Goats represent a strategic commodity within Indonesia’s agribusiness sector, driven by the country’s rapidly growing population. According to the Directorate General of Livestock and Animal Health Services (2024)(Directorate General of Livestock and Animal Health Services, 2024), the goat population in Indonesia increased from 14,374,014 in 2023 to 15,710,055 in 2024. Goats play a significant role in fulfilling the nutritional needs of humans, particularly in rural areas(Gunawan et al., 2018). Goats are widely distributed across the country, with almost 99% of goats farmed by smallholders in small-scale production, typically with 2 to 8 animals per farm(Sujarwanta et al., 2024). Smallholders favor goats due to their ease of management, consistent market demand, function as a financial reserve, and frequent use in religious or cultural ceremonies. Most farmers prefer to raise local breeds, as these animals are well-adapted to Indonesia’s climate and environmental conditions(Rusdin et al., 2020);(Khasanah et al., 2016).

The Etawah Grade (EG) goat is an indigenous Indonesian breed, classified as a dual-purpose breed that is used for both milk and meat production (Figure 1). The EG goat is originated from crossbreeding between the Jamunapari goat from India with local Indonesian goats, developed over a long period, since the Dutch colonial era. The Indonesian government has officially recognized the EG goat as a local goat breed through Ministerial Decree of Agriculture No. 695/Kpts/PD.410/2/2013. Despite this recognition, the potential of the EG goats has not been fully realized, resulting in lower production performance compared to introduced goat breeds. The development of EG goats can be achieved through breeding programs that improve their productivity, genetic quality, and economic value. Effective implementation of breeding programs requires comprehensive recording systems and complete information on various factors influencing production traits(Resti et al., 2024). Estimating genetic parameters, including heritability, genetic correlation, and Estimated Breeding Value (EBV), is an essential step in selecting superior breeding stock for traits with high economic value.

Growth traits are important production traits in goats, significantly influencing both survival and economic value. These traits are essential for assessing the potential for meat production and reproductive performance. Growth traits are determined by both genetic and non-genetic factors, including birth year, climate, birth type, sex, and parity(Ofori & Hagan, 2020). Non-genetic factors may mask an individual’s true genetic potential, as animal performance results from the interaction between genetic and environmental influences (Mamutse et al., 2023). A better understanding of non-genetic effects on body weight traits is necessary to optimize management and improve the accuracy of estimating genetic parameters in EG goats.

Previous studies on non-genetic effects and genetic parameters have focused on several goat breeds, including Jamunapari goat(Dige et al., 2022), Assam Hill goat(Sarma et al., 2019), and Sirohi goat(Dudhe et al., 2015). For Indonesian local goats, genetic parameter estimation has been reported for the Sapera goat(Anggraeni et al., 2020) and the EG goat(Hasan et al., 2014). However, there is limited information regarding non-genetic effects and on genetic parameters estimation for growth traits in the Indonesian local goat, particularly the EG goat. Incorporating non-genetic factors as fixed effects in the model and using a large sample size are essential for improving the accuracy of genetic parameter estimation(Atoui et al., 2017). The present study aims to analyze the influence of non-genetic factors, estimate genetic parameters, and assess long-term genetic progress for growth traits of the EG goat at different ages.

The overall means of BW, WW, 6MW, 8MW, and 12MW observed in this study were 3.491±0.008 kg, 14.754±0.060 kg, 21.223±0.085 kg, 26.062±0.151 kg, and 31.062±0.167 kg, respectively. The BW recorded aligns with previous findings on Turkish Saanen goats(Erdoğan Ataç et al., 2023) and exceeds those reported for Inner Mongolia White Cashmere goats(Shi et al., 2024) and Boer x Highland goats(Tesema et al., 2021), with values of 3.33, 2.54, and 2.52 kg, respectively. The weaning weight (WW) in the present study was lower than that reported for Turkish Saanen goats(Erdoğan Ataç et al., 2023), with values of 18.38 kg and 15.17 kg for Inner Mongolia White Cashmere goats(Shi et al., 2024), but has a higher value than that of the Assam Hill goat(Sarma et al., 2019). Rout et al. (2018)(Rout et al., 2018) reported lower 6MW, 8MW, and 12MW values for Jamunapari goats compared to those observed in the present study, with respective values of 14.5, 19.4, and 23.9 kg. Similarly, Magotra et al. (2021)(Magotra et al., 2021) reported lower 6MW, 8MW, and 12MW body weights in Beetal goats compared to the present study, with values of 13.8, 17.8, and 20.2 kg, respectively. Variations in body weight among goat breeds are primarily determined by genetic potential and breed-specific production objectives. Cashmere-type goat is more adapted for fiber production rather than rapid growth or large body size(Gawat et al., 2023). Small-framed goat breeds are typically found in harsh environments, where their smaller body weight represents a trade-off that enhances reproductive efficiency and environmental adaptability(Amiri et al., 2023). However, dairy-type goats generally have large body sizes, and the males are often utilized for meat production, resulting in relatively high body weight(Gawat et al., 2023).

The overall mean and standard error values for ADG3, ADG6, ADG8, and ADG12 of the EG goat were 0.124±0.001 kg day^-1, 0.090±0.001 kg day^-1, 0.091±0.002 kg day^-1, and 0.068±0.001 kg day^-1, respectively. Tesema et al. (2021)(Tesema et al., 2021) reported lower average daily gain values in crossbred Boer goats, with 0.080 kg day^-1 for ADG3, 0.038 kg day^-1 for ADG6, 0.042 kg day^-1 for ADG8, and 0.030 for ADG12. In contrast, Wang et al. (2024b)(Wang et al., 2024) reported higher average daily gain values in Dumeng sheep compared to the present study, with ADG3 and ADG6 values of 0.302 kg day^-1 and 0.232 kg day^-1, respectively. The observed differences in growth traits between the present and previous studies may be attributed to variations in agroclimatic conditions and the genetic backgrounds of the breeds.

Significant differences (p<0.01) in body weight and average daily gain were observed between male and female goats across all age groups. Male EG goats showed higher body weights and average daily gains than females at every age. These findings are consistent with previous research, which indicated that male goats demonstrated a higher growth rate than female goats(Markos et al., 2023). Phenotypic differences between males and females are referred to as sexual size dimorphism and are primarily attributed to difference in endocrine conditions and associated with nutrient requirements(Ghafouri-Kesbi & Baneh, 2018). Sex hormones influence the growth-related genes, even though males and females share the same genome. Androgen and estrogen hormones are known to have different concentrations between males and females and influence growth traits(Ghione & Dean, 2025). High levels of androgen hormones in male goats enhance muscle development by increasing the protein synthesis rates(Tesema et al., 2022). In contrast, estrogen in females delays growth by restricting long bones’ elongation, resulting in lower body weight compared to males, but supporting earlier sexual maturity(Ghione & Dean, 2025). High estrogen levels in female goats can also reduce dry matter intake, further affecting their growth(Safdar & Sadeghi, 2015). Sexual size dimorphism has important implications for management and breeding strategies, as rapid muscle development in males makes male goats more suitable for meat production.

Type of birth had a highly significant effect (p<0.01) on all traits, except ADG12, for which no significant effect (p>0.05) was observed. Single-born goats had the highest BW, WW, 6MW, 8MW, ADG3, and ADG6 compared to other birth types. The 12MW showed relatively similar values among all types of birth. Previous studies have also reported lower body weight and slower growth in multiple-born kids in both EG goats(Husen et al., 2025) and Boer goats(Zhang et al., 2009). The variation in growth rate among birth types is influenced by intrauterine competition for space and nutrients(Gootwine, 2020). A single-born goat kid would not have to compete to get space and nutrients in the uterus during the embryonic period. Differences in growth and body weight during the weaning period are further influenced by competition for milk from the doe during the suckling period. Limited milk production of doe goats during lactation may result in lower growth rates for multiple-born kids compared to single-born kids(Shi et al., 2024). Lower body weight during the post-weaning period in multiple-born kids may be influenced by a slower growth rate during the suckling period compared with single-born kids(Hasan et al., 2014). After the suckling period, the maternal effect becomes minimal, and the feeding program shifts to free feeding. As animals grow older, their physical fitness continues to improve, and their immunity gradually strengthens(Shi et al., 2024). This effect allowed goats with multiple-birth types to achieve similar or even faster growth rates compared to single-born kids.

Year of birth had a highly significant (p<0.01) effect for all traits, although the trend fluctuated across years (Table 2). BW remained relatively stable from 2015 to 2024, ranging from 3.37 to 3.59 kg, with the lowest BW being reached in 2019 and the highest in 2020. The lowest WW, 6MW, 8MW, and 12MW, were observed for goats born in 2018, while the highest WW was observed in 2021 with the value of 17.37 kg. The highest trend of 6MW, 8MW, and 12MW was observed in 2023 with values of 24.34, 28.77, and 39.04 kg, respectively. The lowest of ADG3, ADG6, ADG8 was in 2018 with the value of 0.090±0.002 kg day^-1, 0.055±0.003 kg day^-1, and 0.038±0.008 kg day^-1, respectively. The highest value of ADG3 (0.152±0.002 kg day^-1) and ADG8 (0.111±0.004 kg day^-1) were reached in 2021, while the highest ADG6 was observed in 2023 with the value of 0.110±0.002 kg day^-1. The highest ADG12 was reached in 2023 (0.095±0.002 kg day^-1) and the lowest in 2024 (0.042±0.013 day^-1). The variation in body weight and growth rate across years is attributed to climate change, which affected feed availability during those years(Menezes et al., 2016). The differences in goat growth traits across years were also influenced by the management practices implemented in each respective year, including health management and feeding management(Hasan et al., 2014).

BW, WW, 6MW, 12MW, ADG3, ADG6, and ADG12 were significantly affected by season of birth (p<0.01), while 8MW and ADG8 were not significantly affected (p>0.05). Goats born in the rainy season showed higher body weight and average daily gain in early growth traits (BW, WW, and 6MW) compared to those born in the dry season. On the contrary, goats born in the dry season showed better performance in later growth traits (8MW and 12MW). Abebe et al. (2023)(Abebe et al., 2023) reported a similar result that BW, WW, and 6MW of lambs born in the rainy seasons were heavier than those born in the dry season. Early growth traits, particularly BW and WW, are largely determined by the dam’s nutritional intake. During the rainy season, vegetation is more varied and nutritious than in the dry season. Lo et al. (2024)(Lo et al., 2024) noted that the crude protein content of the forage during the rainy season is higher than during the dry season, whereas the dry matter content during the dry season was higher than during the rainy season. Better feed availability and nutritional quality during the rainy season are associated with endocrine conditions in dams, particularly increased prolactin secretion and milk production, thereby contributing to higher weaning weights(Farrag, 2022). Elevated insulin levels during the rainy season stimulate anabolic processes in muscle and liver tissues, improving carbohydrate and lipid metabolism(Murillo-Ortiz et al., 2013);(Song et al., 2025). These metabolic effects contribute to improved post-weaning growth and are positively associated with increased neonatal birth weight through enhanced maternal nutrient utilization(Song et al., 2025). In later growth phases, growth is more influenced by dry matter intake, organic matter intake, and energy supply(Wang et al., 2024). This phenomenon explains the higher BW, WW, and 6MW in goats born during the rainy season, while 12MW is higher in those born during the dry season. Higher body weight at 12MW during the dry season may result from compensatory growth after nutritional restriction, increased feed intake under low-quality forage conditions, and improved nutrient utilization efficiency during post-weaning growth.

Parity had a highly significant (p<0.01) effect on BW, WW, 6MW, 8MW, 12MW, and ADG3, a significant effect (p<0.05) on ADG6, and no effect (p>0.05) on ADG8 and ADG12. The highest BW was observed in offspring from second or third parity dams, while the lowest values were recorded in offspring from first parity dams. WW, 6MW, 8MW, and 12MW were the highest in offspring from first-parity dams, with body weight decreasing gradually with the increase in parity. ADG3 and ADG6 showed a gradual decrease as the number of parities increased. Parity of dams influences kid performance due to the development of the dam’s uterus with increasing age and frequency of parturition(Hasan et al., 2014). Similar effects of parity on body weight have been reported in previous studies(Ehsaninia, 2021). Multiparous dams tend to produce larger litter size compared to primiparous dams(Mamutse et al., 2023). There is an antagonistic relationship between body weight and litter size, particularly during early growth stages(Besufkad et al., 2024), which may explain the higher BW, WW, 6MW, and 8MW in offspring from primiparous dams compared to those from multiparous dams. Early growth of the goat is strongly affected by maternal influences, since the nutrient availability depends primarily on the dam’s nutritional status during the prenatal period. However, maternal effects gradually decline as offspring age.

The heritability of BW, WW, 6MW, 8MW, and 12MW were estimated at 0.314±0.028, 0.265±0.034, 0.352±0.038, 0.273±0.054, and 0.254±0.052, respectively. Heritability values above 0.4 are classified as high heritability, 0.2 to 0.4 as medium heritability, and below 0.2 as low heritability(Jonker et al., 2018). Overall, the estimated heritability in this study was classified as moderate. Previous studies reported a higher heritability in West African Dwarf goat of BW (0.45), WW (0.57), 8MW (0.74), 12MW (0.49) than this study, except for 6MW (0.04)(Ofori & Hagan, 2020). In contrast, lower heritability estimates were found in Beetal goats for BW (0.07), 8MW (0.17), and 12MW (0.10), with a similar heritability value for WW (0.27) and a higher value for 6MW (0.37)(Magotra et al., 2021). The estimated heritability of average daily gain traits was low to moderate, ranging from 0.085 to 0.254, and decreased gradually with increasing animal age. Magotra et al. (2021)(Magotra et al., 2021) reported a similar pattern and lower heritability values for average daily gain in Beetal goats, with 0.21 for ADG3, 0.21 for ADG6, and 0.07 for ADG6. Higher heritability for ADG3 (0.31) was reported in Boer goats(Menezes et al., 2016) and Cashmere goats(Wang et al., 2022). Rout et al. (2018)(Rout et al., 2018) found higher heritability for ADG6 and ADG12, at 0.37 and 0.17, respectively.

The differences in heritability estimates were due to goat breed and the evaluation methods used(Ladeira et al., 2021). Differences in estimating heritability may also be influenced by sample size and the fixed effects included in the model. Previous studies on the same breed reported higher heritability estimates than those found in the present study. Hasan et al. (2014)(Hasan et al., 2014) used a smaller sample size and did not account for non-genetic factors as fixed effects. A larger sample size can comprehensively capture variation, making the estimated value more accurate, higher precision, and stronger statistical power, thereby facilitating the discovery of genetic laws(Shi et al., 2025). Non-genetic factors such as gender, breed, dam age, season, year, and type of birth significantly influence determining trait variations and should always be taken into account as fixed effects(Wang et al., 2024).

Body weight traits showed higher heritability than average daily gain, indicating that environmental variation has a greater impact on average daily gain than on body weight. Higher heritability estimates indicate that body weight traits are more reliable selection criteria than average daily gain in EG goats. Among the traits, 6MW had the highest heritability and can be considered a suitable selection criterion at the early growth stage of EG goats, as it demonstrates the greatest potential to be passed on to the next generation. High heritability estimation indicates that the variation in that trait is mainly influenced by genetic factors rather than environmental factors(Fathoni et al., 2022). Selecting for 6MW can yield higher genetic gain and faster phenotypic improvement compared to other traits. The low estimates of heritability for some traits may suggest that genetic variation is probably unobservable due to environmental factors(Rout et al., 2018). Manirakiza et al. (2020)(Manirakiza et al., 2020) reported that improvement in traits with low heritability can be achieved through improved recording systems and better management practices, including feeding and health management. Maternal effect also significantly influences the expression of early growth traits; however, their influence decreases as the animal ages(Husen et al., 2025);(Manirakiza et al., 2020). Furthermore, accounting for both maternal genetic and environmental effects is important to improve the accuracy of genetic parameter estimation in future studies.

The phenotypic and genetic correlations among BW, WW, 6MW, 8MW, and 12MW in this study ranged from weak to strongly positive. Besufkad et al. (2024)(Besufkad et al., 2024) reported similar findings to this study, with BW showing positive but low phenotypic (0.21-0.28) and genetic correlations (0.20-0.24) with WW, 6MW, and 12MW. A Higher phenotypic and genetic correlation between WW – 6MW, WW – 8MW, and WW–12MW was reported on West African Dwarf goats with values between 0.81-0.92 for phenotypic correlation and 0.75-0.92 for genetic correlation(Ofori & Hagan, 2020). Tesema et al. (2021)(Tesema et al., 2021) also reported high positive correlations between 6MW and 8MW (0.79) and between 6MW and 12MW (0.71). Positive genetic correlations among the observed traits indicate that these traits are influenced by genes that control more than one trait (pleiotropy)(Habtegiorgis et al., 2022). The positive genetic correlations across all traits in this study indicate that selection for any trait should result in positive genetic change in the others. Indirect selection can also be applied to traits with strong genetic correlations, particularly for traits that are difficult to measure(Bourdon, 2014). Among the evaluated traits, 6MW is the most promising selection criterion for growth in EG goats, as its high heritability and strong genetic correlations are expected to achieve greater genetic improvement in other traits.

The estimated genetic trends in this study indicated improvements in all evaluated traits, except for 8MW and ADG8. Rout et al. (2018)(Rout et al., 2018) reported higher estimated genetic trends for BW, WW, 6MW, 8MW, and 12MW in Jamunapari goats, with values of 0.037, 0.080, 0.118, 0.144, and 0.199 kg year^-1, respectively. In contrast, Tesema et al. (2021)(Tesema et al., 2021) observed declines in genetic trends for BW, WW, and 6MW in Boer x Highland goats, with values of 0.0207, 0.0805, and 0.0317 kg year^-1, while 8MW and 12MW increased by 0.1692 and 0.2133 kg year^-1, respectively. Ren et al. (2024)(Ren et al., 2024) reported higher genetic trends for ADG3 but lower genetic trends for ADG6 in Luzhong sheep, at 0.0012 kg day^-1 and 0.00002 kg day^-1, respectively. Low estimated genetic trends may be attributed to climatic conditions that reduced feed availability and decreased livestock productivity in the observed year(Habtegiorgis et al., 2022). The positive and significant genetic trends observed for BW, WW, 6MW, 12MW, ADG3, ADG6, and ADG12 indicate that the selection program implemented at the breeding station has been effective in improving these traits genetically over the years. However, these trends observed were lower than those reported in previous studies across several goat breeds. The low estimated genetic trend suggests that the selection program implemented at the breeding station in recent years has not been fully effective in achieving optimal genetic improvement. The low estimated genetic trend indicates that the selection differential was not maximized; other factors contributing to the low genetic trend include selection practices that do not account for estimated breeding values and the culling of animals with low productivity at specific ages(Tesema et al., 2021);(Gholizadeh & Ghafouri-Kesbi, 2015). Several strategies that can be implemented to enhance genetic trends include reducing the generation interval and increasing selection intensity(He et al., 2023);(Kasinathan et al., 2015). The most practical approach to reducing the generation interval is to shorten the length of sire usage and to implement reproductive technologies such as artificial insemination (AI). The application of genomic selection has also been proven to reduce the generation interval, as replacement animals can be selected at a relatively young age(Roos et al., 2011).

These findings emphasize the need to account for non-genetic variation when estimating genetic parameters for selection programs in EG goat populations. Adjusting body weight for non-genetic factors will reduce bias in estimating genetic parameters. In future studies, both maternal genetic and maternal environmental effects should be considered in the estimation of genetic parameters to increase the accuracy of the estimates, especially for early growth traits. The findings of this study may provide a basis for decision-making in production management to prevent performance decline driven by the effects of non-genetic factors. Several strategies, including management improvement and implementation of technology, are required to enhance the genetic trend for growth traits in EG goats.

Growth traits in EG goats are significantly influenced by several non-genetic factors, including sex, birth type, year of birth, season, and parity. Notably, only 8MW was unaffected by the season of birth. The estimated heritability of growth traits in EG goats was moderate, suggesting sufficient genetic variability to respond to selection. Among the evaluated traits, 6MW exhibited the highest heritability and strong genetic associations with later growth traits; it could serve as an effective selection criterion in EG goat breeding programs. A positive genetic correlation among growth traits suggests that improvement in one trait may lead to favorable responses in related traits. The negative genetic trend may indicate inconsistencies in selection practices or management conditions during the study period.