Possibility of early selection of Limousin young stock based on analysis of repeatedly measured body size parameters

Possibility of early selection of Limousin young stock based on analysis of repeatedly measured body size parameters | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Possibility of early selection of Limousin young stock based on analysis of repeatedly measured body size parameters Márton János Demény, Lili Dóra Brassó, Márton Szűcs, János Tőzsér This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9277528/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In beef cattle breeding, analysing body size parameters enables the evaluation of production-related correlations and growth dynamics. By assessing these traits at multiple time points, it becomes possible to identify early predictors of later development, supporting early selection decisions. In this study, the correlations between the data measured at weaning (230 days) and at 13–14 months of age (weight, withers height, tail height, length of back, shoulder width, hip width and pin width) were analysed. Data of bull calves kept for breeding and fattening at weaning ages were also compared. The results indicated a weaker correlation between body measurements taken at weaning age (r AVG = 0.581) than at 13–14 months of age (r AVG = 0.640, n = 817). Pin width showed very weak correlations in all cases (r = 0.293–0.930). The strongest positive correlation between the two measurement times was found for withers height (r = 0.618) and tail height (r = 0.631). The results of fattening bulls were compared with those of bull calves retained for breeding. Comparative analysis revealed that breeding bulls outperformed fattening bulls across all parameters except the overgrowth index and pin width. These findings suggest that selection decisions can be effectively based on measured traits alone. Overall, although Limousin calves exhibit variable growth rates, withers height and tail height can be regarded as pre-selection criteria for breeding programs. Beef cattle body composition breeding and fattening bulls decision support system growth rate weaning calves Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION The measurement of body parameters in cattle, including Limousin breed, is a crucial aspect of livestock management that impacts animal performance, productivity, and overall herd health. Numerous studies have demonstrated strong relationships between body measurements, conformation traits and growth or productive performance, highlighting their value in breeding and management decisions (Koenen and Groen 1998 ; Abreu et al. 2018 ; Dominguez-Castaño et al. 2024 ). Morphological traits can effectively differentiate cattle breeds, reflecting genetic diversity and environmental adaptation (Traoré et al. 2016 ). Body weight and body size parameters recorded at weaning are reliable indicators of growth and are widely used to evaluate breeding efficiency and heritability in cattle populations (Nurgiartiningsih et al. 2022 ). Also, the application of genetic markers associated with body measurement traits plays a key role in determining cattle conformation and growth traits (Gao et al. 2019 ). Several studies have shown that body dimensions can be used to accurately estimate live weight, providing practical benefits for nutritional planning and economic decision-making in beef production systems (Paputungan et al. 2013 ; Haq et al. 2020 ). Emerging research indicates that targeted feeding strategies can enhance growth performance consistent with findings on phenotypic diversity and environmental adaptation in cattle (Terefe et al. 2015 ). Comprehensive body measurements enable informed management decisions that support animal health and farm profitability, while also providing a basis for exploring the genetic background of growth traits. As highlighted by Kamprasert et al. ( 2019 ), the heritability of body measurements, including frame scores, offers a framework for breeding programs aimed at optimizing growth performance in various cattle breeds. This genetic perspective is essential as it enables producers to select for traits that can enhance growth rates while potentially improving overall herd quality. The significance of measuring weight and body parameters at various ages in beef cattle to fit growth models that describe the development of these animals have been demonstrated by the international literature (Crispim et al. 2015 ; Ribeiro et al. 2023 ; Yang et al. 2024 ). The use of random regression models in addition to single trait models enabled researchers to pinpoint functional candidate genes involved in body weight variations (Ribeiro et al. 2023 ). The reviewed studies demonstrate that evaluating morphological traits, body weight, and body measurements is essential for improving beef cattle production, supporting efficient management, and aligning growth with market demands. However, information on repeated body measurements in Limousin cattle across different growth stages remains limited. Therefore, the study aimed to assess the practical use of body size data for preselection of the Limousin breed by analysing correlations between measurements taken at two time points and by comparing bull calves selected for breeding and fattening. Key body measurements including withers height, tail height, back length, shoulder width, hip width and pin width, serve as significant indicators of physiological and genetic status. Evaluating these traits supports improved understanding of growth patterns and informs breeding, feeding, and management decisions to optimize Limousin cattle performance. In the study, we hypothesised that significant relationships exist between the body size measurements recorded at the two time points, particularly for withers height and tail height due to the more uniform and consistent development of these body parts compared to others. We further hypothesized that body size data would be more homogeneous and that stronger correlations among traits would be observed at the second measurement time point. Additionally, when comparing breeding and fattening animals, we assumed that calves selected for breeding would perform better than the population average in body weight and at least one body size parameter at the time of selection. Based on these assumptions, our key hypothesis is that body weight and body size parameters can be effectively used for preselection, offering substantial economic benefits and time savings in practical breeding programs. 2. MATERIALS AND METHODS 2.1. Study population and the measured parameters The study compared data from three years (2022–2024) involving 14 Limousin breeding farms. The breeding farms were all located in the same region of Hungary, Transdanubia, belonging to Zala, Veszprém, and Fejér counties. In accordance with strict rules governing breeding farms, calves were raised using similar technology until weaning, supplemented with calf feed on pasture. The 13–14-month-old breeding heifers were also kept in a similar manner, in open-air stables using a feeding technology based on concentrate feed and hay. The technology for keeping bulls participating in the own performance test is the most clearly defined, where they must achieve a minimum performance in groups of 2–5 animals, in deep litter or open pens, with a diet based on concentrate feed and hay in order to qualify as breeding bulls. In general, test period lasts 150–180 days and must be completed by the time the animals reach a maximum age of 450 days. The guidelines for the husbandry and feeding technologies, registration and qualification of bulls, as well as data collection are set in the breeding program of the Association of Hungarian Limousin and Blonde d'Aquitaine Breeders which is available on the website of the association in Hungarian ( www.limousin.hu ). A total of 817 breeding cattle including bulls (n = 469) and heifers (n = 348) were examined for body size data. Body measurements were taken at weaning age (at 230 days average) and at 13–14 months of age (at an average age of 434 days) in accordance with the breeding regulations of the Association of Hungarian Limousin and Blonde d'Aquitaine Breeders. Thus, each animal had two measurement results, for both bulls and heifers. Further evaluations focused on comparing the body size data at weaning age of breeding and fattening bulls (n = 928). Half of the bulls with the best qualities were kept further after weaning (n = 469) and took part in the own performance test. Another half (n = 459) was slaughtered when reached 300 kg or above (600–700 kg). Both breeding and fattening groups were kept on the same farms with identical feeding, housing and management conditions before and during measurements until weaning. Then, the feed of the two groups differed; fattening bulls received growing concentrate, while breeding animals were fed with breeding concentrate feed. Body measurement points and measuring equipment are illustrated in Fig. 1 . In addition to measuring weight and body parameters at the time of measurement, body mass index (BMI) and overgrowth index (OI) were also calculated. $$\:BMI=kg\frac{100}{WH}$$ $$\:OI=TH\frac{100}{WH}$$ In the formulae, TH denotes the term “tail height", and WH denotes the term "withers height". *inhibitors indicate points measured with measuring stick, while arrows demonstrate tape measurements Figure was created in https://BioRender.com First, the raw and corrected data were compared. For example, in the case of withers height, the data were corrected using the following equation: $$\:\text{W}\text{H}\text{c}\:=\:\text{W}\text{H}\text{i}\:\pm\:\text{b}\:\left(\text{A}\text{G}\text{a}\text{v}\text{g}\:-\:\text{A}\text{g}\text{i}\right)$$ , where "WHc" is the corrected withers height (cm), "WHi" is the withers height of the given individual (cm), "b" is the regression coefficient, "AGavg" is the average age of the group (day), and "AGi" is the age of the given individual (day). The measurement error was calculated using the following formula, where N is the number of individuals, λ = 2.57624, SD = 4.649 and X = 112.25 cm. $$\:N>{\left(\frac{\lambda\:\times\:SD}{SEM}\right)}^{2}$$ Based on the formula, the result shows that the measurement error is 2 cm for 36 individuals, 1.5 cm for 64 individuals, and 1 cm for 143 individuals (α = 0.01; P = 0.99). Since in our case the number of elements exceeded 143 in all cases, we used a measurement error of 1 cm in the analysis of statistical correlations. Correlation levels between body weight and body parameters were determined according to (Szabó et al. 2004), as follows: I r I < 0.4: weak correlation 0.4 < I r I < 0.7: moderate correlation 0.7 < I r I 0.9: very strong correlation This defines the categories more strictly than is generally the case in the international literature. 2.2 Statistical analysis The data were evaluated using the Statistical Package for Social Sciences (SPSS 24.0) software package. Descriptive statistics, involving mean and standard deviation were conducted to compare the age-adjusted and raw data of weaning bulls including various body measurement parameters, age and weight at measurement. Paired-Samples T Test was applied for the comparison of means of age-adjusted and raw data. Correlations between body measurement parameters were analysed with Pearson’s Bivariate Correlation. Linear function fitting was performed between the first and second measurements of each body size. In the analysis, the independent variable was the data of the first measurement, while the dependent variable was the results of the second measurement. A significance level of P < 0.05 was adopted as the threshold for statistical significance. 3. RESULTS AND DISCUSSION Several studies have demonstrated that body weight and linear body measurements play a crucial role in selection decisions aimed at improving desirable genetic traits, growth potential, carcass yield, and overall animal health in cattle populations (Kamprasert et al. 2019 ; Naserkheil et al. 2020 ; Nurgiartiningsih et al. 2022 ). Consequently, the relationship between body parameter assessment and preselection in cattle breeding is an essential aspect of sustainable livestock production and genetic improvement strategies. As the demand for high-quality beef products increases, the focus on efficient selection methods becomes paramount. 3.1. Descriptive statistics of the age-adjusted and raw data of weaning bulls For individual body measurements and calculated indices, the correlations between corrected and raw data ranged from r = 0.847 to r = 0.999 ( P 0.05) the differences between the raw and corrected data. Since there was a strong positive correlation between the two databases and the differences were not statistically significant, the raw data were used for further analysis to avoid the distorting effects of correction. CV% for most parameters were below 10% indicating low variability in data. Body weight, corrected body weight and body mass index showed slightly higher, moderate CV%s. In Italy, Rezende et al. ( 2022 ) measured the body weight of limousine cattle 247 kg at 210 days of age and 411 kg at 365 days of age. Animals had 60 kg lower weight and 18 days younger compared to our study. There was no information available on the grazing, feeding and climatic conditions for further conclusions. In Indonesia, in Sumba Ongole cattle, mean body weight of bulls at 673 days old was 299.kg with 122 cm withers height. Cattle were fed Elephant grass ( Pennisetum purpureum ), rice straw and cassava meal ad libitum (Putra, 2020 ). In West Java, Limousine bulls ages between 1.5 and 13 years showed body weight between 384 and 1021 kg but authors calculated correlations with chest girth, body length and shoulder height (Adhianto et al. 2025 ). Hungarian Simmental, Hereford, Aberdeen Angus, Red Angus, Lincoln Red, Limousin, Charolais, Blonde d’Aquitaine and Shaver cows’ live weight and body measurement were conducted. Authors found that weights ranged between 500 (Hungarian Simmental) and 638 kg (Blonde d’Aquitaine), and Limousins showed 591 kg being double of what we measured. However, animals in their experiment were fully developed, at ages between 4 and 12 years. The feed was pasture, corn silage, hay and concentrate feed supplement (Bene et al. 2007 ). In Brahman, the body weight at 200 days of age was 160.72 kg with 102.05 cm hip height. At 400 days of age, body weight increased to 220.35 kg with 115.8 cm hip height. Cattle were fed with pasture supplemented with silage, hay or concentrate (Kamprasert et al. 2019 ). In North Central Nigeria, in White Fulani cows aged between 1.5 and 2.4 years, Yakubu ( 2010 ) established body weight of 116 kg, withers height of 83 cm, hip width of 12 cm, and shoulder width of 18 cm. The author measured much lower values compared to our measurements. No data were available on the feeding and husbandry conditions. Limousin female calves bred in Bulgaria showed 126.5 cm withers height and 13.2 cm pin width at 1 year of age. They measured 14 cm higher withers height and 0.7 cm greater pin width. The feed included pasture and concentrate fodder (Karamfilov et al. 2020 ). Differences can be accounted for the differences in genetics, feed composition, climate and also for husbandry conditions. Table 1 Descriptive statistics (mean, standard deviation and coefficient of variation) of the age-adjusted and raw data of weaning bulls (n = 469) regarding age, weight and body measurement parameters Body measurement parameters Mean Std. Deviation CV% First age at measurement (days) 227.94 21.087 9.25 Body weight (kg) 307.04 41.434 13.49 Corrected body weight (kg) 307.04 34.771 11.32 Withers height (WH) (cm) 112.25 4.646 4.14 Corrected WH (cm) 112.25 4.201 3.74 Tail height (TH) (cm) 121.57 4.706 3.87 Corrected TH (cm) 121.57 4.211 3.46 Back length (BL) (cm) 61.80 3.696 5.98 Corrected BL (cm) 61.80 3.501 5.67 Shoulder width (SW) (cm) 32.73 2.605 7.96 Corrected SW (cm) 32.73 2.521 7.70 Hip width (HW) (cm) 37.97 2.431 6.40 Corrected HW (cm) 37.97 2.296 6.05 Pin width (PW) (cm) 12.52 1.022 8.16 Corrected PW (cm) 12.53 0.998 7.97 Body mass index (kg*100/WH) 272.81 29.702 10.89 Corrected body mass index 273.09 25.272 9.25 Overgrowth index (TH*100/WH) 108.32 1.221 1.13 Corrected overgrowth index 108.32 1.220 1.13 * Corrected weight and body measurement parameters were adjusted to age 228 days. *Std. deviation: standard deviation CV%: Coefficient of variation 3.2. Correlations between body parameters Results on the correlation between body parameters measured at weaning (at an average age of 230 days) are shown in Table 2 a. There was a moderately strong and strong correlation between body parameters and weight (SW1: r = 0.592, WH1: r = 0.773) at the time of weaning. The exception to this was pin width, where a weak correlation was observed (PW1: r = 0.293). It is worth mentioning that there was a weak negative correlation (r= -0.166) between weaning weight and overgrowth index (n = 817, P ≤ 0.001). Hip width (HW1) was moderately correlated with withers height (WH1), tail height (TH1), back length (BL1), and shoulder width (SW1) (r = 0.419–0.694). The average correlation coefficient between the traits was r AVG = 0.581 (n = 817, P ≤ 0.001). Pin width (PW1) showed a weak positive correlation with all traits, with the weakest correlation in the case of SW1 (r = 0.096) and the strongest in the case of WH1 (r = 0.293). The overgrowth index showed weak and negative correlations with values between r= -0.001 and r= -0.354 (n = 817, P ≤ 0.001). Results on the correlation between body parameters measured at 13–14 months of age (average age of 434 days) are shown in Table 2 b. There was a strong positive correlation between body measurements taken at 13–14 months of age and body weight (BL2: r = 0.726, SW2: r = 0.812) (n = 817, P ≤ 0.001). The exception in this case was PW2, where a weak negative correlation (r= -0.009) was observed, but it was not statistically substantiated ( P > 0.05). A weak negative correlation (r= -0.192) was also observed between body weight and the overgrowth index (n = 817, P ≤ 0.001). The WH2, TH2, BL2, SW2, and HW2 traits were moderately strongly correlated with each other, but overall, the correlations were stronger than those observed in the weaning results. The lowest value (moderate correlation; r = 0.424) was between TH2 and HW2, while the highest (very strong correlation; r = 0.958) was between WH2 and TH2. The average correlation coefficient between the traits was r AVG = 0.640 (n = 817, P ≤ 0.001). Pin width (PW2) showed a weak correlation with all traits, with the weakest correlation in the case of SW2 (r= -0.93) and the strongest in the case of TH2 (r = 0.165). The BMI showed the highest (very strong) correlation with body weight (r = 0.989). The overgrowth index also showed weak negative correlations with values between r= -0.251 and r= -0.027 (n = 817, P ≤ 0.001). Cattle body measurements during growth are influenced by several factors, including genetics, nutrition, and management practices. For instance, Warman et al. ( 2023 ) performed a morphometric analysis of Limousin–Bali crossbred cattle, noting increases in withers height and hip height throughout their growth phases. The study highlighted that Limousin crosses generally exhibit larger body dimensions compared to their Bali counterparts. In the study undertaken by Yakubu ( 2010 ) in White Fulani cows aged between 1.5 and 2.4 years, the highest correlation was observed between withers height and rump height (r = 0.98) and the lowest was established between rump height and shoulder width (0.51). Conversely, the lowest value was recorded for rump height and shoulder width (r = 0.51). Similar to our findings, in Cumra, in Holstein cows of between 26 and 36 months of age, Tasdemir et al. ( 2011 ) found medium correlations of 0.66 and 0.63 between body weight and withers height and body weight and hip width. Rezende et al. ( 2022 ) found a correlation of 0.76 between body weight at 210 days of age and at 365 days of age in Limousin cattle, suggesting that early growth performance is a strong predictor of later body weight. In Indonesia, in Bali breed, Azis et al. ( 2023 ) reported strong positive correlations between body weight and hip height (r = 0.756), body weight and body length (r = 0.754), and body weight and chest girth (r = 0.877) highlighting the close relationship between live weight and body conformation traits. These findings suggest that body measurements may serve as reliable predictors of body weight through regression-based approaches, particularly in field conditions where direct weighing is not feasible. Similar results were established by Budianto et al. (2021), who observed positive correlations (r = 0.619–0.809) between body weight and several morphometric traits, including chest depth, width and circumference, abdominal circumference, withers height, pelvic height, hip height, and rump width in Limousin x Peranakan Ongole crossbred slaughter cattle. In Ongole cattle aged 2–5 years of age in South Lampung Regency, Dakhlan et al. ( 2024 ) reported strong positive correlations between body weight and chest girth (r = 0.91), as well as body weight and body length (r = 0.91). These results further indicate that linear body measurements can be effectively used for predicting body weight and provide a practical basis for selection aimed at improving growth performance and meat production. Table 2 a. Correlations between body parameters measured at weaning (at an average age of 230 days) (n = 817) Weight1 Weight1 WH1 TH1 BL1 SW1 HW1 PW1 BMI1 OI1 1 0.770** 0.773** 0.652** 0.592** 0.656** 0.293** 0.975** -0.166** WH1 1 0.964** 0.575** 0.494** 0.558** 0.293** 0.611** -0.354** TH1 1 0.566** 0.515** 0.594** 0.283** 0.627** -0.094** BL1 1 0.430** 0.419** 0.232** 0.612** -0.167** SW1 1 0.694** 0.096** 0.568** -0.039 HW1 1 0.199** 0.623** -0.001 PW1 1 0.263** -0.104** BMI1 1 -0.87* OI1 1 *Weight: body weight (kg), WH: withers height (cm), TH: tail height (cm), BL: back length (cm), SW: shoulder weight (cm), HW: hip width (cm), PW: pin width (cm), BMI: body mass index, OI: overgrowth index ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed) Table 2 b. Correlations between body parameters measured at an average age of 434 days (n = 817) Weight2 Weight2 WH2 TH2 BL2 SW2 HW2 PW2 BMI2 OI2 1 0.761** 0.729** 0.726** 0.812** 0.762** -0.009 0.989** -0.192** WH2 1 0.958** 0.705** 0.546** 0.613** 0.123** 0.660** -0.246** TH2 1 0.683** 0.490** 0.424** 0.165** 0.632** 0.041 BL2 1 0.551** 0.551** 0.108** 0.682** -0.148** SW2 1 0.674** -0.093** 0.821** -0.251** HW2 1 0.122** 0.753** -0.027 PW2 1 -0.031 0.132** BMI2 1 -0.168** OI2 1 *Weight: body weight (kg), WH: withers height (cm), TH: tail height (cm), BL: back length (cm), SW: shoulder weight (cm), HW: hip width (cm), PW: pin width (cm), BMI: body mass index, OI: overgrowth index ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed) 3.3. Correlations between the parameters of repeated measurements The results indicated a weaker correlation between body measurements taken at weaning age (r AVG = 0.581) than at 13–14 months of age (r AVG = 0.640, n = 817). With regard to withers height (WH), a positive, moderately strong correlation was found between the two measurement time points (Fig. 2 a), with a standard error of 3.95 cm (n = 817, r = 0.618, r 2 = 0.382, SE = 3.952; P < 0.001). In the case of tail height (TH), a moderately strong positive correlation was also observed between the two measurement time points (Fig. 2 b) with a standard error of 4.08 cm (n = 817, r = 0.631, r 2 = 0.398, SE = 4.079; P < 0.001). In a study involving Hungarian Simmental, Hereford, Aberdeen Angus, Red Angus, Lincoln Red, Limousin, Charolais, Blonde d’Aquitaine and Shaver cows aged 4–12 years, withers height showed variable correlations with age depending on the breed. In Hungarian Simmental, a weak, positive correlation was observed (r= 0.08), while Hereford also exhibited a weak positive correlation (r= 0.32). In contrast, a weak negative correlation was found in Angus (r= -0.06), and the overall mean across all breeds, withers height had a minus and weak correlation (r= -0.01) with age (Bene et al. 2007). Consequently, the authors found somewhat controversial results compared to our findings. Such discrepancies may be attributed to differences in breed, age, feeding and husbandry management. In the study of Vargas et al. (2000), the phenotypic correlation of hip height (in our case tail height) at weaning and post-weaning (at 18 months of age) in Brahman cattle was r= -0.46 which was lower than in our case. The difference may be accounted for breed, weight and sex alterations. In the case of back length (BL), a moderate positive correlation was found for between the two measurement time points with a standard error of 4.15 cm (n = 817, r = 0.415, r2 = 0.172, SE = 4.151; P < 0.001) ( Fig. 3 a). In the case of shoulder width (SW), a weak positive correlation was detected between the two measurement time points with a standard error of 3.96 cm (n = 817, r = 0.267, r 2 = 0.071, SE = 3.966; P < 0.001) (Fig. 3 b). No studies reporting correlations for repeated measures of back length (BL) were identified in the international literature. However, a related study examined the correlation between body length and the rate of maturation, defined as the postnatal growth rate relative to the mature size, and reported a strong negative correlation of -0.90 (Hafiz et al., 2014). Furthermore, Bene et al. (2007) reported correlations between age and body length of r= 0.37 for Hungarian Simmental, r= 0.03 for Hereford, and r= 0.20 for Angus aged 4–12 years. These correlations are lower than those we observed, however, correlation for Hungarian Simmental were relatively close to our result. Differences between studies may be related to alterations in breeds, sex, ages, and environmental conditions. No similar studies regarding correlations in shoulder width by age have been reported in the international literature to enable comparison. *Points indicate the results of the first (BL1; SW1) and second (BL2; SW2) measurements. Relationship between data is illustrated by the linear regression line. In the case of hip width (HW), a moderate positive correlation was found between the two measurement time points with a standard error of 3.08 cm (n = 817, r = 0.433, r 2 = 0.188, SE = 3.079; P < 0.001) (Fig. 4 a). Regarding hip width, age-related associations are available in the case of age at first calving in Hanwoo beef cows. Authors indicated very weak negative phenotypic correlations between HW and age at first calving (AFC) (r= -0.013 for 24 months) suggesting minimal association between body size and reproductive timing (Shin et al. 2021 ). Comparing these findings, it can be concluded that while HW is a useful indicator of growth and body development, it has limited predictive value for AFC, highlighting the need to consider additional traits when selecting for reproductive performance. In the case of pin width (PW), a weak positive correlation was detected between the two measurement time points with a standard error of 0.95 cm (n = 817, r = 0.264, r 2 = 0.070, SE = 0.956; P < 0.001) (Fig. 4 b). No similar studies regarding correlations in shoulder width by age have been reported in the international literature to enable comparison. *Points indicate the results of the first (HW1; PW1) and second (HW2; PW2) measurements. Relationship between data is illustrated by the linear regression line. 3.4. Results for calves kept for breeding and fattening The weight and body size data of the bulls at the time of weaning were compared according to whether the breeder kept them for fattening or breeding. The study included 928 bull calves, of which 469 were kept for breeding and 459 for fattening after weaning. As shown in Table 3 , the fattening group had higher standard deviation values, even though the breeding group had higher average values, except for pin width. There was no difference between the ages of the two groups ( P > 0.05). Based on the results, we found that, with the exception of the overgrowth index and pin width, the breeding group exceeded the results of the fattening bulls in all parameters ( P < 0.05). The greatest difference was found in the measured weight, where the breeding group exceeded the results of the fattening group by 37.233 kg ( P < 0.001). In terms of body size, there were slight differences between the groups, ranging from 1.168 to 3.162 cm. In the case of PW, although the difference of 0.159 cm is statistically substantiated, practical experience and the standard error of measurements suggest that it is negligible. Body measurements are essential from both breeding and fattening aspects for selection, the analysis of body weight development, the estimation of breeding value, slaughter value and meat production and information on animal health (Li et al. 2022 ). Bene et al. ( 2007 ) measured the overgrowth index of breeding-purpose cows from various cattle breeds including Hungarian Simmental, Hereford, Aberdeen Angus, Red Angus, Lincoln Red, Limousin, Charolais, Blonde d’Aquitaine and Shaver cows aged between 4 and 12 years, and found similar overgrowth indexes ranging between 101 and 104. For limousine, the index was 103.84, showing slightly lower value compared to ours which can be explained by sex difference. Studies dealing with both breeding and fattening-purpose cattle populations is not available in the international literature. Our analysis, in this regard, can be gap-filling. Table 3 Comparisons of body measurement parameters on bull calves with group statistics kept for breeding (B) (n = 469) and fattening (F) (n = 459) Body measurement parameters Breeding/Fattening Mean Std. Deviation CV% Age at first measurement (days) B 227.94 b 21.087 9.3 F 223.28 a 60.983 27.3 Body weight (kg) B 307.04 b 41.434 13.5 F 269.81 a 47.240 17.5 Withers height (WH) (cm) B 112.25 b 4.646 4.1 F 109.10 a 5.824 5.3 Tail height (TH) (cm) B 121.57 b 4.706 3.9 F 118.41 a 5.860 4.9 Back length (BL) (cm) B 61.80 b 3.696 6.0 F 58.64 a 4.210 7.2 Shoulder width (SW) (cm) B 32.73 b 2.605 8.0 F 30.72 a 3.534 11.5 Hip width (HW) (cm) B 37.97 b 2.431 6.4 F 36.80 a 3.340 9.1 Pin width (PW) (cm) B 12.52 a 1.022 8.2 F 12.37 a 1.022 8.3 Body mass index (kg*100/WH) B 272.81 b 29.702 10.9 F 245.60 a 36.005 14.7 Overgrowth index (TH*100/WH) B 108.32 a 1.221 1.1 F 108.56 b 1.567 1.4 a,b Different letter within a row of each body measurement parameter between “B” and “F” indicate significant difference ( P < 0.05) *Std. deviation: standard deviation CV% Coefficient of variation *B: breeding; F: fattening Detailed statistical differences between the breeding and fattening groups regarding Levene's Test for Equality of Variances and t-test for Equality of Means are illustrated in Table 4 . Levene’s test indicated that the assumption of homogeneity of variances was violated for most traits ( P < 0.05), except for pin width, where equal variances could be assumed ( P = 0.956). The t-test revealed statistically significant differences between the breeding groups for nearly all measured parameters ( P < 0.05), except for age at first measurement ( P = 0.122). The magnitude and direction of the mean differences indicate that one of the breeding groups consistently exhibited higher values across most body dimensions, particularly for body weight and body mass index, while the negative mean difference observed for the overgrowth index suggests a relative proportional variation between height traits. Table 4 Mean statistical differences (Levene's Test for Equality of Variances and t-test for Equality of Means) in body measurement parameters between the breeding groups Body measurement parameters Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Significance Two-Sided P Mean Difference Std. Error Difference Age at first measurement (days) 7.580 0.006 1.549 563.900 0.122 4.661 3.008 Body weight (kg) 6.910 0.009 12.746 903.587 0.000 37.233 2.921 Withers height (WH) (cm) 20.035 0.000 9.106 874.244 0.000 3.154 0.346 Tail height (TH) (cm) 17.161 0.000 9.051 876.745 0.000 3.162 0.349 Back length (BL) (cm) 5.469 0.020 12.113 905.381 0.000 3.153 0.260 Shoulder width (SW) (cm) 45.577 0.000 9.869 841.725 0.000 2.015 0.204 Hip width (HW) (cm) 27.340 0.000 6.082 836.193 0.000 1.168 0.192 Pin width (PW) (cm) 0.003 0.956 2.362 925.570 0.018 0.159 0.067 Body mass index (kg*100/WH) 9.350 0.002 12.545 886.431 0.000 27.214 2.169 Overgrowth index (TH*100/WH) 4.081 0.044 -2.679 865.179 0.008 -0.247 0.092 *df: degree of freedom; Std. Error Difference: standard error of the difference 3.5. Limitations of the study and avenues for future research The study aimed to identify parameters describing the relationship between body measurements recorded at weaning and at one year of age to support early selection in practical breeding programs. By systematically collecting body size data, breeders are provided with the opportunity to perform early selection of breeding animals based on objective measurements, which may result in significant economic advantages. Beyond the numerous strengths highlighted throughout the manuscript, it is also important to acknowledge several limitations inherent to the present study. These constraints should be considered when interpreting the findings and their broader applicability. One of the main challenges of the study is the lack of standardized experimental conditions. In Hungary, breeding populations are typically small, making it difficult to examine large numbers of animals (e.g., over 100 individuals) of the same age, measured at the same time, and raised under identical conditions. As the dataset was compiled from multiple farms, where animals are kept under broadly similar but not identical conditions, the timing of data collection varies across the year. In addition, animals included in the study differ in age and do not originate from the same weaning groups. To address these limitations, a large database was assembled by collecting extensive data, enabling statistically reliable comparisons. As a result, differences in age within the dataset do not show statistically significant effects among the examined animals. If a breeding population of sufficient size were available, where a large number of animals could be weaned simultaneously, repeated controlled experiments could potentially yield more robust and reliable results. However, such conditions are currently not feasible. For future analyses, this research could be continued by expanding the dataset with data based on artificial intelligence and machine learning approaches to reveal deeper relationships. It could enhance early selection strategies and enable the prediction of expected phenotypic performance of the animals. 4. Conclusions In conclusion, withers height (WH) and tail height (TH) proved to be the most effective traits for early selection. A progressive increase in correlation coefficients between body weight and linear body measurements at later stages indicates that the initially heterogeneous calf population becomes more uniform by 13–14 months of age. Although differences between breeding and fattening groups were modest, selected breeding bulls consistently showed superior performance, confirming that current selection practices are reflected in measurable traits. It can therefore be seen that although breeders select on the basis of development, this tendency is also reflected in the measured parameters, implicating that data-based selection is possible. Accordingly, it is recommended that the Limousin breeding programme be simplified by omitting pin width (PW) and focusing on weight and height measurements at weaning. Furthermore, the development of farm-specific decision-support tools (i.e. a platform for breeders) could enhance pre-selection efficiency. This study contributes novel insights by demonstrating the value of longitudinal body measurements for early selection in beef cattle. Declarations Statement of Animal Rights Ethical clearance was granted by the Institutional Animal Welfare and Use Committee of Széchenyi István University Faculty of Agricultural and Food Sciences (clearance number: SZE-AKMK/MAB/012/2024). Conflict of Interest Statement The authors declare no conflict of interest. ACKNOWLEDGEMENTS The authors would like to thank the Association of Hungarian Limousin and Blonde d’Aquitaine Breeders for providing the opportunity to conduct the measurements and supply the data; the University for providing the facilities; and the Ministry of Culture and Innovation and the National Research, Development and Innovation Fund programme (2024 − 2.1.2-EKÖP-KDP-2024_00016) for the bursary that supported the research. Data Availability Statements The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns. References Abo-Ismail MK, Brito LF, Miller SP, Sargolzaei M, Grossi DA, Moore SS, Plastow G, Stothard P, Nayeri S, Schenkel FS (2017) Genome-wide association studies and genomic prediction of breeding values for calving performance and body conformation traits in Holstein cattle. Genet Sel Evol 49:82. 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Tasdemir S, Abdullah U, Seref I (2011) Determination of body measurements on Holstein cows using digital image analysis. Comput Electron Agric 76:189–197. DOI: https://doi.org/10.1016/j.compag.2011.02.001 Terefe E, Dessie T, Haile A, Mulatu W, Mwai O (2015) Phenotypic characterization of Mursi cattle. Anim Genet Resour 57:15–24. DOI: https://doi.org/10.1017/S2078633615000132 Traoré A, Koudandé DO, Fernández I, Soudré A, Álvarez I, Diarra S, Diarra F, Kaboré A, Sanou M, Tamboura HH, Goyache F (2016) Multivariate characterization of morphological traits in West African cattle. Arch Anim Breed 59:337–344. DOI: https://doi.org/10.5194/aab-59-337-2016 Vargas CA, Elzo MA, Chase CC, Olson TA (2000) Genetic parameters between hip height and weight in Brahman cattle. J Anim Sci 78:3045–3052. DOI: https://doi.org/10.2527/2000.78123045x Warman AT, Fadhilah GT, Atmoko BA, Ibrahim A, Baliarti E, Panjono P (2023) Morphometric characteristics of Limousin-Bali crossbred cows. IOP Conf Ser Earth Environ Sci 1183:012036. DOI: https://doi.org/10.1088/1755-1315/1183/1/012036 Watanabe D, Ikeda H, Kazamatsuri H, Ando T, Ohtsuka H, Kobayashi S, Oikawa M, Sugimoto Y (2010) Growth and biochemical changes in Japanese Black cattle. J Vet Med Sci 72:1203–1208. DOI: https://doi.org/10.1292/jvms.09-0393 Wu Z, Zhang J, Li J, Zhao W (2024) Automated cattle body size measurement using multi-view fusion. Animals 14:3190. DOI: https://doi.org/10.3390/ani14223190 Yakubu A (2010) Fixing multicollinearity in prediction of body weight in cattle. J Cent Eur Agric 11:487–492. DOI: https://doi.org/10.5513/jcea01/11.4.872 Yang L, Jiang T, Gui X, Duan Q (2024) Automated body measurement of beef cattle using keypoint detection. Meas Sci Technol 35:126013. DOI: https://doi.org/10.1088/1361-6501/ad73f4 Zhao S, Jin H, Xu L, Jia Y (2022) Polymorphisms affecting body conformation traits in cattle. Animals 12:1601. DOI: https://doi.org/10.3390/ani12131601 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9277528","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622603444,"identity":"5fbd0523-c21f-4ceb-a42d-768e4b34ac90","order_by":0,"name":"Márton János Demény","email":"","orcid":"","institution":"Magyar Agrar- es Elettudomanyi Egyetem","correspondingAuthor":false,"prefix":"","firstName":"Márton","middleName":"János","lastName":"Demény","suffix":""},{"id":622603445,"identity":"a5ec818e-a001-4634-baa2-53e6ab8f3440","order_by":1,"name":"Lili Dóra Brassó","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBACAyjN2ADED2BsCSA3gRgtzAYka2GTgIni1WIuffjxxx8VDLL90oefVfz4dY/B4HZz442POxjyDA5g12LZl2YmzXOGwXgmkHGzt6+YweDOwWbLmWcYinFpMTjDYMbM2MaQuAHIuMHbk8BgcCOxTZoXJIJTC/vnjz/BWti/Ff6FafmLVwuPgQTYzDM8Zsw8P6BaGPFosezhKQP6RcJ4Zg9PsbRsQwKP5I3EZsveMxKJM3FoMedh3wwMMRvZfh72jR/f/EmQ47uR/vDGzx02iX04tEABNEaA7uFBFSEM/hCrcBSMglEwCkYSAAC3H2FPNqI0ZwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-0256-4892","institution":"University of Debrecen: Debreceni Egyetem","correspondingAuthor":true,"prefix":"","firstName":"Lili","middleName":"Dóra","lastName":"Brassó","suffix":""},{"id":622603446,"identity":"959bb05a-f78f-486d-b6a2-4bb8df880813","order_by":2,"name":"Márton Szűcs","email":"","orcid":"","institution":"Limousin- és Blond'd Aquitaine Tenyésztők Egyesülete","correspondingAuthor":false,"prefix":"","firstName":"Márton","middleName":"","lastName":"Szűcs","suffix":""},{"id":622603447,"identity":"9ebe6b92-8664-46a7-93b5-69f66039f4f9","order_by":3,"name":"János Tőzsér","email":"","orcid":"","institution":"Magyar Agrar- es Elettudomanyi Egyetem","correspondingAuthor":false,"prefix":"","firstName":"János","middleName":"","lastName":"Tőzsér","suffix":""}],"badges":[],"createdAt":"2026-03-31 09:22:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9277528/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9277528/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107453706,"identity":"55f65ac6-3d65-4669-9276-fef5eb1d3d4b","added_by":"auto","created_at":"2026-04-21 15:36:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68893,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of the key body measurement points on the cattle’s body including heights, widths and length and the measuring equipment\u003c/p\u003e\n\u003cp\u003e*inhibitors indicate points measured with measuring stick, while arrows demonstrate tape measurements\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9277528/v1/8d98e87c1e191c3f902b8690.jpg"},{"id":107489536,"identity":"bcb3acf6-8eea-4a0c-a3b4-1334e906d9ae","added_by":"auto","created_at":"2026-04-22 02:48:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":134605,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea and 2b\u003c/strong\u003e Correlations between the first (at an average age of 230 days) and second (at an average age of 434 days) withers height (WH) and tail height (TH) measurements\u003c/p\u003e\n\u003cp\u003e*Points indicate the results of the first (WH1; TH1) and second (WH2; TH2) measurements. Relationship between data is illustrated by the linear regression line.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9277528/v1/0ba9aceeabf681b97045216b.jpg"},{"id":107453708,"identity":"b8c10cd5-c551-4559-a74a-ce649e6f4815","added_by":"auto","created_at":"2026-04-21 15:36:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":127226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea and 3b\u003c/strong\u003e Correlations between the first (at an average age of 230 days) and second (at an average age of 434 days) back length (BL) and shoulder width (SW) measurements.\u003c/p\u003e\n\u003cp\u003e*Points indicate the results of the first (BL1; SW1) and second (BL2; SW2) measurements. Relationship between data is illustrated by the linear regression line.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9277528/v1/08c488d689054108239dc583.jpg"},{"id":107490260,"identity":"06196ebb-fca0-4339-b89d-b7c0b5666c63","added_by":"auto","created_at":"2026-04-22 02:51:27","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":93970,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea and 4b\u003c/strong\u003e Correlations between the first (at an average age of 230 days) and second (at an average age of 434 days) hip width (HW) and pin width (PW) measurements.\u003c/p\u003e\n\u003cp\u003e*Points indicate the results of the first (HW1; PW1) and second (HW2; PW2) measurements. Relationship between data is illustrated by the linear regression line.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9277528/v1/cf255b6885b87eadae9da5fb.jpg"},{"id":108182101,"identity":"3cc27448-48be-45e1-b468-68495fc978f2","added_by":"auto","created_at":"2026-04-30 08:59:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":980399,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9277528/v1/d4a77acd-0a5d-4ed0-999a-e582a4cc68e6.pdf"}],"financialInterests":"","formattedTitle":"Possibility of early selection of Limousin young stock based on analysis of repeatedly measured body size parameters","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe measurement of body parameters in cattle, including Limousin breed, is a crucial aspect of livestock management that impacts animal performance, productivity, and overall herd health. Numerous studies have demonstrated strong relationships between body measurements, conformation traits and growth or productive performance, highlighting their value in breeding and management decisions (Koenen and Groen \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Abreu et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dominguez-Casta\u0026ntilde;o et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Morphological traits can effectively differentiate cattle breeds, reflecting genetic diversity and environmental adaptation (Traor\u0026eacute; et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Body weight and body size parameters recorded at weaning are reliable indicators of growth and are widely used to evaluate breeding efficiency and heritability in cattle populations (Nurgiartiningsih et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Also, the application of genetic markers associated with body measurement traits plays a key role in determining cattle conformation and growth traits (Gao et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Several studies have shown that body dimensions can be used to accurately estimate live weight, providing practical benefits for nutritional planning and economic decision-making in beef production systems (Paputungan et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Haq et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Emerging research indicates that targeted feeding strategies can enhance growth performance consistent with findings on phenotypic diversity and environmental adaptation in cattle (Terefe et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Comprehensive body measurements enable informed management decisions that support animal health and farm profitability, while also providing a basis for exploring the genetic background of growth traits. As highlighted by Kamprasert et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the heritability of body measurements, including frame scores, offers a framework for breeding programs aimed at optimizing growth performance in various cattle breeds. This genetic perspective is essential as it enables producers to select for traits that can enhance growth rates while potentially improving overall herd quality. The significance of measuring weight and body parameters at various ages in beef cattle to fit growth models that describe the development of these animals have been demonstrated by the international literature (Crispim et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ribeiro et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The use of random regression models in addition to single trait models enabled researchers to pinpoint functional candidate genes involved in body weight variations (Ribeiro et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e The reviewed studies demonstrate that evaluating morphological traits, body weight, and body measurements is essential for improving beef cattle production, supporting efficient management, and aligning growth with market demands. However, information on repeated body measurements in Limousin cattle across different growth stages remains limited. Therefore, the study aimed to assess the practical use of body size data for preselection of the Limousin breed by analysing correlations between measurements taken at two time points and by comparing bull calves selected for breeding and fattening. Key body measurements including withers height, tail height, back length, shoulder width, hip width and pin width, serve as significant indicators of physiological and genetic status. Evaluating these traits supports improved understanding of growth patterns and informs breeding, feeding, and management decisions to optimize Limousin cattle performance. In the study, we hypothesised that significant relationships exist between the body size measurements recorded at the two time points, particularly for withers height and tail height due to the more uniform and consistent development of these body parts compared to others. We further hypothesized that body size data would be more homogeneous and that stronger correlations among traits would be observed at the second measurement time point. Additionally, when comparing breeding and fattening animals, we assumed that calves selected for breeding would perform better than the population average in body weight and at least one body size parameter at the time of selection. Based on these assumptions, our key hypothesis is that body weight and body size parameters can be effectively used for preselection, offering substantial economic benefits and time savings in practical breeding programs.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study population and the measured parameters\u003c/h2\u003e \u003cp\u003eThe study compared data from three years (2022\u0026ndash;2024) involving 14 Limousin breeding farms. The breeding farms were all located in the same region of Hungary, Transdanubia, belonging to Zala, Veszpr\u0026eacute;m, and Fej\u0026eacute;r counties. In accordance with strict rules governing breeding farms, calves were raised using similar technology until weaning, supplemented with calf feed on pasture. The 13\u0026ndash;14-month-old breeding heifers were also kept in a similar manner, in open-air stables using a feeding technology based on concentrate feed and hay. The technology for keeping bulls participating in the own performance test is the most clearly defined, where they must achieve a minimum performance in groups of 2\u0026ndash;5 animals, in deep litter or open pens, with a diet based on concentrate feed and hay in order to qualify as breeding bulls. In general, test period lasts 150\u0026ndash;180 days and must be completed by the time the animals reach a maximum age of 450 days. The guidelines for the husbandry and feeding technologies, registration and qualification of bulls, as well as data collection are set in the breeding program of the Association of Hungarian Limousin and Blonde d'Aquitaine Breeders which is available on the website of the association in Hungarian (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.limousin.hu\u003c/span\u003e\u003cspan address=\"http://www.limousin.hu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA total of 817 breeding cattle including bulls (n\u0026thinsp;=\u0026thinsp;469) and heifers (n\u0026thinsp;=\u0026thinsp;348) were examined for body size data. Body measurements were taken at weaning age (at 230 days average) and at 13\u0026ndash;14 months of age (at an average age of 434 days) in accordance with the breeding regulations of the Association of Hungarian Limousin and Blonde d'Aquitaine Breeders. Thus, each animal had two measurement results, for both bulls and heifers.\u003c/p\u003e \u003cp\u003eFurther evaluations focused on comparing the body size data at weaning age of breeding and fattening bulls (n\u0026thinsp;=\u0026thinsp;928). Half of the bulls with the best qualities were kept further after weaning (n\u0026thinsp;=\u0026thinsp;469) and took part in the own performance test. Another half (n\u0026thinsp;=\u0026thinsp;459) was slaughtered when reached 300 kg or above (600\u0026ndash;700 kg). Both breeding and fattening groups were kept on the same farms with identical feeding, housing and management conditions before and during measurements until weaning. Then, the feed of the two groups differed; fattening bulls received growing concentrate, while breeding animals were fed with breeding concentrate feed. Body measurement points and measuring equipment are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn addition to measuring weight and body parameters at the time of measurement, body mass index (BMI) and overgrowth index (OI) were also calculated.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:BMI=kg\\frac{100}{WH}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:OI=TH\\frac{100}{WH}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formulae, TH denotes the term \u0026ldquo;tail height\", and WH denotes the term \"withers height\".\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e*inhibitors indicate points measured with measuring stick, while arrows demonstrate tape measurements\u003c/p\u003e \u003cp\u003eFigure was created in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://BioRender.com\u003c/span\u003e\u003cspan address=\"https://BioRender.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eFirst, the raw and corrected data were compared. For example, in the case of withers height, the data were corrected using the following equation:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\text{W}\\text{H}\\text{c}\\:=\\:\\text{W}\\text{H}\\text{i}\\:\\pm\\:\\text{b}\\:\\left(\\text{A}\\text{G}\\text{a}\\text{v}\\text{g}\\:-\\:\\text{A}\\text{g}\\text{i}\\right)$$\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere \"WHc\" is the corrected withers height (cm), \"WHi\" is the withers height of the given individual (cm), \"b\" is the regression coefficient, \"AGavg\" is the average age of the group (day), and \"AGi\" is the age of the given individual (day).\u003c/p\u003e \u003cp\u003eThe measurement error was calculated using the following formula, where N is the number of individuals, λ\u0026thinsp;=\u0026thinsp;2.57624, SD\u0026thinsp;=\u0026thinsp;4.649 and X\u0026thinsp;=\u0026thinsp;112.25 cm.\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:N\u0026gt;{\\left(\\frac{\\lambda\\:\\times\\:SD}{SEM}\\right)}^{2}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBased on the formula, the result shows that the measurement error is 2 cm for 36 individuals, 1.5 cm for 64 individuals, and 1 cm for 143 individuals (α\u0026thinsp;=\u0026thinsp;0.01; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.99). Since in our case the number of elements exceeded 143 in all cases, we used a measurement error of 1 cm in the analysis of statistical correlations.\u003c/p\u003e \u003cp\u003eCorrelation levels between body weight and body parameters were determined according to (Szab\u0026oacute; et al. 2004), as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eI r I\u0026thinsp;\u0026lt;\u0026thinsp;0.4: weak correlation\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e0.4\u0026thinsp;\u0026lt;\u0026thinsp;I r I\u0026thinsp;\u0026lt;\u0026thinsp;0.7: moderate correlation\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e0.7\u0026thinsp;\u0026lt;\u0026thinsp;I r I\u0026thinsp;\u0026lt;\u0026thinsp;0.8: strong correlation\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eI r I\u0026thinsp;\u0026gt;\u0026thinsp;0.9: very strong correlation\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis defines the categories more strictly than is generally the case in the international literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe data were evaluated using the Statistical Package for Social Sciences (SPSS 24.0) software package. Descriptive statistics, involving mean and standard deviation were conducted to compare the age-adjusted and raw data of weaning bulls including various body measurement parameters, age and weight at measurement. Paired-Samples T Test was applied for the comparison of means of age-adjusted and raw data. Correlations between body measurement parameters were analysed with Pearson\u0026rsquo;s Bivariate Correlation. Linear function fitting was performed between the first and second measurements of each body size. In the analysis, the independent variable was the data of the first measurement, while the dependent variable was the results of the second measurement. A significance level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was adopted as the threshold for statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cp\u003eSeveral studies have demonstrated that body weight and linear body measurements play a crucial role in selection decisions aimed at improving desirable genetic traits, growth potential, carcass yield, and overall animal health in cattle populations (Kamprasert et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Naserkheil et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nurgiartiningsih et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, the relationship between body parameter assessment and preselection in cattle breeding is an essential aspect of sustainable livestock production and genetic improvement strategies. As the demand for high-quality beef products increases, the focus on efficient selection methods becomes paramount.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Descriptive statistics of the age-adjusted and raw data of weaning bulls\u003c/h2\u003e \u003cp\u003eFor individual body measurements and calculated indices, the correlations between corrected and raw data ranged from r\u0026thinsp;=\u0026thinsp;0.847 to r\u0026thinsp;=\u0026thinsp;0.999 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For both heifers and bulls, we obtained similar or even stronger correlations for the results measured at both measurement times. The Paired Sample Test did not confirm (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) the differences between the raw and corrected data. Since there was a strong positive correlation between the two databases and the differences were not statistically significant, the raw data were used for further analysis to avoid the distorting effects of correction. CV% for most parameters were below 10% indicating low variability in data. Body weight, corrected body weight and body mass index showed slightly higher, moderate CV%s.\u003c/p\u003e \u003cp\u003eIn Italy, Rezende et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) measured the body weight of limousine cattle 247 kg at 210 days of age and 411 kg at 365 days of age. Animals had 60 kg lower weight and 18 days younger compared to our study. There was no information available on the grazing, feeding and climatic conditions for further conclusions. In Indonesia, in Sumba Ongole cattle, mean body weight of bulls at 673 days old was 299.kg with 122 cm withers height. Cattle were fed Elephant grass (\u003cem\u003ePennisetum purpureum\u003c/em\u003e), rice straw and cassava meal \u003cem\u003ead libitum\u003c/em\u003e (Putra, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In West Java, Limousine bulls ages between 1.5 and 13 years showed body weight between 384 and 1021 kg but authors calculated correlations with chest girth, body length and shoulder height (Adhianto et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Hungarian Simmental, Hereford, Aberdeen Angus, Red Angus, Lincoln Red, Limousin, Charolais, Blonde d\u0026rsquo;Aquitaine and Shaver cows\u0026rsquo; live weight and body measurement were conducted. Authors found that weights ranged between 500 (Hungarian Simmental) and 638 kg (Blonde d\u0026rsquo;Aquitaine), and Limousins showed 591 kg being double of what we measured. However, animals in their experiment were fully developed, at ages between 4 and 12 years. The feed was pasture, corn silage, hay and concentrate feed supplement (Bene et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In Brahman, the body weight at 200 days of age was 160.72 kg with 102.05 cm hip height. At 400 days of age, body weight increased to 220.35 kg with 115.8 cm hip height. Cattle were fed with pasture supplemented with silage, hay or concentrate (Kamprasert et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In North Central Nigeria, in White Fulani cows aged between 1.5 and 2.4 years, Yakubu (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) established body weight of 116 kg, withers height of 83 cm, hip width of 12 cm, and shoulder width of 18 cm. The author measured much lower values compared to our measurements. No data were available on the feeding and husbandry conditions. Limousin female calves bred in Bulgaria showed 126.5 cm withers height and 13.2 cm pin width at 1 year of age. They measured 14 cm higher withers height and 0.7 cm greater pin width. The feed included pasture and concentrate fodder (Karamfilov et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Differences can be accounted for the differences in genetics, feed composition, climate and also for husbandry conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics (mean, standard deviation and coefficient of variation) of the age-adjusted and raw data of weaning bulls (n\u0026thinsp;=\u0026thinsp;469) regarding age, weight and body measurement parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody measurement parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCV%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFirst age at measurement (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e227.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody weight (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e307.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCorrected body weight (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e307.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWithers height (WH) (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCorrected WH (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTail height (TH) (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCorrected TH (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBack length (BL) (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCorrected BL (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eShoulder width (SW) (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCorrected SW (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHip width (HW) (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCorrected HW (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePin width (PW) (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCorrected PW (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody mass index (kg*100/WH)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e272.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCorrected body mass index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e273.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOvergrowth index (TH*100/WH)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCorrected overgrowth index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* Corrected weight and body measurement parameters were adjusted to age 228 days.\u003c/p\u003e \u003cp\u003e*Std. deviation: standard deviation\u003c/p\u003e \u003cp\u003eCV%: Coefficient of variation\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Correlations between body parameters\u003c/h2\u003e \u003cp\u003eResults on the correlation between body parameters measured at weaning (at an average age of 230 days) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea. There was a moderately strong and strong correlation between body parameters and weight (SW1: r\u0026thinsp;=\u0026thinsp;0.592, WH1: r\u0026thinsp;=\u0026thinsp;0.773) at the time of weaning. The exception to this was pin width, where a weak correlation was observed (PW1: r\u0026thinsp;=\u0026thinsp;0.293). It is worth mentioning that there was a weak negative correlation (r= -0.166) between weaning weight and overgrowth index (n\u0026thinsp;=\u0026thinsp;817, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001). Hip width (HW1) was moderately correlated with withers height (WH1), tail height (TH1), back length (BL1), and shoulder width (SW1) (r\u0026thinsp;=\u0026thinsp;0.419\u0026ndash;0.694). The average correlation coefficient between the traits was r\u003csub\u003eAVG\u003c/sub\u003e= 0.581 (n\u0026thinsp;=\u0026thinsp;817, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001). Pin width (PW1) showed a weak positive correlation with all traits, with the weakest correlation in the case of SW1 (r\u0026thinsp;=\u0026thinsp;0.096) and the strongest in the case of WH1 (r\u0026thinsp;=\u0026thinsp;0.293). The overgrowth index showed weak and negative correlations with values between r= -0.001 and r= -0.354 (n\u0026thinsp;=\u0026thinsp;817, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eResults on the correlation between body parameters measured at 13\u0026ndash;14 months of age (average age of 434 days) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb. There was a strong positive correlation between body measurements taken at 13\u0026ndash;14 months of age and body weight (BL2: r\u0026thinsp;=\u0026thinsp;0.726, SW2: r\u0026thinsp;=\u0026thinsp;0.812) (n\u0026thinsp;=\u0026thinsp;817, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001). The exception in this case was PW2, where a weak negative correlation (r= -0.009) was observed, but it was not statistically substantiated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). A weak negative correlation (r= -0.192) was also observed between body weight and the overgrowth index (n\u0026thinsp;=\u0026thinsp;817, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001). The WH2, TH2, BL2, SW2, and HW2 traits were moderately strongly correlated with each other, but overall, the correlations were stronger than those observed in the weaning results. The lowest value (moderate correlation; r\u0026thinsp;=\u0026thinsp;0.424) was between TH2 and HW2, while the highest (very strong correlation; r\u0026thinsp;=\u0026thinsp;0.958) was between WH2 and TH2. The average correlation coefficient between the traits was r\u003csub\u003eAVG\u003c/sub\u003e= 0.640 (n\u0026thinsp;=\u0026thinsp;817, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001). Pin width (PW2) showed a weak correlation with all traits, with the weakest correlation in the case of SW2 (r= -0.93) and the strongest in the case of TH2 (r\u0026thinsp;=\u0026thinsp;0.165). The BMI showed the highest (very strong) correlation with body weight (r\u0026thinsp;=\u0026thinsp;0.989). The overgrowth index also showed weak negative correlations with values between r= -0.251 and r= -0.027 (n\u0026thinsp;=\u0026thinsp;817, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eCattle body measurements during growth are influenced by several factors, including genetics, nutrition, and management practices. For instance, Warman et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) performed a morphometric analysis of Limousin\u0026ndash;Bali crossbred cattle, noting increases in withers height and hip height throughout their growth phases. The study highlighted that Limousin crosses generally exhibit larger body dimensions compared to their Bali counterparts. In the study undertaken by Yakubu (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) in White Fulani cows aged between 1.5 and 2.4 years, the highest correlation was observed between withers height and rump height (r\u0026thinsp;=\u0026thinsp;0.98) and the lowest was established between rump height and shoulder width (0.51). Conversely, the lowest value was recorded for rump height and shoulder width (r\u0026thinsp;=\u0026thinsp;0.51). Similar to our findings, in Cumra, in Holstein cows of between 26 and 36 months of age, Tasdemir et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) found medium correlations of 0.66 and 0.63 between body weight and withers height and body weight and hip width. Rezende et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found a correlation of 0.76 between body weight at 210 days of age and at 365 days of age in Limousin cattle, suggesting that early growth performance is a strong predictor of later body weight. In Indonesia, in Bali breed, Azis et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported strong positive correlations between body weight and hip height (r\u0026thinsp;=\u0026thinsp;0.756), body weight and body length (r\u0026thinsp;=\u0026thinsp;0.754), and body weight and chest girth (r\u0026thinsp;=\u0026thinsp;0.877) highlighting the close relationship between live weight and body conformation traits. These findings suggest that body measurements may serve as reliable predictors of body weight through regression-based approaches, particularly in field conditions where direct weighing is not feasible. Similar results were established by Budianto et al. (2021), who observed positive correlations (r\u0026thinsp;=\u0026thinsp;0.619\u0026ndash;0.809) between body weight and several morphometric traits, including chest depth, width and circumference, abdominal circumference, withers height, pelvic height, hip height, and rump width in Limousin x \u003cem\u003ePeranakan\u003c/em\u003e Ongole crossbred slaughter cattle. In Ongole cattle aged 2\u0026ndash;5 years of age in South Lampung Regency, Dakhlan et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported strong positive correlations between body weight and chest girth (r\u0026thinsp;=\u0026thinsp;0.91), as well as body weight and body length (r\u0026thinsp;=\u0026thinsp;0.91). These results further indicate that linear body measurements can be effectively used for predicting body weight and provide a practical basis for selection aimed at improving growth performance and meat production.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ea.\u003c/b\u003e Correlations between body parameters measured at weaning (at an average age of 230 days) (n\u0026thinsp;=\u0026thinsp;817)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eWeight1\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWH1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTH1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBL1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSW1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHW1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePW1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBMI1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOI1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.770**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.773**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.652**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.592**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.656**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.293**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.975**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.166**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWH1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.964**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.575**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.494**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.558**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.293**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.611**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.354**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTH1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.566**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.515**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.594**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.283**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.627**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.094**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBL1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.430**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.419**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.232**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.612**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.167**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSW1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.694**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.096**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.568**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHW1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.199**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.623**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePW1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.263**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.104**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.87*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOI1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e*Weight: body weight (kg), WH: withers height (cm), TH: tail height (cm), BL: back length (cm), SW: shoulder weight (cm), HW: hip width (cm), PW: pin width (cm), BMI: body mass index, OI: overgrowth index\u003c/p\u003e \u003cp\u003e** Correlation is significant at the 0.01 level (2-tailed)\u003c/p\u003e \u003cp\u003e* Correlation is significant at the 0.05 level (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eb.\u003c/b\u003e Correlations between body parameters measured at an average age of 434 days (n\u0026thinsp;=\u0026thinsp;817)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eWeight2\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWH2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTH2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBL2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSW2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHW2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePW2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBMI2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOI2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.761**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.729**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.726**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.812**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.762**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.989**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.192**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWH2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.958**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.705**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.546**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.613**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.123**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.660**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.246**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTH2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.683**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.490**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.424**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.165**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.632**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBL2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.551**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.551**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.108**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.682**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.148**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSW2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.674**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.093**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.821**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.251**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHW2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.122**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.753**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePW2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.132**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.168**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOI2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e*Weight: body weight (kg), WH: withers height (cm), TH: tail height (cm), BL: back length (cm), SW: shoulder weight (cm), HW: hip width (cm), PW: pin width (cm), BMI: body mass index, OI: overgrowth index\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e** Correlation is significant at the 0.01 level (2-tailed)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e* Correlation is significant at the 0.05 level (2-tailed)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Correlations between the parameters of repeated measurements\u003c/h2\u003e \u003cp\u003eThe results indicated a weaker correlation between body measurements taken at weaning age (r\u003csub\u003eAVG\u003c/sub\u003e= 0.581) than at 13\u0026ndash;14 months of age (r\u003csub\u003eAVG\u003c/sub\u003e= 0.640, n\u0026thinsp;=\u0026thinsp;817). With regard to withers height (WH), a positive, moderately strong correlation was found between the two measurement time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), with a standard error of 3.95 cm (n\u0026thinsp;=\u0026thinsp;817, r\u0026thinsp;=\u0026thinsp;0.618, r\u003csup\u003e2\u003c/sup\u003e= 0.382, SE\u0026thinsp;=\u0026thinsp;3.952; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the case of tail height (TH), a moderately strong positive correlation was also observed between the two measurement time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) with a standard error of 4.08 cm (n\u0026thinsp;=\u0026thinsp;817, r\u0026thinsp;=\u0026thinsp;0.631, r\u003csup\u003e2\u003c/sup\u003e= 0.398, SE\u0026thinsp;=\u0026thinsp;4.079; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn a study involving Hungarian Simmental, Hereford, Aberdeen Angus, Red Angus, Lincoln Red, Limousin, Charolais, Blonde d\u0026rsquo;Aquitaine and Shaver cows aged 4\u0026ndash;12 years, withers height showed variable correlations with age depending on the breed. In Hungarian Simmental, a weak, positive correlation was observed (r= 0.08), while Hereford also exhibited a weak positive correlation (r= 0.32). In contrast, a weak negative correlation was found in Angus (r= -0.06), and the overall mean across all breeds, withers height had a minus and weak correlation (r= -0.01) with age (Bene et al. 2007). Consequently, the authors found somewhat controversial results compared to our findings. Such discrepancies may be attributed to differences in breed, age, feeding and husbandry management. In the study of Vargas et al. (2000), the phenotypic correlation of hip height (in our case tail height) at weaning and post-weaning (at 18 months of age) in Brahman cattle was r= -0.46 which was lower than in our case. The difference may be accounted for breed, weight and sex alterations.\u003c/p\u003e \u003cp\u003eIn the case of back length (BL), a moderate positive correlation was found for between the two measurement time points with a standard error of 4.15 cm (n\u0026thinsp;=\u0026thinsp;817, r\u0026thinsp;=\u0026thinsp;0.415, r2\u0026thinsp;=\u0026thinsp;0.172, SE\u0026thinsp;=\u0026thinsp;4.151; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In the case of shoulder width (SW), a weak positive correlation was detected between the two measurement time points with a standard error of 3.96 cm (n\u0026thinsp;=\u0026thinsp;817, r\u0026thinsp;=\u0026thinsp;0.267, r\u003csup\u003e2\u003c/sup\u003e= 0.071, SE\u0026thinsp;=\u0026thinsp;3.966; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eNo studies reporting correlations for repeated measures of back length (BL) were identified in the international literature. However, a related study examined the correlation between body length and the rate of maturation, defined as the postnatal growth rate relative to the mature size, and reported a strong negative correlation of -0.90 (Hafiz et al., 2014). Furthermore, Bene et al. (2007) reported correlations between age and body length of r= 0.37 for Hungarian Simmental, r= 0.03 for Hereford, and r= 0.20 for Angus aged 4–12 years. These correlations are lower than those we observed, however, correlation for Hungarian Simmental were relatively close to our result. Differences between studies may be related to alterations in breeds, sex, ages, and environmental conditions. No similar studies regarding correlations in shoulder width by age have been reported in the international literature to enable comparison.\u003c/p\u003e \u003cp\u003e*Points indicate the results of the first (BL1; SW1) and second (BL2; SW2) measurements. Relationship between data is illustrated by the linear regression line.\u003c/p\u003e \u003cp\u003eIn the case of hip width (HW), a moderate positive correlation was found between the two measurement time points with a standard error of 3.08 cm (n\u0026thinsp;=\u0026thinsp;817, r\u0026thinsp;=\u0026thinsp;0.433, r\u003csup\u003e2\u003c/sup\u003e= 0.188, SE\u0026thinsp;=\u0026thinsp;3.079; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Regarding hip width, age-related associations are available in the case of age at first calving in Hanwoo beef cows. Authors indicated very weak negative phenotypic correlations between HW and age at first calving (AFC) (r= -0.013 for \u0026lt;\u0026thinsp;24 months; r= -0.022 for \u0026gt;\u0026thinsp;24 months) suggesting minimal association between body size and reproductive timing (Shin et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Comparing these findings, it can be concluded that while HW is a useful indicator of growth and body development, it has limited predictive value for AFC, highlighting the need to consider additional traits when selecting for reproductive performance. In the case of pin width (PW), a weak positive correlation was detected between the two measurement time points with a standard error of 0.95 cm (n\u0026thinsp;=\u0026thinsp;817, r\u0026thinsp;=\u0026thinsp;0.264, r\u003csup\u003e2\u003c/sup\u003e= 0.070, SE\u0026thinsp;=\u0026thinsp;0.956; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). No similar studies regarding correlations in shoulder width by age have been reported in the international literature to enable comparison.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e*Points indicate the results of the first (HW1; PW1) and second (HW2; PW2) measurements. Relationship between data is illustrated by the linear regression line.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Results for calves kept for breeding and fattening\u003c/h2\u003e \u003cp\u003e The weight and body size data of the bulls at the time of weaning were compared according to whether the breeder kept them for fattening or breeding. The study included 928 bull calves, of which 469 were kept for breeding and 459 for fattening after weaning. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the fattening group had higher standard deviation values, even though the breeding group had higher average values, except for pin width. There was no difference between the ages of the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Based on the results, we found that, with the exception of the overgrowth index and pin width, the breeding group exceeded the results of the fattening bulls in all parameters (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The greatest difference was found in the measured weight, where the breeding group exceeded the results of the fattening group by 37.233 kg (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In terms of body size, there were slight differences between the groups, ranging from 1.168 to 3.162 cm. In the case of PW, although the difference of 0.159 cm is statistically substantiated, practical experience and the standard error of measurements suggest that it is negligible.\u003c/p\u003e \u003cp\u003eBody measurements are essential from both breeding and fattening aspects for selection, the analysis of body weight development, the estimation of breeding value, slaughter value and meat production and information on animal health (Li et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Bene et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) measured the overgrowth index of breeding-purpose cows from various cattle breeds including Hungarian Simmental, Hereford, Aberdeen Angus, Red Angus, Lincoln Red, Limousin, Charolais, Blonde d\u0026rsquo;Aquitaine and Shaver cows aged between 4 and 12 years, and found similar overgrowth indexes ranging between 101 and 104. For limousine, the index was 103.84, showing slightly lower value compared to ours which can be explained by sex difference. Studies dealing with both breeding and fattening-purpose cattle populations is not available in the international literature. Our analysis, in this regard, can be gap-filling.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparisons of body measurement parameters on bull calves with group statistics kept for breeding (B) (n\u0026thinsp;=\u0026thinsp;469) and fattening (F) (n\u0026thinsp;=\u0026thinsp;459)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody measurement parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreeding/Fattening\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCV%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge at first measurement (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e227.94\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223.28\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBody weight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e307.04\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269.81\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWithers height (WH) (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.25\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTail height (TH) (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121.57\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.41\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBack length (BL) (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.80\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.64\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eShoulder width (SW) (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.73\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.72\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHip width (HW) (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.97\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.80\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePin width (PW) (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.52\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBody mass index (kg*100/WH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e272.81\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245.60\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOvergrowth index (TH*100/WH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108.32\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108.56\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea,b\u003c/sup\u003e Different letter within a row of each body measurement parameter between \u0026ldquo;B\u0026rdquo; and \u0026ldquo;F\u0026rdquo; indicate significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Std. deviation: standard deviation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eCV% Coefficient of variation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*B: breeding; F: fattening\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDetailed statistical differences between the breeding and fattening groups regarding Levene's Test for Equality of Variances and t-test for Equality of Means are illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Levene\u0026rsquo;s test indicated that the assumption of homogeneity of variances was violated for most traits (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), except for pin width, where equal variances could be assumed (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.956). The t-test revealed statistically significant differences between the breeding groups for nearly all measured parameters (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), except for age at first measurement (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.122). The magnitude and direction of the mean differences indicate that one of the breeding groups consistently exhibited higher values across most body dimensions, particularly for body weight and body mass index, while the negative mean difference observed for the overgrowth index suggests a relative proportional variation between height traits.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean statistical differences (Levene's Test for Equality of Variances and t-test for Equality of Means) in body measurement parameters between the breeding groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBody measurement parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLevene's Test for Equality of Variances\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e \u003cp\u003et-test for Equality of Means\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificance Two-Sided \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean Difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStd. Error Difference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at first measurement (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e563.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody weight (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e903.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWithers height (WH) (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e874.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTail height (TH) (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e876.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBack length (BL) (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e905.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eShoulder width (SW) (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e841.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHip width (HW) (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e836.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePin width (PW) (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e925.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody mass index (kg*100/WH)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e886.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOvergrowth index (TH*100/WH)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e865.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*df: degree of freedom; Std. Error Difference: standard error of the difference\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Limitations of the study and avenues for future research\u003c/h2\u003e \u003cp\u003eThe study aimed to identify parameters describing the relationship between body measurements recorded at weaning and at one year of age to support early selection in practical breeding programs. By systematically collecting body size data, breeders are provided with the opportunity to perform early selection of breeding animals based on objective measurements, which may result in significant economic advantages.\u003c/p\u003e \u003cp\u003eBeyond the numerous strengths highlighted throughout the manuscript, it is also important to acknowledge several limitations inherent to the present study. These constraints should be considered when interpreting the findings and their broader applicability. One of the main challenges of the study is the lack of standardized experimental conditions. In Hungary, breeding populations are typically small, making it difficult to examine large numbers of animals (e.g., over 100 individuals) of the same age, measured at the same time, and raised under identical conditions. As the dataset was compiled from multiple farms, where animals are kept under broadly similar but not identical conditions, the timing of data collection varies across the year. In addition, animals included in the study differ in age and do not originate from the same weaning groups.\u003c/p\u003e \u003cp\u003eTo address these limitations, a large database was assembled by collecting extensive data, enabling statistically reliable comparisons. As a result, differences in age within the dataset do not show statistically significant effects among the examined animals. If a breeding population of sufficient size were available, where a large number of animals could be weaned simultaneously, repeated controlled experiments could potentially yield more robust and reliable results. However, such conditions are currently not feasible.\u003c/p\u003e \u003cp\u003eFor future analyses, this research could be continued by expanding the dataset with data based on artificial intelligence and machine learning approaches to reveal deeper relationships. It could enhance early selection strategies and enable the prediction of expected phenotypic performance of the animals.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eIn conclusion, withers height (WH) and tail height (TH) proved to be the most effective traits for early selection. A progressive increase in correlation coefficients between body weight and linear body measurements at later stages indicates that the initially heterogeneous calf population becomes more uniform by 13\u0026ndash;14 months of age. Although differences between breeding and fattening groups were modest, selected breeding bulls consistently showed superior performance, confirming that current selection practices are reflected in measurable traits. It can therefore be seen that although breeders select on the basis of development, this tendency is also reflected in the measured parameters, implicating that data-based selection is possible. Accordingly, it is recommended that the Limousin breeding programme be simplified by omitting pin width (PW) and focusing on weight and height measurements at weaning. Furthermore, the development of farm-specific decision-support tools (i.e. a platform for breeders) could enhance pre-selection efficiency. This study contributes novel insights by demonstrating the value of longitudinal body measurements for early selection in beef cattle.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eStatement of Animal Rights\u003c/h2\u003e \u003cp\u003eEthical clearance was granted by the Institutional Animal Welfare and Use Committee of Sz\u0026eacute;chenyi Istv\u0026aacute;n University Faculty of Agricultural and Food Sciences (clearance number: SZE-AKMK/MAB/012/2024).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflict of Interest Statement\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e \u003cp\u003eThe authors would like to thank the Association of Hungarian Limousin and Blonde d\u0026rsquo;Aquitaine Breeders for providing the opportunity to conduct the measurements and supply the data; the University for providing the facilities; and the Ministry of Culture and Innovation and the National Research, Development and Innovation Fund programme (2024\u0026thinsp;\u0026minus;\u0026thinsp;2.1.2-EK\u0026Ouml;P-KDP-2024_00016) for the bursary that supported the research.\u003c/p\u003e\u003ch2\u003eData Availability Statements\u003c/h2\u003e \u003cp\u003eThe data presented in this study are available on request from the corresponding author. 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DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ani12131601\u003c/span\u003e\u003cspan address=\"10.3390/ani12131601\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Beef cattle, body composition, breeding and fattening bulls, decision support system, growth rate, weaning calves","lastPublishedDoi":"10.21203/rs.3.rs-9277528/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9277528/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn beef cattle breeding, analysing body size parameters enables the evaluation of production-related correlations and growth dynamics. By assessing these traits at multiple time points, it becomes possible to identify early predictors of later development, supporting early selection decisions. In this study, the correlations between the data measured at weaning (230 days) and at 13\u0026ndash;14 months of age (weight, withers height, tail height, length of back, shoulder width, hip width and pin width) were analysed. Data of bull calves kept for breeding and fattening at weaning ages were also compared. The results indicated a weaker correlation between body measurements taken at weaning age (r\u003csub\u003eAVG\u003c/sub\u003e= 0.581) than at 13\u0026ndash;14 months of age (r\u003csub\u003eAVG\u003c/sub\u003e= 0.640, n\u0026thinsp;=\u0026thinsp;817). Pin width showed very weak correlations in all cases (r\u0026thinsp;=\u0026thinsp;0.293\u0026ndash;0.930). The strongest positive correlation between the two measurement times was found for withers height (r\u0026thinsp;=\u0026thinsp;0.618) and tail height (r\u0026thinsp;=\u0026thinsp;0.631). The results of fattening bulls were compared with those of bull calves retained for breeding. Comparative analysis revealed that breeding bulls outperformed fattening bulls across all parameters except the overgrowth index and pin width. These findings suggest that selection decisions can be effectively based on measured traits alone. Overall, although Limousin calves exhibit variable growth rates, withers height and tail height can be regarded as pre-selection criteria for breeding programs.\u003c/p\u003e","manuscriptTitle":"Possibility of early selection of Limousin young stock based on analysis of repeatedly measured body size parameters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 15:36:27","doi":"10.21203/rs.3.rs-9277528/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dfd8d335-da78-41f8-8d57-11e81698dcde","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T14:42:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 15:36:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9277528","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9277528","identity":"rs-9277528","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}