Genetic Evaluation of Reproductive and Productive Traits in Zaraibi Goats Under Tropical Climatic Conditions

Simple Summary

Zaraibi goats play a crucial role in enhancing food and nutritional security and improving household income in Egypt. This study evaluated the genetic potential of Zaraibi goats—an important source of milk and meat for smallholder farmers—by analyzing 1888 kids raised under hot, tropical conditions. Key traits examined included litter size at birth, birth weight, weaning weight, and average daily gain. The findings revealed moderate to high heritability for growth and productivity traits, suggesting strong genetic control. These results highlight the potential for selective breeding to enhance meat production, strengthen food security, and improve the goats’ adaptability to heat stress, supporting farmers’ resilience in the face of climate change and limited resources.

Abstract

Understanding the genetic and phenotypic basis of economically important traits is essential for designing effective breeding programs in livestock. This study aimed to evaluate the phenotypic performance and estimate genetic parameters for one reproductive trait—litter size at birth (LSB)—and three pre-weaning growth traits—birth weight (BW), weaning weight (WW), and average daily gain (ADG)—in a population of 1888 Zaraibi kids born between 2018 and 2023. Genetic parameters were estimated using animal models implemented in the MTDFREML software. The overall least squares means (±standard error) for LSB, BW, WW, and ADG were 2.22 ± 0.02, 2.03 ± 0.01 kg, 10.22 ± 0.05 kg, and 90.00 ± 0.50 g/day, respectively. Statistical analyses indicated that month of birth, year of birth, and type of birth had significant (p < 0.05) effects on all traits, while the sex of the kids had no significant effect (p > 0.05) on LSB. Direct heritability estimates (h²_d) derived from Model 1 (including additive genetic, permanent environmental, and residual effects) were 0.13 ± 0.01 for LSB, 0.30 ± 0.04 for BW, 0.38 ± 0.01 for WW, and 0.30 ± 0.10 for ADG. Under Model 2 (which incorporated maternal genetic effects and their covariance with direct genetic effects), maternal heritability (h²_m) estimates for LSB, BW, WW, and ADG were 0.05 ± 0.01, 0.15 ± 0.01, 0.12 ± 0.01, and 0.14 ± 0.01, respectively. Overall, the results emphasize the importance of maternal genetic effects in influencing pre-weaning growth traits. Therefore, maternal genetic components should be explicitly considered in genetic evaluation and selection strategies aimed at improving early growth performance in Zaraibi goats.

1. Introduction

Goat production plays a significant role in Egypt’s livestock economy, particularly among smallholder farmers. With an estimated population of 1.5 million goats, about 100,000 smallholders depend on them as a vital source of income [1]. Among indigenous breeds, Zaraibi and Baladi goats are the most prominent. Zaraibi goats, also known as Zaribi or Zaraayeb, are particularly valued for their dual-purpose characteristics—milk and meat production—making them central to both subsistence and commercial farming systems in Egypt compared to Damascus [2].

The Zaraibi goats are recognized as Egypt’s leading dairy breed, primarily due to their high milk yield, prolific reproductive performance, and adaptability to varying environmental conditions [3]. Under optimal conditions, these goats can produce 1.5 to 2.5 kg of milk daily, with lactation periods lasting up to 250 days. This translates into a total milk yield of 270 to 450 kg per lactation, with even higher outputs recorded in organized farms. Their meat production potential is also notable: at birth, kids weigh around 2.5–3.0 kg, reach 10–14 kg by weaning at three months, and achieve 20–25 kg by six months under Sudanese conditions [4].

Reproductive efficiency is another defining trait of the Zaraibi breed. They reach sexual maturity between 12 and 15 months and have a typical kidding interval of 7 to 9 months, allowing for up to three kiddings within two years [5]. Average litter sizes range from 1.7 to 2.2 kids, with twins and triplets commonly observed when nutritional needs are met. Notably, Zaraibi goats exhibit non-seasonal breeding behavior, enabling year-round reproduction—a trait that supports continuous production cycles in intensive and semi-intensive systems [6]. Efforts aimed at improving Zaraibi productivity have emphasized genetic selection, particularly for milk yield and reproductive traits [7]. Selective breeding, combined with enhanced management practices such as optimized feeding, reproductive planning, and disease control, has demonstrated the potential for substantial productivity gains. However, for such strategies to be successful, it is imperative to understand the genetic architecture of economically important traits. Estimating genetic parameters—such as heritability and genetic correlations—provides insight into the extent of variation attributable to genetics and informs breeding decisions. Studies conducted on Zaraibi goats at the Nubaria Experimental Station have estimated moderate heritability for daily milk yield (0.27), suggesting that selective breeding can effectively enhance this trait [7]. Conversely, litter size at birth showed low heritability (0.12), consistent with its strong environmental dependency. The age at first kidding showed a heritability estimate of 0.21, indicating potential for genetic improvement through targeted selection.

Growth traits in Zaraibi goats have also been studied. A report estimated heritability values of 0.36 for birth weight, 0.28 for weaning weight, and 0.25 for body weight at six months [8], all of which support the use of selection for meat production improvement. Genetic correlations between traits have further refined breeding strategies. For example, a positive genetic correlation between milk yield and litter size (r_g = 0.34) suggests that selecting for milk yield may also enhance fertility. However, a negative correlation between ages at first kidding and litter size warns of potential trade-offs, underlining the need for balanced selection approaches [7]. More recent research employing animal models and restricted maximum likelihood (REML) methods has provided multi-trait estimates for milk yield, lactation length, and reproductive traits [9]. These studies emphasize the importance of using estimated breeding values (EBVs) and robust performance recording systems to support genetic improvement in Zaraibi herds. Collectively, available evidence underscores that Zaraibi goats possess favorable genetic attributes for productivity and reproduction, making them suitable candidates for structured genetic improvement programs. Improving reproductive and growth performance is critical for maximizing returns from goat farming. Traits such as birth weight and litter size at birth are important selection criteria, as they influence early survival and growth performance, which are essential for both meat and milk production. Heritability estimates for these traits vary, with reported values for litter size at birth ranging from 0.02 to 0.08 and for growth traits between 0.18 and 0.42 [10,11,12,13,14,15,16]. These suggest that while some traits are influenced by environmental or maternal factors, there remains significant genetic variability to enable progress through selection. Notably, maternal effects have a considerable influence on early growth traits such as pre-weaning weight [17,18,19], highlighting the need to incorporate both direct and maternal effects in breeding models. Given this background, the current study was undertaken to estimate phenotypic traits and genetic parameters for litter size at birth, birth weight, weaning weight, and average daily gain from birth to weaning in Zaraibi goats raised in Egypt. Using animal models accounting for both direct additive and maternal genetic effects, the study aims to identify traits with sufficient genetic variability which is critical for developing appropriate breeding strategies to enhance productivity, reproductive efficiency, and climate resilience. We hypothesize that genetic variation exists within the Zaraibi goat population for reproductive and growth traits and that accounting for both direct and maternal effects in genetic models will improve the accuracy of selection strategies. The outcomes of this work are expected to support evidence-based breeding programs for sustainable improvement in smallholder and commercial goat systems in Egypt.

2. Materials and Methods

2.1. Data and Animal Management

The data utilized in this study were obtained from performance records of Zaraibi goats maintained at the Serwa Experimental Station, located in the Dakahlia Governorate of Egypt. This station, operated by the Animal Production Research Institute (APRI) under the Ministry of Agriculture, is situated in Serwa Village near Sherbin (31.2520° N latitude, 31.5370° E longitude) and lies within a semi-arid Mediterranean climatic zone. The facility provides suitable conditions for year-round breeding, nutritional trials, and performance evaluations. The dataset comprised records of 1888 kids born between 2018 and 2023. Animals were housed in semi-enclosed pens and received seasonal feeding: berseem (Trifolium alexandrinum) during winter and spring, and a combination of crop stubble, berseem hay, and/or sorghum fodder (Sorghum bicolor) during summer and autumn. The does were able to produce three parities within a two-year cycle. The traits evaluated included litter size at birth (LSB) as a reproductive trait, and birth weight (BW), weaning weight at 90 days (WW), and average daily gain from birth to weaning (ADG) as production traits. In Egypt, Zaraibi goats typically mate from August to October. The photoperiod, ambient temperature, and feed availability of the area all affect this seasonal breeding pattern.

2.2. Data Analysis

The parameters of birth weight (BW), weaning weight (WW), and average daily gain (ADG) were examined using a linear mixed model that included fixed effects of birth month, birth year, sex of kids, and type of birth, as well as random variables of sires (bucks), dams (does), and residual error. The random effect of dams accounted for maternal variables, including both genetic and permanent environmental effects. The following linear mixed model was utilized: Y_ijklmno = µ + S_i + d_ij + M_k + Y_l + se_m + t_n + e_ijlmno; where: Y_ijklmno = the performance variable, µ = overall mean, S_i = random effect of the ith bucks, d_ij = random effect of the jth does within the ith bucks, M_k = fixed effect of the k the month of birth, k = 2, 3, 4, 11 and 12, Y_l = fixed effect of year of kidding, l = 2018, 2019, 2020, 2021, 2022, 2023.se_m = fixed effect of sex, m = 1 or 2, t_n = fixed effect of the nth type of birth, m = 1, 2, 3 or 4, and e_ijklmno = random error with mean zero and variance σ²_e. Dam age was not included as a fixed effect due to the unavailability of age data in the dataset, though the random effect of does partially accounted for maternal variation. Birth months were restricted to February, March, April, November, and December because these correspond to the seasonal kidding periods of Zaraibi goats, driven by estrus cycles in late summer (September–October) and early summer (June–July).

For the analysis of litter size at birth (LSB), the same model was applied, with the exception that the effect of birth type (t_n) was excluded due to its inherent correlation with the response variable.

Estimation of Variance Components and Genetic Parameters

All traits analyzed—litter size at birth (LSB), birth weight (BW), weaning weight (WW), and average daily gain (ADG)—were evaluated using a Multiple Trait Animal Model (MTAM) implemented via the MTDFREML software version 3.1 [20]. Two distinct multi-trait animal models were employed. Model 1 accounted for the fixed effects of birth month and year, sex of kids, and type of birth, along with the random effects of animal genetic merit, permanent environmental effects, and residual error. In matrix notation, the model 1 was expressed as: y = Xb + Zg + Mm + Wp + e; where: y = observation vectors of animals, b = fixed effect vectors (month and year of birth, sex of kids and type of birth), g = animal genetic vector, p = permanent effect vector, e = error effect vectors and Z and W are incidence matrices. For Model 2, the model includes the main effects of month and year of birth, sex of kids, and type of birth, and the random effects of animals, maternal genetic effects, permanent environmental effects, and errors. In matrix notation, the following model 2 was used: y = Xb + Zu + Wm + Sp_e + e; where: y = Vector of observations, Xb = vector of main effects (month and year of birth, sex of kids and type of birth), Zu = vector of random animal effects, Wm = vector of random maternal (indirect) genetic effects, Sp_e = vector of permanent environmental effects, e = vector of random residual effects and X, Z, W and S are incidence matrices relating records to fixed, animal, maternal genetic and permanent environmental effects, respectively. Estimates of direct heritability (h²ₐ), maternal heritability (h²_m), genetic correlations (r_g), and phenotypic correlations (r_g) were derived using MTDFREML. This software utilizes the Restricted Maximum Likelihood (REML) method within the mixed linear model framework to estimate variance and covariance components. By partitioning genetic variance into direct (individual) and maternal (dam) components, REML provides accurate and unbiased estimates of genetic parameters, particularly critical for traits such as birth and weaning weights. The resulting covariance matrices from the multi-trait analyses were used to compute both genetic and phenotypic correlations among traits. These estimates are vital for designing selection strategies that aim to improve multiple traits concurrently without compromising genetic progress.

3. Results and Discussion

3.1. Productive and Reproductive Performance of Zaraibi Goats

The unadjusted means for the traits evaluated in this study are presented in

Table 1. Unadjusted means for litter size at birth (LSB), birth weight (BW), weaning weight (WW), and average daily gain from birth to weaning (ADG) in Zaraibi goats (n = 1888).

Variables	Mean	SE *	SD *	CV%
LSB	2.32	0.02	0.62	26.72
BW (kg)	2.03	0.01	0.35	17.24
WW (kg)	10.22	0.05	2.09	20.45
ADG (g/day)	90.00	0.50	20.00	22.22

* SE—Standard error of mean, * SD—Standard deviation, CV—Coefficient of Variability.

. The overall means for litter size at birth (LSB), birth weight (BW), weaning weight (WW), and average daily gain (ADG) from birth to weaning were 2.32 ± 0.02, 2.03 ± 0.01 kg, 10.22 ± 0.05 kg, and 90.00 ± 0.50 g, respectively. The mean LSB observed in this study aligns with the range previously reported by other researchers [9,13,21]. In comparison, a study conducted on Zaraibi goats in Egypt [22] recorded lower mean LSB values, with figures of 1.60 in Zaraibi, 1.59 in Baladi, and 1.21 in Damascus goats [2], indicating a relatively higher LSB in the current population. The BW, WW, and ADG values reported in this study also fall within the ranges documented for various goat breeds in the earlier literature. For instance, a study involving 626 Zaraibi does reported an average WW of 10.7 kg [14], while another study on a different Zaraibi population noted BW and WW averages of 2.0 kg and 10.7 kg, respectively [23]. Similarly, BW, WW, and ADG values in Anglo-Nubian kids were 2.55 kg, 13.19 kg, and 87.14 g, respectively, whereas Baladi kids recorded values of 2.25 kg, 10.19 kg, and 64.74 g [17]. Nonetheless, the mean values for productive traits observed in this study are generally lower than those reported in several studies conducted on different goat breeds across various environmental and management conditions globally. In general, the Average Daily Gain (ADG) usually fluctuates with the type of birth, frequently reflecting variations similar to those seen in birth weight (BW) and weaning weight (WW). Single-born children typically exhibit greater BW and WW than twins or triplets, since they obtain more nutrients during pregnancy and encounter reduced competition for maternal resources post-birth. As a result, singletons generally show greater ADG because of improved initial body condition and milk availability, whereas multiples typically demonstrate reduced ADG due to their relatively lower birth and weaning weights. Comprehending these connections aids in handling nutritional needs and growth expectations based on birth type.

The coefficients of variation (CV%) for the traits evaluated in this study ranged from 17.24% to 26.72% (Table 1), aligning with values reported in previous studies [17,24,25]. For instance, in Jamunapari goats, CVs of 22.84% and 23.93% were reported for birth weight (BW) and weaning weight (WW), respectively [24], while a CV of 31.08% was recorded for WW in Jakhrana goats [25]. The comparatively lower CV observed for BW in the current study may reflect the limited influence of environmental factors on this trait. In contrast, the slightly higher CVs noted for WW and average daily gain (ADG) (Table 1) indicate greater phenotypic variability in postnatal growth traits, which are of economic significance. The observed differences between the present results and those of earlier studies may be attributed to variations in genetic background, environmental conditions, management systems, and the statistical methodologies employed.

3.2. Effect of Non-Genetic Factors

The least-squares means for the factors influencing various traits in Zaraibi goats are presented in

Table 2. Least-squares means (±SE) for the production and reproduction traits of Zaraibi goats.

Effects	N	LSB	BW (kg)	WW (kg)	ADG (g/day)
Overall mean	1888	2.22 ± 0.02	2.03 ± 0.01	10.22 ± 0.05	90.00 ± 0.50
Month of birth		**	**	**	**
February	311	2.09 ± 0.10 a	1.85 ± 0.09 b	9.93 ± 0.64 b	88.94 ± 0.60 a
March	422	2.23 ± 0.02 a	2.00 ± 0.02 a	10.49 ± 0.14 ab	93.99 ± 0.20 b
April	434	2.10 ± 0.06 ab	1.99 ± 0.03 a	9.83 ± 0.31 ab	87.20 ± 0.30 a
November	350	2.06 ± 0.02 ab	2.04 ± 0.02 a	11.15 ± 0.18 a	100.55 ± 0.20 b
December	371	1.81 ± 0.10 b	2.02 ± 0.05 a	9.92 ± 0.78 ab	84.37 ± 0.80 a
Year of birth		**	**	**	**
2018	332	2.24 ± 0.05 ab	1.88 ± 0.03 c	9.67 ± 0.25 a	80.61 ± 0.30 a
2019	298	2.19 ± 0.06 b	1.96 ± 0.03 b	10.41 ± 0.26 b	91.99 ± 0.30 b
2020	349	2.34 ± 0.06 b	2.13 ± 0.03 a	10.81 ± 0.28 b	91.50 ± 0.30 b
2021	297	2.13 ± 0.06 a	2.04 ± 0.03 a	10.45 ± 0.29 b	92.21 ± 0.30 b
2022	205	1.87 ± 0.06 c	1.92 ± 0.03 b	10.50 ± 0.27 b	93.10 ± 0.30 b
2023	96	1.57 ± 0.08 d	1.95 ± 0.04 a	10.55 ± 0.30 b	92.50 ± 0.30 b
Sex		ns	**	**	**
Male	871	2.24 ± 0.05	2.06 ± 0.03 a	10.89 ± 0.25 a	90.68 ± 0.30 a
Female	706	2.24 ± 0.05	1.90 ± 0.03 b	9.64 ± 0.25 b	80.51 ± 0.30 b
Type of birth			**	**	**
Single	955		2.28 ± 0.03 a	12.08 ± 0.32 a	109.11 ± 0.40 a
Twins	437		2.03 ± 0.02 b	10.39 ± 0.22 b	94.42 ± 0.20 b
Triplets	145		1.82 ± 0.03 c	10.36 ± 0.23 b	92.41 ± 0.20 bc
Quadruplets	40		1.80 ± 0.05 c	9.54 ± 0.43 c	84.79 ± 0.30 c

All values are reported as least-squares mean ± standard error (SE). Different superscripts (a–d) in the same column indicate statistically significant differences (** p < 0.01); ns = not significant. LSB—Litter size at birth, BW—Birth weight, WW—Weaning weight, ADG—Average daily gain.

. Both month and year of birth had a statistically significant effect on all traits examined (p < 0.05 or p < 0.01). These findings are consistent with those reported by previous studies across different goat breeds and geographical locations [12,16,26,27,28]. No consistent trend was observed in the effect of birth year, which may be attributed to fluctuations in management practices across years, including the culling of aged or unproductive does, changes in feeding regimes, and varying levels of heat stress [29]. Furthermore, earlier studies have suggested that the significant influence of non-genetic factors on growth traits may be partially explained by inter-annual differences in environmental conditions and feeding systems. When analyzing the impacts of month and year on the performance of animals, it is more reasonable to focus on biological and managerial aspects instead of trusting statistical significance. Seasonal changes in temperature can cause cold or heat stress which has a direct influence on feed intake, growth, and reproduction efficiency. For example, summer months are associated with high temperatures which can lead to a decrease in fertility or milk production due to heat stress. Cold months are also associated with higher maintenance energy requirements. Alongside these factors, feed quantity and quality tend to change seasonally. Animals’ nutritional status fluctuates depending on whether it is the dry or rainy season. Disease and parasitic infections also impact overall health and productivity in different parts of the year. Changes in management practices such as breeding, feeding, and health measures may also differ by month or year and cause the changes that have been observed. Changes in performance from year to year may be the result of changing forage quality or environmental conditions, with farm management changes such as altered nutrition or housing leading to improved performance. Blending these explanations alongside statistical explanations provide a satisfying comprehension of the animal traits under consideration.

The sex of the kids had a highly significant effect (p < 0.01) on birth weight (BW), weaning weight (WW), and average daily gain (ADG), but no significant influence (p > 0.05) on litter size at birth (LSB) (Table 2). As shown in Table 2, male kids were significantly heavier than female kids for BW, WW, and ADG (p < 0.01). Similarly, the type of birth significantly affected all traits studied (p < 0.01), with single-born kids exhibiting higher BW, WW, and ADG compared to twins, triplets, and quadruplets. These findings are in agreement with previous studies conducted on different goat breeds across various regions [9,12,17,26].

The higher birth weights observed in single-born kids, as opposed to multiple-born counterparts, may be attributed to limited uterine space and competition for nutrients during gestation in multiple pregnancies. In Beetal goats in Pakistan, for example, birth type was identified as a significant source of variation in BW (p < 0.01), with single-born kids averaging 3.69 kg, compared to 3.37 kg in twins and 3.08 kg in triplets [26]. Similarly, a study on Norduz goats in Turkey reported that single and twin kids had average birth weights of 3.0 kg and 2.8 kg, and weaning weights of 20.0 kg and 19.2 kg, respectively [27].

These findings underscore the importance of non-genetic factors, such as sex and birth type, as critical sources of variation in litter size at birth, birth weight, and weaning weight in Zaraibi goats under Egyptian management and environmental conditions.

3.3. Genetic Parameters

3.3.1. Heritability Estimates

The current heritability (h²) estimate for litter size at birth (LSB), as presented in

Table 3. Estimates of direct heritability (h²_d) (±SE) on diagonal, genetic (±SE) (below diagonal) and phenotypic (above diagonal) associations for different variables (Model 1).

Parameters	LSB	BW	WW	ADG
LSB	0.13 ± 0.01 *	0.80 ± 0.14	0.88 ± 0.18	0.80 ± 0.19
BW	0.09 ± 0.10	0.30 ± 0.04 *	0.90 ± 0.10	0.24 ± 0.11
WW	0.24 ± 0.10	0.40 ± 0.05	0.38 ± 0.01 *	0.80 ± 0.11
ADG	0.45 ± 0.10	0.93 ± 0.10	0.82 ± 0.10	0.30 ± 0.10 *

* The diagonal elements in bold are heritability estimates.

, aligns with the range previously reported for various goat breeds. In Zaraibi goats, an earlier study utilizing an animal model reported a direct heritability of 0.08 for LSB, suggesting that genetic improvement through selection for this trait may require considerable time [13]. Other estimates for LSB heritability in Zaraibi goats, derived using different animal models, ranged from 0.07 to 0.21 [9]. Variations in heritability estimates across studies are likely attributable to differences in estimation methods, sample size, and statistical models employed.

Moawed and Shalaby [30], in their study on Zaraibi kids (n = 600) using three animal models, found that direct heritability estimates for LSB were consistently higher than maternal heritability across all models, ranging from 0.09 to 0.21. In a separate study on Markhoz goats, direct heritability was moderate for pre-weaning traits—0.22 for birth weight (BW) and 0.16 for weaning weight (WW)—but low for reproductive traits, with an estimate of 0.01 for LSB [15]. Similarly, Menezes et al. [31], working on Boer goats (n = 1300) in Brazil, reported heritability estimates for reproductive traits near zero for LSB, while growth traits showed moderate heritability, ranging from 0.23 to 0.31.

The current estimates of direct heritability (h²_d) for birth weight (BW), weaning weight (WW), and average daily gain (ADG) fall within the range reported by various authors [12,16,17,23]. Oudah et al. [12], in a study involving Egyptian Zaraibi kids (progeny of 17 sires and 296 dams from 1999 to 2000) using a multi-trait animal model (MTAM), reported moderate to high h²_d values for BW, ranging from 0.272 to 0.422. Similarly, Shaat et al. [23] estimated direct heritability values (115 bucks and 1387 does) of 0.21 for BW, 0.16 for WW, and 0.33 for ADG in Zaraibi kids. In a comparative analysis involving 573 Anglo-Nubian and 318 Baladi kids using four different animal models, heritability estimates for Anglo-Nubian kids ranged from 0.36 to 0.58 for BW, 0.01 to 0.38 for WW, and 0.08 to 0.46 for ADG [17]. For Baladi kids, the corresponding ranges were 0.26 to 0.45 for BW, 0.28 to 0.39 for WW, and 0.21 to 0.31 for ADG. Furthermore, a study conducted on Markhoz goats in Kurdistan reported direct heritability estimates of 0.29 for BW and 0.15 for WW [16].

The present findings suggest that genetic improvement for certain reproductive traits, such as litter size at birth (LSB), may be challenging due to the low heritability and high influence of non-genetic factors. Therefore, enhancing reproductive performance, particularly LSB, may be more effectively achieved through improved management practices. These include better nutrition, heat detection, timely insemination using high-quality semen, and overall reproductive management. Notably, litter size can be enhanced through nutritional strategies such as flushing, which involves feeding does additional concentrates 3 to 4 weeks before the breeding season. This practice increases ovulation rates, thereby raising the likelihood of twinning and triplet births. In contrast, the moderate heritability estimates observed for birth weight (BW), weaning weight (WW), and average daily gain (ADG) indicate that genetic progress in growth traits up to weaning is achievable. These traits can be improved through appropriate selection strategies targeting both bucks and does.

Estimates of direct and maternal heritability derived using Animal Model 2 are presented in

Table 4. Estimates of heritability (±SE) on diagonal, genetic (±SE) (below diagonal) and phenotypic (above diagonal) correlations for productive and reproductive traits as estimated (Model 2).

Trait	Direct h2d				Maternal h2m
	LSB	BW	WW	ADG	LSB	BW	WW	ADG
LSB	0.07 ± 0.02	0.82 ± 0.01	0.87 ± 0.01	0.84 ± 0.01	0.05 ± 0.02	0.08 ± 0.01	0.04 ± 0.01	0.12 ± 0.01
BW	0.66 ± 0.02	0.22 ± 0.01	0.93 ± 0.02	0.28 ± 0.02	0.02 ± 0.01	0.15 ± 0.01	0.13 ± 0.01	0.24 ± 0.01
WW	0.46 ± 0.01	0.38 ± 0.01	0.22 ± 0.01	0.84 ± 0.02	0.07 ± 0.01	0.10 ± 0.01	0.12 ± 0.01	0.40 ± 0.01
ADG	0.61 ± 0.01	0.92 ± 0.10	0.81 ± 0.10	0.25 ± 0.01	0.10 ± 0.01	0.15 ± 0.01	0.10 ± 0.01	0.14 ± 0.01

standard error (SE).

. The direct heritability (h²_d) estimates were 0.07 ± 0.02 for litter size at birth (LSB), 0.22 ± 0.01 for birth weight (BW), 0.22 ± 0.01 for weaning weight (WW), and 0.25 ± 0.01 for average daily gain (ADG). Corresponding maternal heritability (h²_m) estimates were 0.05 ± 0.02, 0.15 ± 0.01, 0.12 ± 0.01, and 0.14 ± 0.01 for LSB, BW, WW, and ADG, respectively. These findings are in agreement with previous studies conducted on different goat breeds. For example, Roy et al. [24], working with Jamunapari goats, reported a decline in maternal heritability of body weight from 0.10 at birth to 0.08 at weaning. Similarly, Boujenane and El Hazzab [10], using Draa goats, observed maternal heritability estimates ranging from 0.00 to 0.24 for body weights recorded at various ages (birth, 30, 60, and 180 days). Meza-Herrera et al. [18], evaluating five goat strains in Mexico, found maternal heritability estimates for birth weight ranging from 0.154 to 0.278. In a study of 1300 kids and approximately 750 reproductive records from 345 Boer goats in Brazil, Menezes et al. [31] estimated maternal heritability for birth weight at approximately 0.13. Furthermore, Yogesh et al. [25], working with Jakhrana goats, reported that maternal permanent environmental effects accounted for 18% of the total additive genetic variance for BW. These maternal effects encompass the uterine environment, nutrient availability, and maternal care. The inclusion of maternal additive genetic and persistent environmental influences in the model resulted in a significant reduction in predicted direct heritability. This is due to a reallocation of variance components, in which maternal components better capture phenotypic variance that was previously attributed to direct additive effects. Furthermore, a negative correlation between direct and maternal genetic influences was found, which contributed to the decrease in direct heritability, especially for early growth parameters such as birth weight and weaning weight.

Incorporating maternal genetic effects and the covariance between direct and maternal genetic effects in the model (Model 2) resulted in reduced estimates of direct heritability. This reduction is attributable to the reallocation of total phenotypic variance, where maternal components (both genetic and environmental) accounted for a portion of the variation. This phenomenon is consistent with theoretical expectations and prior empirical findings in small ruminants. Early-life traits, especially those expressed during the pre-weaning period, are heavily influenced by maternal inputs, such as the uterine environment, maternal nutrition, and postnatal care. When maternal effects are not explicitly modeled, their contribution is inadvertently absorbed into the direct genetic variance, leading to an overestimation of direct heritability. In contrast, excluding these components (Model 1) yielded comparatively higher estimates. This indicates that accounting for maternal effects leads to a more accurate estimation of (co)variance components and genetic parameters for both reproductive and growth traits. Similar findings were reported by Mokhtari et al. [32] in Cashmere goats in Iran, where maternal imprinting effects explained 9% of the phenotypic variance for weaning weight (WW).

Based on the moderate estimates of direct heritability (h²_d) for birth weight (BW), weaning weight (WW), and average daily gain (ADG) obtained from both models (Table 3 and Table 4), it can be concluded that genetic improvement in body weight traits of Zaraibi goats at different growth stages is achievable through selection of both bucks and does. Furthermore, the current results reinforce the importance of including maternal genetic effects and the covariance between direct and maternal effects in genetic models to enhance the precision of variance component and parameter estimation for growth traits in Zaraibi goats. Conversely, several studies have reported low estimates of direct heritability for body weight at various ages [14,33,34]. For example, Hyder et al. [33], working on Teddy goats in Pakistan using an animal model, reported low heritability estimates of 0.042 for birth weight, 0.12 for weaning weight, and 0.14 for yearling weight. In Iranian Cashmere goats, heritability estimates for weaning weight across the first, second, and third parity were similarly low, reported as 0.072, 0.109, and 0.082, respectively [34]. In Zaraibi goats, heritability estimates for weaning weight remained consistently low (0.10) across different animal models, with no significant variation based on the model used [14]. Furthermore, a study on Dhofari goats reported an even lower estimate of 0.02 for birth weight heritability [35]. These low heritability values suggest that environmental factors may play a more substantial role in determining body weight traits, particularly at birth, and highlight the importance of improving management and environmental conditions to enhance growth performance.

3.3.2. Genetic Correlation (r_g)

Genetic correlation estimates among all traits are presented in Table 3 and Table 4. The genetic correlations were positive across all traits, ranging from 0.09 to 0.93. Similar trends have been documented in previous studies across different goat breeds, with reported correlations ranging from 0.40 to 0.99 [11,15,16,23,33,35,36]. The positive and significant genetic correlations observed among birth weight (BW), weaning weight (WW), and average daily gain (ADG) indicate that selection for increased birth weight is likely to yield concurrent improvements in both weaning weight and growth rate. This suggests that these traits are influenced by common genetic factors operating in the same direction, thereby allowing for indirect genetic gains through the selection of a single trait. Consequently, performance recording programs in goats should prioritize early-life traits, as they serve as effective indicators for overall growth potential.

3.3.3. Phenotypic Correlation (r_p)

Phenotypic correlations (r_p) among the studied traits are presented in Table 3 and Table 4. The correlations between all traits were positive, ranging from 0.24 to 0.90 (Table 3). These findings suggest that kids with higher birth weights tend to grow faster and achieve greater weaning weights and average daily gains from birth to weaning. Similar trends have been reported by several researchers [23,37]. For instance, Shaat et al. [23], in a study on Zaraibi kids, observed positive phenotypic correlations among BW, WW, and ADG, ranging from 0.42 to 0.82. In Barki lambs, phenotypic correlations between birth weight and subsequent weights at weaning, six months, and one year were also positive, with values between 0.43 and 0.82 [37]. Additionally, in Iranian Baluchi lambs, both phenotypic and environmental correlations were positive, although generally lower than their corresponding genetic correlations [37]. Boujenane and Diallo [38] similarly reported positive phenotypic correlations among body weights at different ages, ranging from 0.27 to 0.72.

3.3.4. Maternal Genetic Correlations (r_m)

Maternal genetic correlations among the studied traits are presented in Table 4. The maternal genetic correlations between litter size at birth (LSB) and birth weight (BW), weaning weight (WW), and average daily gain (ADG) were 0.02, 0.07, and 0.10, respectively. The correlations among the growth traits themselves were positive but low, ranging from 0.10 to 0.15 (Table 4). Despite their relatively small magnitude, these values are lower than those reported by several authors working on different sheep breeds across various countries. For instance, in Barki lambs, maternal genetic correlations among BW, WW, six-month weight, and yearling weight ranged from 0.79 to 0.94 [37]. Similarly, Abbasi et al. [37] observed positive maternal genetic correlations among BW, WW, and ADG, ranging from 0.11 to 0.64. Boujenane and Diallo [38], in their study on Sardi sheep in Morocco, reported a maternal genetic correlation of 0.60 between BW and 30-day weight.

Although the maternal genetic correlations among body weights at different ages in the present study were low, these results imply that maternal influences—especially those of prenatal origin—can exert a favorable impact on postnatal growth traits. Moreover, both maternal genetic effects and the covariance between direct and maternal genetic effects contribute significantly to the phenotypic variance of traits such as BW, WW, and ADG. Consequently, incorporating both direct and maternal genetic effects is essential for the accurate estimation of genetic parameters for early growth traits [39]

4. Conclusions

According to the study, birth weight and early growth features are significantly influenced by maternal genetic factors; thus, breeding programs must document maternal identity. There is good potential for genetic improvement through sire and dam selection, as indicated by moderate heritability estimates for birth weight, weaning weight, and average daily gain. These findings underscore the importance of accounting for maternal contributions in selection programs to avoid biased estimates and suboptimal genetic gains. Furthermore, selecting for higher birth weight may significantly improve weaning performance and overall weaned weight per doe, as indicated by the strong positive genetic association between birth weight and weaning weight. To enhance the accuracy and sustainability of genetic progress, it is essential to record maternal identity, use appropriate animal models, and consider environmental and management factors in breeding strategies tailored for tropical production systems.