0.02	0.26 ± 0.02

BW: Birth Weight; WW: Weaning Weight; PADG: Pre-Weaning Average Daily Gain; KR: Kleiber Ratio; AW: Awassi; AF: Assaf; IA: Improved Awassi; XB: Crossbred; σa²: direct additive genetic variance; σm²: maternal genetic variance; σam: covariance between direct additive genetic effect and maternal genetic effect; σp²: total phenotypic variance; r_am: correlation between direct additive genetic effect and maternal additive genetic effect; h²: direct heritability; m²: maternal heritability; c²: the environmental proportion of total variance.

.

For BW, the direct heritability (h²) was highest in XB lambs at 0.43 ± 0.043 and lowest in IA lambs at 0.15 ± 0.02. The maternal heritability (m²) was highest in IA lambs at 0.59 ± 0.03 and lowest in AF lambs at 0.23 ± 0.01. In the case of WW, direct heritability estimates ranged from 0.08 ± 0.01 in XB lambs to 0.42 ± 0.03 in IA lambs. Maternal heritability was highest in IA lambs at 0.16 ± 0.02 and lowest in AF lambs at 0.16 ±0.01.

For PADG, direct heritability estimates varied from 0.81 ± 0.028 in XB lambs to 0.95 ± 0.01 in AW lambs. The highest maternal heritability was observed in IA lambs at 0.45 ± 0.02, while the lowest was in XB lambs at 0.23 (±0.046). Regarding KR, direct heritability was highest in AW lambs at 0.84 ± 0.01 and lowest in AF lambs at 0.22 ± 0.01. Maternal heritability estimates ranged from 0.31 ± 0.02 in IA lambs to 0.60 (±0.01) in AF lambs.

The genetic, maternal, and phenotypic correlations of pre-weaning lamb growth traits and the Kleiber ratio were estimated and are presented in

Table 5. Genetic, maternal, and phenotypic correlations between pre-weaning growth traits and Kleiber ratio across lamb genotypes.

Trait	Genotype	Correlations
		σa1a2	σm1m2	σp1p2	ra	rm	rρ
BW&WW	AW	0.64 ± 0.03	1.63 ± 0.05	3.44 ± 0.04	0.32 ± 0.014	0.78 ± 0.020	0.40 ± 0.02
	AF	0.61 ± 0.02	1.02 ± 0.02	4.65 ± 0.05	0.38 ± 0.00	0.58 ± 0.00	0.76 ± 0.012
	IW	−0.44 ± 0.08	1.48 ± 0.07	4.10 ± 0.22	−0.28 ± 0.04	0.77 ± 0.03	0.15 ± 0.09
	XB	1.12 ± 0.07	1.26 ± 0.08	5.00 ± 0.15	0.60 ± 0.04	0.44 ± 0.01	0.79 ± 0.02
BW& PADG	AW	1.34 ± 0.06	0.8 ± 0.07	4.46 ± 0.33	0.80 ± 0.02	0.59 ± 0.01	0.65 ± 0.01
	AF	1.06 ± 0.02	1.08 ± 0.02	2.65 ± 0.05	0.80 ± 0.01	0.79 ± 0.01	0.67 ± 0.009
	IW	2.74 ± 0.35	−2.02 ± 0.23	−15.99 ± 0.50	0.59 ± 0.02	−0.33 ± 0.02	−0.108 ± 0.022
	XB	0.95 ± 0.05	0.60 ± 0.04	4.06 ± 0.09	0.37 ± 0.037	0.60 ± 0.038	0.82 ± 0.02
BW & KR	AW	1.34 ± 0.05	0.90 ± 0.05	4.59 ± 0.05	0.84 ± 0.01	0.33 ± 0.01	0.689 ± 0.007
	AF	0.92 ± 0.02	1.29 ± 0.03	5.47 ± 0.10	0.32 ± 0.00	0.89 ± 0.02	0.72 ± 0.01
	IW	0.66 ± 0.06	1.32 ± 0.05	5.23 ± 0.10	0.32 ± 0.02	0.87 ± 0.02	0.25 ± 0.06
	XB	1.64 ± 0.08	0.31 ± 0.08	2.16 ± 0.15	0.78 ± 0.04	0.31 ± 0.04	0.63 ± 0.04
WW & PADG	AW	0.38 ± 0.018	1.58 ± 0.027	5.09 ± 0.047	0.79 ± 0.000	0.67 ± 0.000	0.439 ± 0.015
	AF	2.23 ± 0.04	0.24 ± 0.01	4.31 ± 0.08	0.59 ± 0.07	0.13 ± 0.09	0.56 ± 0.02
	IA	1.71 ± 0.05	1.72 ± 0.05	5.45 ± 0.09	0.075 ± 0.02	0.18 ± 0.02	0.056 ± 0.004
	XB	0.73 ± 0.06	1.69 ± 0.09	5.50 ± 0.16	0.30 ± 0.05	0.26 ± 0.04	0.30 ± 0.02
WW & KR	AW	0.99 ± 0.021	0.93 ± 0.020	4.60 ± 0.045	0.52 ± 0.000	0.73 ± 0.000	0.409 ± 0.018
	AF	−0.59 ± 0.03	1.29 ± 0.02	3.55 ± 0.06	−0.073 ± 0.09	0.68 ± 0.07	0.19 ± 0.02
	IA	0.92 ± 0.04	1.64 ± 0.05	4.79 ± 0.06	0.22 ± 0.16	0.71 ± 0.12	0.054 ± 0.007
	XB	1.46 ± 0.07	1.53 ± 0.07	6.36 ± 0.10	0.61 ± 0.07	0.30 ± 0.07	0.50 ± 0.03
PADG & KR	AW	0.99 ± 0.027	0.93 ± 0.017	5.59 ± 0.057	0.62 ± 0.022	0.57 ± 0.014	0.62 ± 0.017
	AF	1.52 ± 0.05	0.65 ± 0.03	5.01 ± 0.12	0.23 ± 0.00	0.38 ± 0.06	0.52 ± 0.04
	IA	1.91 ± 0.05	−0.3750 ± 0.03	4.98 ± 0.05	0.15 ± 0.11	−0.05 ± 0.012	0.063 ± 0.008
	XB	1.05 ± 0.05	1.23 ± 0.06	5.44 ± 0.10	0.32 ± 0.05	0.55 ± 0.05	0.55 ± 0.07

BW: Birth Weight; WW: Weaning Weight; PADG: Pre-Weaning Average Daily Gain; KR: Kleiber Ratio; AW: Awassi; AF: Assaf; IA: Improved Awassi; XB: Crossbred; σa1a2: covariance between direct additive genetic for trait 1 and trait 2; σm1m2: covariance between maternal genetic effect for trait 1 and trait 2; σp1p2: covariance between phenotypic effect for trait 1 and trait 2; ra: correlation between direct additive genetic effect of trait1 and trait 2; rm: correlation between maternal genetic effect of trait1 and trait 2; rρ: phenotypic correlation.

. In the analysis, genetic correlations between BW and WW ranged from 0.32 to 0.60 across different genotypes. The AW genotype had a direct additive genetic correlation of 0.32, while the AF genotype correlated with 0.38. For the IW genotype, a negative correlation of −0.28 was observed, and the XB genotype had a higher direct genetic correlation of 0.60.

The maternal genetic correlations for BW and WW ranged from 0.44 to 0.78. Specifically, AW showed a correlation of 0.78, AF 0.58, IW 0.77, and XB 0.44. Phenotypic correlations between these traits also varied, with the AW at 0.40, the AF at 0.76, the IW at 0.15, and the XB at 0.79.

For BW and PADG, direct additive genetic correlations ranged from 0.59 to 0.80. AW and AF exhibited high correlations of 0.80, whereas IA showed a negative correlation of −0.33. XB showed a correlation of 0.37. Maternal correlations for BW and PADG were 0.59 for AW and 0.79 for AF, with a notable negative value of −0.33 in IA and 0.60 in XB.

Regarding BW and KR, the direct genetic correlations ranged from 0.32 to 0.84. The AW had a high direct correlation of 0.84, AF 0.32, IA 0.32, and XB 0.78. Maternal correlations ranged from 0.31 to 0.89, with AW at 0.33, AF at 0.89, IW at 0.87, and XB at 0.31. Phenotypic correlations varied from 0.25 to 0.79, with the AW at 0.69, AF at 0.72, the IA at 0.25, and the XB at 0.63.

For WW and PADG, direct genetic correlations ranged from 0.30 to 0.79, with AW at 0.79, AF at 0.59, IA at 0.075, and XB at 0.30. Maternal correlations ranged from 0.13 to 0.67, with AW at 0.67, AF at 0.13, IW at 0.18, and XB at 0.26.

Lastly, the correlations between PADG and KR ranged from 0.15 to 0.62 for direct additive genetic effects. AW had a direct genetic correlation of 0.62, AF 0.23, IA 0.15, and XB 0.32. Maternal correlations were between −0.05 and 0.57, with AW at 0.57, AF at 0.38, IA at −0.05, and XB at 0.55. Phenotypic correlations ranged from 0.30 to 0.62.

4. Discussion

4.1. Birth Weight (BW)

The higher BW of IA lambs is consistent with previous research by Milan et al. [22], who also reported higher average BWs for Awassi lambs. Ghafouri-Kesbi et al. [23] found similar trends, indicating that genetic selection for growth traits in IA lambs has led to improved BW. Additionally, Adile and Sinan [24] reported that environmental factors, such as nutrition and management practices, significantly influence BW, which aligns with the current study findings regarding regional variations. The lower BW observed in AF lambs may be attributed to the higher incidence of multiple births in this genotype. The absence of genetic improvement in the Awassi genotype could potentially explain the observed decrease in BW. The significant influence of location on BW found in this study aligns with previous studies, such as those conducted by Haile et al. [25], which also observed variations in BW across different regions.

Additionally, artificial insemination decreased BW. This finding is supported by Jawasreh et al. [26], who emphasized the role of breeding methods in influencing lamb growth. Furthermore, the higher BW of single lambs compared to twin-born lambs was consistent with the findings of Azzam et al. [27], who highlighted differences in BW based on the type of birth.

The study demonstrated that lamb BW was influenced by the intricate interactions between genetic growth potential, environmental adaptability, and breeding method efficiency, with significant variations observed across different genotypes. Compared to AF and AW lambs, IA lambs had higher BW and were recognized for having superior genetic growth potential. The reduced BW noted in AF lambs was attributed to their increased twinning rates, which limited the potential for individual growth. Despite this, AF lambs had the highest WW, probably because of their favorable genetic composition, which promoted better growth and development after birth. The absence of focused breeding programs aimed at improving this trait is likely what caused the comparatively lower BW of AW lambs.

Twins added more total weight to the flock than single-born lambs, even though birth type had an impact on growth rates, with single-born lambs typically growing faster than twins. Growth was also positively influenced by regional environmental factors, such as those found in the Middle region; however, artificial insemination, typically using genetically improved sires, produced higher BW than natural mating. Birth weights were further influenced by biological characteristics like growth differences related to sex. It has been imperative to combine breed-specific genetic traits with customized environmental and management practices to maximize lamb growth and enhance flock performance. Breeding IA and AF lambs and using AI from superior sires, along with adjusting management techniques to local conditions and controlling birth type, all improved flock performance and growth results.

4.2. Weaning Weight (WW)

The research highlighted significant variations in weaning weight among different lamb genotypes. AF lambs had higher WW compared to AW lambs found by Jawasreh et al. [26]; this supports the current study’s results. The WW of IA lambs is consistent with previous research by Adile and Sinan [24], who reported higher WW for AW lambs than other breeds. Furthermore, the study by Haile et al. [25] revealed that Turkish and Syrian AW sheep exhibited higher WW compared to the findings of this study. The location also played a significant role in WW, with lambs in the North region exhibiting the highest average weaning weight. This finding is aligned with prior research emphasizing geographical differences in WW Barazandeh et al. [28]. The study demonstrated that natural mating resulted in a significantly higher average weaning weight compared to artificial mating. This result is supported by the research of Adile and Sinan [24], highlighting the influence of mating type on WW. Male lambs had slightly higher average WW than female lambs, and single-born lambs had significantly higher average WW compared to twin-born lambs. These results align with the existing literature, such as the study by Petrovic et al. [29], which indicates the impact of birth type on WW and daily gains in lambs.

The AF genotype with high WW demonstrated successful genetic selection for growth traits, whereas IA lambs with lower WW showed a mismatch between their genetic potential and the current management or environmental circumstances. Because the sires used in natural mating were more suited to the local environment than those used in artificial insemination, which originated from farms with different environments, the study showed that natural mating produced higher WW than artificial insemination. There was also an influence from biological characteristics; single-born lambs had higher individual WW because they had more prenatal resources. Even though single-born lambs grew faster on their own, twins contributed more to the flock weight overall, suggesting that twins offered higher overall productivity.

4.3. Pre-Weaning Average Daily Gain (PADG)

The result is in agreement with the study conducted by Milan et al. [22], which reported a slightly higher PADG of 0.26 kg/day for AF lambs. Mohammadi et al. [30] also noted that crossbred lambs tend to have higher PADG compared to purebred lambs, which aligned with the current study’s observations. However, XB and AW lambs displayed slightly lower growth rates compared to pure AF lambs, indicating the influence of genotype on PADG. Location significantly affected PADG. This outcome aligned with previous research emphasizing the impact of geographical location on lamb growth rates and is supported by Ghafouri-Kesbi et al. [23], who found that environmental conditions in certain regions can enhance growth rates. Jawasreh et al. [26] reported a lower pre-weaning daily gain of 0.20 kg/day for AW lambs, highlighting variations in growth rates across different locations and genotypes. The study also revealed that lambs produced through artificial insemination and male lambs showed slightly higher PADG compared to natural mating and female lambs, respectively. Additionally, single-born lambs demonstrated a slightly higher PADG compared to multiple-born lambs, underscoring the impact of birth type on growth rates.

4.4. Kleiber Ratio (KR)

The KR, which is a measure of the efficiency of growth, exhibited slight variations across different genotypes, locations, mating types, sexes, and types of birth. The KR for AF lambs was 0.026, which was higher than that observed in previous studies for other genotypes, such as Muzaffarnagar sheep (0.01637) by Dass et al. [31] and Baharat Merino sheep (0.01764) by Mallick et al. [32]. The KR is influenced by both genetic and environmental factors, as noted by Mohammadi et al. [30], who found that higher KR values are associated with better feed conversion efficiency in lambs.

When KR was analyzed across lamb breeds and conditions, AF lambs had higher feed conversion efficiency than AW lambs consistently across locations, mating types, and sex, indicating a strong genetic trait for growth. Selecting higher KR values in breeding programs can enhance feed conversion efficiency.

4.5. Genetic Parameters

4.5.1. Birth Weight (BW)

The heritability estimates for BW across genotypes revealed considerable genetic variation. IA lambs had the lowest direct heritability, suggesting a limited genetic influence on BW due to management or genetic diversity. In contrast, XB lambs showed the highest heritability, indicating strong genetic control, making them suitable for selection. Maternal heritability was high in IA, highlighting the significant role of maternal traits in BW. Environmental factors had minimal influence across all genotypes, suggesting their limited impact on BW variation. Previous studies have reported varying heritability estimates for BW in different sheep genotypes. Specifically, Behrem [33] found heritability estimates for BW in Central Anatolian Merino sheep to be around 0.20, which aligns with the lower estimates observed in the IA genotype. Conversely, Zhang et al. [34] reported higher heritability estimates for BW in other genotypes, highlighting that genetic potential for this trait may vary significantly across different genetic backgrounds.

Although environmental factors are important, their impact on BW is not as great as that of genetic factors. This makes it possible for breeding strategies to concentrate more on genetic improvements, especially in XB, which demonstrated a strong genetic basis for BW, and IA, where genetic factors contribute less to BW variation. Even though IA has a lower heritability for BW, outcomes can be improved by concentrating on enhancing management practices and including maternal traits in selection criteria. The high genetic heritability of XB suggests that focusing on genetic improvement will be especially successful in boosting BW.

4.5.2. Weaning Weight (WW)

Heritability estimates for WW revealed varying genetic and environmental influences. AW lambs had low direct heritability, suggesting limited genetic variation or management practices hindering effective selection for WW. In contrast, IA lambs exhibited higher heritability, indicating better potential for genetic improvement through selection. XB lambs had notably high maternal heritability, emphasizing the strong impact of maternal traits like milk yield on WW. Environmental factors had an influence on AW lambs, highlighting the significant role of nutrition and management in their growth when compared with previous studies like Safari et al. [35], who reported heritability estimates for WW in various sheep genotypes, with values ranging from 0.20 to 0.40, which supports the findings for IA but highlights the challenges in AW.

To address the low heritability and high environmental proportion of AW, efforts should be directed toward optimizing growth conditions and improving management practices. Environmental effects can be lessened by enhancing healthcare and nutrition, and heritability can be increased through crossbreeding to increase genetic diversity. On the other hand, because of its higher heritability, IA presents a significant opportunity for genetic improvement. Dedicated breeding initiatives should be put in place to select for characteristics that improve WW while maintaining a focus on improving management techniques and improving maternal traits for the XB genotype, which exhibits high maternal heritability, as these factors have a major impact on WW.

4.5.3. Pre-Weaning Average Daily Gain (PADG)

Heritability estimates for PADG showed moderate values across genotypes, with AF lambs exhibiting the highest direct heritability, making them a strong candidate for genetic selection to improve PADG. IA lambs also showed high heritability, indicating similar potential for genetic improvement. AW lambs had the highest environmental influence, highlighting the significant role of factors like feed availability, quality, and management in determining PADG. Previous research has indicated that heritability estimates for PADG can vary widely, with estimates reported by Kumar et al. [36] showing values around 0.40 in Harnali sheep, which is consistent with the findings for the IA and AF genotypes in this study.

Given their high estimates of direct heritability, targeted breeding programs should prioritize the AF and IA genotypes to improve PADG. By applying genetic selection techniques to these genotypes, PADG may significantly improve. To take advantage of the AF genotype’s superior genetic potential, concentrate on selective breeding. Likewise, utilize focused breeding techniques to capitalize on the high heritability of the IA genotype for maximum growth. On the other hand, the AW genotype’s comparatively high environmental proportion emphasizes the necessity of addressing environmental factors affecting PADG. For this genotype, growth rates can be enhanced by reducing environmental influences and enhancing feed quality, availability, and general management practices.

4.5.4. Kleiber Ratio (KR)

Heritability estimates for KR vary across genotypes, with AF lambs showing the highest direct heritability, indicating significant genetic influence on growth efficiency and making them ideal for selection programs targeting feed efficiency. In contrast, AW lambs have lower heritability, suggesting limited potential for genetic improvement in growth efficiency, possibly due to low genetic diversity or less effective management practices. The environmental proportion (c²) is consistent across genotypes, demonstrating that while environmental factors play a role in KR, they are not the primary influence. In line with these findings, Safari et al. [35] noted that heritability estimates for feed efficiency traits in sheep can vary, with some genotypes showing higher potential for genetic improvement than others. For IA, which has lower direct heritability, improving maternal health and implementing selective breeding strategies are essential. For AW, prioritizing genetic selection and improving environmental conditions, such as pasture quality and supplemental feeding, are crucial. For AF and crossbred (XB) lambs, enhancing growth efficiency through targeted genetic selection, optimizing feed quality, and improving overall animal welfare will maximize production outcomes.

4.6. Genetic, Maternal, and Phenotypic Correlation

4.6.1. Birth Weight (BW) and Weaning Weight (WW)

The genetic correlation between BW and WW in the AW genotype indicates a moderate positive genetic relationship, aligning with studies like Safari et al. [35], who found genetic correlations between BW and WW in Merino sheep around 0.25 to 0.35, and Notter [37], who reported correlations ranging from 0.20 to 0.40 in various sheep genotypes. The maternal genetic correlation in AW is higher, reflecting a stronger influence of maternal effects, consistent with Safari et al. [35] and Maria et al. [38], who reported high maternal genetic correlations in Romanov sheep. The phenotypic correlation for AW is moderate, similar to Safari et al. [35] and Notter [37], who found phenotypic correlations between 0.30 and 0.50.

For the AF genotype, the genetic correlation between BW and WW is slightly higher, with moderate maternal and phenotypic correlations. These findings point towards strong genetic and maternal influences on the relationship between BW and WW, similar to the values observed in Merino sheep by Safari et al. [35] and Romanov sheep by Maria et al. [38]. In contrast, the IA genotype displayed a negative genetic correlation, indicating an inverse genetic relationship, while the maternal correlation remained high. This negative genetic correlation, though less common, has been observed in specific studies on crossbreeds where environmental factors play a significant role, as noted by Notter [37]. In the XB genotype, the genetic correlation between BW and WW is the highest, with a moderate maternal correlation and a high phenotypic correlation. These results suggest strong genetic and phenotypic relationships between these traits in crossbreeds.

For the AW, the moderate genetic correlation between BW and WW suggested that targeted breeding strategies could effectively improve both traits simultaneously. Strengthening maternal traits would have improved offspring growth even more due to the strong maternal genetic correlation. For the AF, the significant genetic correlation between BW and WW highlighted the importance of focusing on both traits in breeding programs to achieve significant growth improvements. To optimize breeding outcomes, the IA genotype’s negative genetic correlation necessitated the refinement of selection criteria as well as a thorough investigation into environmental factors influencing this relationship. In contrast, the high genetic correlation between BW and WW in XB allowed for significant improvements in both traits through breeding efforts. The moderate maternal correlation in XB indicated that, while maternal effects were important, achieving the best results required balancing them with genetic advancements.

4.6.2. Birth Weight (BW) and Pre-Weaning Average Daily Gain (PADG)

In the AW genotype, the strong genetic correlation between BW and PADG indicated a significant genetic linkage. For instance, Safari et al. [35] reported genetic correlations for growth traits in Merino sheep to be around 0.70 to 0.85. Similarly, Ekiz et al. [39] observed genetic correlations in Kivircik lambs to be around 0.75 to 0.82, further reinforcing the strong genetic relationship seen in the AW. The maternal correlation in the AW is moderately high, which is in line with the maternal correlations of 0.50 to 0.70 reported by Safari et al. [35] and the range of 0.55 to 0.68 found by Maria et al. [38] in Romanov sheep.

The phenotypic correlation in the AW genotype also aligns well with the range reported in these studies, indicating substantial genetic and maternal influences on PADG. In the AF genotype, the high genetic and maternal correlations between BW and PADG highlight robust contributions from both genetic and maternal factors to PADG.

These findings are consistent with the results of studies on other genotypes, such as the values reported by Notter [37]. For Suffolk and Polypay sheep, genetic correlations for pre-weaning growth traits were found to be similarly high, around 0.75 to 0.85. The phenotypic correlation in the AF genotype aligns with those observed in other genotypes, reinforcing the significant genetic and maternal influences on PADG. In contrast, IA showed a positive genetic correlation but a negative maternal correlation. While negative maternal correlations are less common, they have been noted in specific cases where environmental factors significantly influence maternal effects. For instance, Notter [37] observed that in certain crossgenotypes, maternal correlations could be negative due to specific management practices or environmental conditions. The genetic correlation of 0.59 is within the typical range for various sheep genotypes, which is generally between 0.50 and 0.70, as reported by Safari et al. [35] and Ekiz et al. [39].

The following recommendations are proposed to improve breeding strategies by taking advantage of the correlations observed between BW and PADG. For AW, strong genetic correlations between BW and PADG necessitate ongoing selection to leverage this genetic linkage. Additionally, improving maternal management, particularly in nutrition and health, is essential to support genetic gains in PADG. For AF, both strong genetic and maternal correlations suggest a dual strategy: selecting for superior growth traits and enhancing maternal qualities. This approach maximizes the benefits of both genetic and maternal influences on PADG. In contrast, IA positive genetic correlation coupled with a negative maternal correlation indicates that while genetic selection can enhance PADG, it is crucial to address and mitigate the adverse maternal effects through targeted environmental and management improvements.

4.6.3. Birth Weight (BW) and Kleiber Ratio (KR)

In the AW genotype, the strong genetic correlation of 0.84 between BW and KR suggests that genetic selection for BW could effectively enhance KR, aligning with findings from Mohammadi et al. [30], which reported a similar correlation of 0.80 in Iranian sheep genotypes. The moderate maternal correlation and high phenotypic correlation further emphasize the importance of both genetic and maternal influences on KR. Conversely, the AF genotype’s lower genetic correlation but high maternal correlation indicates a substantial maternal effect on KR, consistent with Abegaz et al. [40], who also noted significant maternal contributions to growth traits in sheep.

In the present study, findings for IA highlight the crucial role of maternal effects, with a high maternal correlation and a moderate genetic correlation. To optimize KR, it is essential to consider both genetic and maternal influences. For AW, selecting for BW, which has a strong genetic correlation with KR, is recommended to indirectly improve KR. Enhancing genetic variation and maternal nutrition can further improve KR. In AF, where a high maternal correlation is observed, improving maternal management, health, and nutrition should be a primary focus. Given the lower genetic correlation, combining genetic selection with robust maternal care is likely to be more effective. For IA, with its strong maternal influence and moderate genetic correlation, breeding strategies should integrate comprehensive maternal management practices alongside genetic selection to maximize KR. This approach ensures that both maternal and genetic factors are addressed for optimal KR outcomes.

4.6.4. Weaning Weight (WW) and Pre-Weaning Average Daily Gain (PADG)

When examining WW and PADG, the AW genotype’s strong genetic correlation and moderately high maternal correlation imply that both genetic and maternal factors significantly influence PADG. This is in line with findings from Prakash et al. [41], who reported similar correlations in other sheep genotypes. In contrast, the AF genotype’s lower genetic correlation (0.59) and very low maternal correlation (0.13) implied that while genetic factors are relevant, maternal influences are less pronounced. The IA genotype’s very low correlations across the board suggest weak associations, which is supported by findings from Mohammadi et al. [30] that identified low heritability estimates for similar traits.

Given the AF decreased genetic and minimal maternal influences, focusing exclusively on genetic selection is likely to generate better results. Improving genetic composition associated with PADG should be the primary goal, with less emphasis placed on maternal variables. To solve the poor correlation in the IA between Weaning Weight (WW) and Pre-weaning Average Daily Gain (PADG), which are characterized by very low correlations, the selection criteria should be reconsidered, and new genetic lines may be added. Furthermore, we should research alternate techniques or managerial abilities that may be introduced.

4.6.5. Weaning Weight (WW) and Kleiber Ratio (KR)

The analysis of WW and KR in the AW genotype shows a moderate genetic correlation, coupled with a high maternal correlation, indicating significant maternal influence on KR. In contrast, the AF genotype exhibits a negative genetic correlation, highlighting that maternal effects dominate in this context. It was also noted by Mohammadi et al. [30] in their study of various sheep genotypes. The XB genotype’s strong genetic correlation, combined with moderate maternal and phenotypic correlations, indicates a balanced contribution from both genetic and maternal factors. To optimize growth and reproduction, tailor strategies for each genotype. For AW, improve maternal care to enhance KR through better nutrition and health treatments. For AF, focus on boosting the maternal environment to offset negative genetic effects. For XB, use a balanced approach that combines genetic improvement with maternal care to achieve optimal results.

4.6.6. Pre-Weaning Average Daily Gain (PADG) and Kleiber Ratio (KR)

In the AW genotype, PADG and KR exhibit significant genetic, maternal, and phenotypic influences. The AF genotype shows a balanced effect from genetic and maternal factors. The IA genotype has very low genetic and maternal correlations, as well as a low phenotypic correlation. The XB genotype displays moderate influences from genetic, maternal, and phenotypic factors. Overall, AW higher correlations reflect significant genetic and maternal impacts, while AF demonstrates a balanced influence, which parallels findings in the Limousin and Charolais genotypes where genetic variability in KR allows for satisfactory genetic gains without adversely affecting BW (Rezende et al.) [42].

To optimize PADG and KR across sheep breeds, targeted strategies are essential. For AW, focus on enhancing genetic selection and maternal care, including nutrition and health. For AF, balance genetic improvements with better maternal management. IA requires overall herd management and environmental improvements due to weak correlations. For XB, maintain high maternal care and environmental conditions while combining these with effective genetic selection to improve PADG and KR.

5. Conclusions

In conclusion, the study reveals significant impacts of genotype on lamb growth traits. The Improved Awassi genotype shows higher birth weights than the local Awassi. Assaf lambs demonstrate superior weaning weights and pre-weaning average daily gain. Regional variations also play a critical role in influencing BW and WW, indicating that targeted strategies based on location can enhance growth. Breeding methods, particularly artificial insemination, lead to better lamb weights compared to natural mating. Additionally, birth type and lamb sex are significant factors, with males and single-born lambs showing better growth. The high heritability of the Kleiber ratio in Assaf lambs suggests potential for genetic improvements in feed efficiency, while lower heritability in Awassi lambs points to a stronger environmental impact. These insights are crucial for designing effective breeding programs and improving pre-weaning growth traits and KR in sheep. Further research into environmental and maternal effects is recommended for optimizing genetic progress.