Brahman performance records were obtained from the Nucleus Breeding Centre (NBC) in Sembawa, Sumatra. The NBC is a central development site for Zebu cattle, as previously reported by Zulkarnaen et al. (2022) (Zulkarnaen et al., 2022), and is located in a hot climate with an average daily temperature of 29.5 °C and an average relative humidity of 77.5%. The cattle were reared under a semi-intensive management system, as described by Zulkarnaen et al. (2025) (Zulkarnaen et al., 2025), where they grazed during the day and were sheltered at night. The cattle were fed a diet of tropical grasses supplemented with 14% crude protein concentrate, and they had free access to water and mineral blocks. Furthermore, feeding management protocols were carefully monitored during the study to minimize variability.

A total of 1,082 records were analyzed, originating from 32 sires and 552 dams. The traits observed included birth weight (BW), weaning weight (WW), yearling weight (YW), and weight at 18 months of age (18MW). All traits were considered as selection criteria in the genetic selection program.

Information used in the analyses included animal ID, birth date, sex, dam ID, dam parity, and body weight records. Comprehensive data screening was conducted before the analysis to identify outliers and inconsistencies. Missing data were retained in the analysis as relationship matrices in the animal model have the capacity to predict incomplete information (Mrode & Pocrnic, 2023).

To understand the influence of fixed effects on observed traits, significance was analyzed using the general linear model (GLM) in SAS 9.0. Duncan’s multiple range test (DMRT) was then applied to identify significant differences among fixed effects, including sex and dam parity (Zulkarnaen et al., 2025). The statistical model is described below:

Y_ij= μ + S_i + P_j + ε_ij

Where Y_ij is the observed trait (body weight at different measurement of age), μ is the mean population, S_i is the sex effect, P_j is the parity effect, and ε_ij is the error.

Genetic parameters were estimated using a univariate animal model with restricted maximum likelihood (REML), and breeding values were predicted using best linear unbiased prediction (BLUP). The estimation of parameters was performed using ASReml (Gilmour et al., 2015). The model fitted for the additive genetic effect (Model 1) is described as follows:

y= Xb + Zu + e

Where y is a vector of the observations (body weight), b is a vector of the fixed effects (calf sex and dam parity), u is a vector of random additive genetic effects, e is a vector of residual effects, X is a matrix related to fixed effects, and Z is a matrix related to additive genetic effects.

The solution matrices for the model with an additive genetic effect are described as follows:

The model with additive and maternal genetic effects (Model 2) is described as follows:

y= Xb + Zu + Wm + e

Where m is a vector of random maternal genetic effects, and W is a matrix related to maternal genetic effects.

The associated solution matrices for the above model are described as follows:

Figure 1 displays the development of growth from birth weight (BW), weaning weight (WW), yearling weight (YY), and 18-month weight (18-MW). Each measurement showed distinct patterns, which interpreted performance, genetic progress, and the influence of the environment over time from 2013 to 2020. Birth weight ranged from 30.32 to 35.16 kg and remained relatively stable across years. The low variability in birth weight indicates consistent maternal and prenatal management, which has led to good uniformity.

Weaning weight (WW) indicated moderate fluctuation, ranging from 85.72 to 106.40 kg, which may suggest variability in environmental conditions. Yearling weight (YY) displayed wider variation, ranging from 153 to 219 kg, likely influenced by weaning weight and greater sensitivity to feed availability, pasture quality, and management factors. Samples with 18-month weight showed the largest fluctuation among the traits, reaching the peak in 2019, which may reflect exceptional performance during the period.

(Figure 1) revealed generally positive progress in body weight over the eight-year period despite some variability. The improvement between 2018 and 2019 suggests better management practices and highlights strong potential for evaluating breeding programs and future selection strategies.

Analysis of herd dynamics revealed a carefully managed and stable population structure (Figure 2) (Figure 3). Figure 2 illustrates annual fluctuations in calf sex ratios compared to the expected natural proportion. In 2016, a deviation toward male predominance (57.89%) represented a stochastic fluctuation typical of breeding populations and did not compromise the availability of selection candidates. With regard to the dam structure, Figure 3 reflects the replacement. The low proportion of cows in advanced parities (7-8) and the substantially higher proportion in early parities (1-4) demonstrate an intensive culling policy. This population structure is critical for accelerating genetic gain through the minimization of generation intervals.

The GLM analysis revealed statistically significant sources of variation across all growth stages (p<0.05). As shown in (Table 1), male calves exhibited higher body weight than females, with this superiority being significant at birth (35.03 kg vs. 32.29 kg), and at 18 months (253.59 kg vs 236.66 kg). This sexual dimorphism likely reflects the anabolic effects of androgens, which stimulate muscle protein synthesis and promote skeletal growth. Given the progressive divergence in growth performance between male and female calves, it is essential to include calf sex as a fixed effect in genetic evaluation models to ensure unbiased comparisons among selection candidates.

The influence of dam parity on birth weight was statistically significant, with the most pronounced effect observed at birth (Table 2). In contrast, subsequent growth phases exhibited minimal impact. A significant performance disadvantage was observed among calves at the first parity, as demonstrated by Duncan’s test. Calves of primiparous dams weighed 31.06 kg, significantly lighter than those born to mature dams (parity 2-5). This disparity is attributed to physiological nutrient portioning in primiparous dams, where nutrients are allocated to maternal somatic growth and fetal development, thereby limiting in utero nutrient availability for the fetus.

During peak reproductive performance, especially at parities 2 to 5, cows exhibit optimal uterine capacity and physiological development, resulting in higher birth weights in their offspring. Cows at advanced parities demonstrated declining birth weights due to reduced biological reproductive efficiency. In addition, post-weaning weights, including weaning weight, yearling weight, and 18-month weight, did not differ significantly across parity groups, as the calves had been maternally separated, allowing the genetic potential for growth to perform.

The estimates of genetic parameters are presented in (Table 3). Two models were employed for the evaluation. Model 1 incorporated the direct additive genetic effect solely, resulting in estimates of heritability (h²), ranging from 0.571 to 0.674. By incorporating maternal genetic effects, the heritability estimates decreased to between 0.426 and 0.501, which were lower than the estimates produced by Model 1. Maternal genetic effects (m²) were remarkably consistent across growth stages, ranging from 0.253 to 0.265, which indicates that maternal influences contributed approximately 25% of the phenotypic variance throughout the growth trajectory.

Estimates of heritability obtained under Model 1 were inflated due to the omission of maternal genetic effects. This overestimation resulted from confounding between direct additive and maternal genetic effects, which led to higher estimates of variance components. Model 2 revealed that maternal genetic effects represent a significant source of phenotypic variation for growth in the Brahman population. These findings emphasize the necessity of considering maternal genetic effects in genetic evaluations to have an accurate estimate of breeding value.

Genetic evaluation was conducted to establish the ranking of animals based on estimated breeding values (EBVs) to identify superior animals for breeding purposes. EBVs for BW, WW, YW, and 18-MW for proven bulls, dams, and young animals are presented in Tables 4 to 7. (Table 4) presents the genetic potential of the top 10 sires ranked by EBVs. The results showed that sire S-80903 exhibited the highest EBVs for BW, WW, and YW, thereby representing the superior sire, while sire S-877.888 recorded the highest EBV for the 18MW trait. (Table 5) indicates a significant variation in the growth trajectory of the dams. The highest EBVs for the dams were observed in individual D-8258 for BW, D-0921 for WW, D-4738 for YW, and D-3072/048 for 18MW. Interestingly, the presence of negative values both for sires and dams in several high-ranking sires and dams indicates the possibility of choosing the bulls and dams at an early stage without sacrificing market weight.

The evaluation of candidate animals is summarized in (Table 6) (Table 7). Table 6 presents the performance metrics of the top 10 male calves. Individual 1-15.04.138 exhibited the highest EBV for BW, while the highest values for WW, YW, and 18MW among male calves were observed in 1-1458, 1-15.04.191, and 1-15.04.087, respectively. Notably, the EBV of the top-ranking young sire exceeded the previous proven sire, which indicates the genetic superiority of the young sire over older sires. The increased EBV across cohorts indicates a favorable genetic trend, which leads to enhanced genetic potential in the herd. The superiority of young sires is expected to accelerate genetic progress in subsequent generations and keep the breeding program sustainable.

Table 7 shows the rank of female calves based on EBV across four key growth stages. Individual 2-15.04.012 exhibited the highest EBV for BW, while individual 2-15.04.140 outperformed others in both WW and YW, and individual 2-15.04.118 showed the highest EBV for 18MW within the female calves’ population. This suggests different physiological growth curves among the top-tier calves.