Growth Curves and Optimum Lamb Slaughter Ages for Eşme, Karya and Kıvırcık Sheep Breeds

Growth Curves and Optimum Lamb Slaughter Ages for Eşme, Karya and Kıvırcık Sheep Breeds | 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 Growth Curves and Optimum Lamb Slaughter Ages for Eşme, Karya and Kıvırcık Sheep Breeds Yavuz Akbaş, Onur Yilmaz, İbrahim Cemal, Nezih Ata, Orhan Karaca This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7676389/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 The aims of this study are (1) to estimate the growth curves of the Eşme, Karya and Kıvırcık sheep breeds from birth to the 200th day of age using the Gompertz, Bertalanffy and Logistic models; (2) to determine how the parameters vary by gender, birth type, tier, and year; (3) to establish the optimum slaughter age for these breeds based on the growth curves; and (4) to compare the growth curve parameters for each breed derived from individual body weights with those obtained from mean body weights at each age point allowing more practical representation of the mean growth curves for the breeds. Both linear (simple linear, quadratic, cubic) and non-linear growth curve models (Gompertz, Bertalanffy and logistic) were fitted and the relationship between body weight and age were investigated. Models were compared using the adjusted coefficient of determination (R2a), mean squared error (MSE), Akaike information criterion (AIC) and Bayesian information criterion (BIC). Estimating population-level growth curves using large datasets is a viable alternative for comparing different breeds. This method is beneficial as tracking individual weights over time in large herds is labor-intensive and costly. The results show that all models were suitable for predicting growth curves of Eşme, Karya, and Kıvırcık sheep breeds. The Logistic model was the best fit for Eşme and Karya breeds, while the Gompertz model was ideal for Kıvırcık sheep. Among linear models, the Cubic model was the most effective for all breeds. Growth curve parameters Eşme Karya Kıvırcık sheep genotypes optimum slaughter age Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Within the scope of the “National Project for Animal Improvement at Breeders’ Conditions”, breeding studies are conducted with various species and breeds across several regions of Türkiye. Aydın Adnan Menderes University is also actively engaged in breeding research on three distinct sheep breeds: Eşme, Karya and Kıvırcık, under breeder conditions (Karaca et al., 2012 ). One of these studies focuses on the improvement of the Kıvırcık breed, which is raised in Aydin province as well as in many Western Anatolian provinces (Karaca et al., 2018 ). Notable for its high fertility and productivity, Kıvırcık is one of the original and prominent breeds in Türkiye’s livestock sector. The Kıvırcık breed originates from the Thrace and Aegean regions is particularly prevalent in the fertile pastures of Marmara and Aegean. Kıvırcık has significant potential for meat production and exhibits a moderate level of performance in terms of milk yield (Kaymakçı et al., 2001 ; Öner et al., 2014 ; Cemal et al., 2018 ). Another project focused on improving Eşme sheep, which hold a significant position in Western Anatolian sheep breeding and are raised in Eşme district of Uşak Province, Türkiye. The Eşme breed was formed by transforming the native Dağlıç sheep breed into a thin-tailed form using mainly Kıvırcık and partly Chios rams, and then continuing the breeding studies towards this form. Eşme sheep not only excel in meat production and quality but also possess a noteworthy level of fertility. In other words, it is a combined breed (Karaca et al., 2021a ; Karaca et al., 2024 ). The third project focuses on improving the Karya sheep, which is widely raised under breeders’ conditions in Western Anatolia (Karaca et al., 2021b ). The objectives include enhancing litter size of ewes, growth characteristics of lambs up to weaning and meat quality of lambs. The Karya sheep breed had initially emerged by breeders through unsystematic crossbreeding of fat-tailed sheep breeds, including Ödemiş, Çine Çaparı, and Dağlıç, with Sakız and Kıvırcık rams. Following the initiation of a breeding program in 1996 aimed at improving the Karya sheep population in the region, along with extensive scientific research, the Karya sheep breed, which has become increasingly preferred by breeders, was officially registered as a national breed in 2013 (Yilmaz et al., 2013 ). Due to its advantages in terms of performance and adaptation compared to the Kıvırcık and Sakız breeds, the Karya has gained popularity in Western Anatolia (Karaca et al., 2009 ; Yücel 2022 ). Historically, the dominant sheep breed in Aydın region was Çine Çaparı; however, the Karya, known for its high milk production and litter size, has become widespread in the plains, while the Kıvırcık breed has mostly taken place in the mountainous parts of the region (Altın et al., 2005 ). In any species, body weight serves as a crucial key performance indicator. Changes in body weight over time are among the most frequently observed phenomena in nature and commonly utilized as indicators of biological systems to enhance production efficiency. Consequently, understanding the growth performance of animals, particularly meat producing animals, is essential for facilitating favorable changes in early muscling, fat cover and weight gain (Santos et al., 2014 ). Therefore, fluctuations in weight over time have been a frequent subject of investigation, as the transition from a young age to adulthood represents a critical biological phenomenon for all farm animals. The use of growth curves to analyze growth over time is a common practice. Growth curve parameters are utilized in both herd management and animal breeding to assess and optimize growth performance (Abegaz et al., 2010 ; Deribe et al., 2023 ). In the studies examining growth curves, the live weights, body measurements, carcass components, and carcass composition of animals over time are typically determined and modelled using various equations which can be either linear or nonlinear (Akbaş 1996 ; Goliomytis et al., 2006 ; Çelikeloğlu and Tekerli 2014 ). Growth curves, which encapsulate the growth process through numerous measurements, are a widely utilized method for understanding growth by employing a limited number of biologically interpretable parameters from growth curve models. The parameters describe specific characteristics of growth over time. Sigmoidal growth curves are well-known and applied in numerous fields including biology, agriculture, pharmacology, toxicology, and other biological and environmental processes (Brunner and Kühleitner 2020 ). Additionally, modeling the live weight growth of livestock is considered important in practice (Makgopa et al., 2024 ). Model selection can vary depending on traits being investigated, such as body weight, body dimensions, carcass components or carcass composition. It also influenced by the structure of data, including initial and final measurement times, the frequency of observation throughout life span, and fluctuations in observations over time due to physiological, environmental, and primarily genetic differences. For instance, factors that cause sudden changes in live weight - such as reaching sexual maturity at different times, climate conditions, production levels, sex, diseases, and stress – can significantly impact model selection and the shape of growth curves. In previous studies various growth curve models have been employed to analyze sheep growth. The most used models, ranked according to their effectiveness for sheep growth curves, are Brody, Gompertz, Logistic, Bertalanffy and Richards. Other models that are used less frequently include Linear, Quadratic, Cubic, Negative Exponential, Cubic Spline, Morgan–Mercer–Flodin, Meloun I, Meloun IV, and Mitscherlich, as well as Janoschek. The growth curves of various sheep breeds have been estimated, including Akkaraman (Kocabaş et al., 1997 ; Kozakli et al., 2022 )vırcık (Akbaş et al., 1999 ; Eyduran et al., 2008 ), Dağlıç (Akbaş et al., 1999 ), Awassi (Esenbuga et al., 2000 ; Topal et al., 2004 ; Ali et al., 2020 ), Morkaraman (Esenbuga et al., 2000 ; Topal et al., 2004 ), Tuj (Esenbuga et al., 2000 ), Suffolk (Lewis and Brotherstone 2002 ; Domínguez-Viveros et al., 2019 ), Texel (Lambe et al., 2006 ), Scottish Blackface (Lambe et al., 2006 ), Dorper (Domínguez-Viveros et al., 2019 ), Gulian (Hossein-Zadeh and Golshani 2016 ), Mengali (Tariq et al., 2011 ; Tariq et al., 2013 ), Moghani (Hossein-Zadeh 2017 ; Dehsahraei et al., 2023 ), Santa Ines (da Silva et al., 2012 ; Santos et al., 2014 ), Norduz (Daskiran et al., 2010 ; Kum et al., 2010 ), Hemsin (Kopuzlu et al., 2014 ), Baluchi (Behzadi et al., 2014 ), Mehraban (Hojjati and Hossein-Zadeh 2018 ), Kaji (Ali et al., 2020 ), Lori-Bakhtiari (Masari et al., 2021 ; Foroutanifar and Khaldari 2024 ), Malya (Aytekin et al., 2010 ), Romanov (Tahtali et al., 2020 ) sheep breeds. The model that best fits the data set varies across studies and breeds. For instance, in the Morkaraman breed, Topal et al.(2004) and Eyduran et al. ( 2008 ) recommended the Gompertz model as the most effective, while Esenbuga et al. ( 2000 ) and Bilgin et al. ( 2004 ) identified the Brody model as the most successful. In addition to studies advocating for the simple linear model (Kocabaş et al., 1997 ; Akbaş et al., 1999 ) in estimating growth curves of sheep, there are also studies that propose the cubic model (Aytekin et al., 2009 ), the Brody model (Aytekin et al., 2009 ), the Gompertz model (Lewis and Brotherstone 2002 ), the Richards model (Hossein-Zadeh 2015 ), and the Logistic model (Daskiran et al., 2010 ), among others. Although some characteristics of Eşme, Karya and Kıvırcık sheep have been documented in literature, no studies have been conducted on the growth curves of Eşme and Karya sheep breeds. Only limited number of studies (Akbaş et al., 1999 ; Eyduran et al., 2008 ; Ozturk et al., 2023 ) have focused on the growth curve of Kıvırcık sheep, and these studies contain a small amount of data. The aims of this study are (1) to estimate the growth curves of the Eşme, Karya and Kıvırcık sheep breeds using the Gompertz, Bertalanffy and Logistic models; (2) to determine how the parameters vary by gender, birth type, tier, and year; (3) to establish the optimum slaughter age for these breeds based on the growth curves; and (4) to compare the growth curve results derived from individual body weights estimates with those obtained from mean body weight estimates at each age point. Material and Methods Data Live body weight data for the Kıvırcık, Eşme, and Karya sheep breeds were collected between 2013 and 2023 as part of the Kıvırcık Sheep Breeding Sub-Project (Project No: TAGEM/09KIV2012-01), the Eşme Sheep Breeding Sub-Project (Project No: 64ESM2011-01), and the Karya Sheep Development Sub-Projects (Project No: 20KAR2006-01, 20KAR2011-02, and 09KAR2006-01). These projects were conducted by Aydın Adnan Menderes University under the National Animal Breeding Projects at the breeder conditions (Karaca et al., 2018 ; Karaca et al., 2021a ; Karaca et al., 2021b ). The total number of animals and observations is presented in Table 1 , detailing changes by breed, gender, birth type, tier, and year, both as numbers and percentages. Specifically, for the Eşme breed, 124,657 records from 57,867 individuals were included. In contrast, there are 405,980 live weight records from 201,632 individuals of the Karya sheep breed raised in the Aydın and Denizli provinces, while the study also includes 111,528 records from 55,956 Kıvırcık individuals registered in Aydın province. Excluding birth weight, the mean number of observations for each test day was 334 for Eşme, 1,090 for Karya, and 298 for Kıvırcık, respectively. Table 1 Number of animals and of observations in Eşme, Karya and Kıvırcık sheep breed populations and distribution of observation to subgroups (number and percentage) Breeds Eşme Karya Kıvırcık Number % Number % Number % Total number of individuals 57867 100.0 201632 100.0 55956 100.0 Total number of observations 124657 100.0 405980 100.0 111528 100.0 Gender Female 63802 51.2 204959 50.5 57706 51.7 Male 60855 48.8 201021 49.5 53822 48.3 Birth Type 1 59008 47.3 185767 45.8 74654 66.9 2 57960 46.5 199291 49.1 33983 30.5 3 7287 5.9 19086 4.7 2645 2.4 4≥ 402 0.3 1836 0.4 246 0.2 Tier Multiplier 27643 22.2 96511 23.8 16807 15.1 Base 97014 77.8 309469 76.2 94721 84.9 Year 2013 13227 10.6 29357 7.2 8772 7.9 2014 13255 10.6 37154 9.2 9851 8.8 2015 13040 10.5 25498 6.3 9786 8.8 2016 12560 10.1 30237 7.4 10548 9.5 2017 8254 6.6 40170 9.9 10246 9.2 2018 10570 8.5 41712 10.3 8811 7.9 2019 13599 10.9 41198 10.1 10196 9.1 2020 11294 9.1 42772 10.5 10453 9.4 2021 14476 11.6 42882 10.6 10811 9.7 2022 7716 6.2 36832 9.1 11428 10.2 2023 6666 5.3 38168 9.4 10626 9.5 With minor variations among breeds, 48–49% of the data pertains to males, while 51–52% pertains to females. Although the majority of the data across all breeds is associated with the first two birth types (singles and twins), there are notable differences in the distribution of these birth types among the breeds. In the Kıvırcık sheep breed, single births account for a higher percentage (66.9%), whereas the rates of single and twin births in the Eşme sheep breed are relatively similar. Conversely, in the Karya sheep breed, the occurrence of twins (49.1%) surpasses that of single births (45.8%). Each breed comprises two tiers: multiplier flocks and base flocks within the breeding program. While multiplier flocks account for approximately 20% of the population, base flocks make up 80%. The distribution of the dataset across the years for each sheep breed typically falls within the range of 8–10%. Even at the lowest of these rates, the presence of 6,666 observations (from the year 2023 for Eşme) is sufficient to estimate the growth curve for each year (Table 1 ). The birth weights of lambs were recorded using electronic hand scales within the first 24 hours after birth, and the lambs were identified with plastic ear tags. Consequently, the birth weights of all individuals are documented. Although births are primarily concentrated between November and March, they occur throughout nearly the entire year. This distribution of births results in periodic body weight inspections on the farms being conducted to be various times throughout the year. The live weights of all animals were measured during the farm visits. Although the birth weights of all individuals were available, it was not possible to track the live body weights of the same individuals at different time points because the data consisted of herds at the breeders’ level. Consequently, age-related live weight changes, or growth curves, was examined at the population level rather than individual level. When considering live weights at the population level, daily live weights were available for each breed up to the 200th day after birth. Due to the high number of observations at each age, it was assumed that the growth curve obtained at the population level would accurately reflect the general growth curve of the sheep breeds. Because of the limited availability of live weight data after day 200, only data up to 200 days were included in this study. Statistical Analysis Growth curve analyses were conducted separately for each breed using two distinct approaches. In the first approach, the individual body weights of each animal were utilized and referred as the analysis using individual body weights. Initially, each model was fit to a dataset containing body weights of all lambs in the breed, and subsequently, this analyze was conducted for subgroups based on gender, birth type, tier, and year for each breed. In the second approach, growth curve analyses were conducted on the average body weights for each age points and referred as analysis using mean body weights. In this analysis, an additional data set was generated using the daily mean live body weight from birth to 200 days of age for each breed. This approach effectively eliminated individual variations in daily live body weights, allowing for a more practical representation of the mean growth curves for the breeds. Models To describe the growth curves of Eşme, Karya, and Kıvırcık sheep, we employed three commonly used non-linear growth functions: the Bertalanffy model (von Bertalanffy 1957 ), the Logistic model (Nelder 1961 ), and the Gompertz model (Laird et al., 1965 ). The equations for the Bertalanffy, Logistic and Gompertz models are presented in Table 2 . The model parameters along with their 95% confidence limits, were estimated using the Levenberg-Marquardt algorithm within the non-linear regression procedure (NLIN) of IBM SPSS version 25. Table 2 Linear and nonlinear growth curve models used in this study Model Equation Age at point of inflection* Weight at point of inflection* Bertalanffy Y = A*(1 − B*exp(− C*𝑡))**3 (Ln 3*B)/C 8*A/27 Logistic Y = A /(1 + B*𝑒xp(− C*𝑡)) ln(B)/C A/2 Gompertz Y = A*𝑒xp(-B*𝑒xp(− C*𝑡)) ln(B)/C A/e Simple Linear Y = A + B*t - - Quadratic Y = A + B*t + C*t 2 - - Cubic Y = A + B*t + C*t 2 + D*t 3 - - * Dehsahraei et al. ( 2023 ) In the models presented in Table 2 , Y represented body weights in kilograms (kg); A denotes the asymptotic body weight as time (t) approaches infinity, interpreted as the adult weight of animal; B is the integration constant that relates to the proportion of the asymptotic mature weight gained after birth, determined by the initial values of weight and time (Hojjati and Hossein-Zadeh 2018 ); exp refers to the base of the natural logarithm; C signifies the maturation rate, which indicates the rate of weight change in relation to mature weight, reflecting how quickly the animal approaches its adult weight; and t represents the animal's age in days. The age at point of inflection, where the maximal growth rate is achieved and the growth rate transitions increasing to decreasing, as well as the weight at point of inflection, were also calculated using the equations provided in Table 2 . Both linear (simple linear, quadratic, cubic) and non-linear growth curve models (Gompertz, Bertalanffy and logistic) were fitted to the datasets in question, and the relationship between body weight and age were investigated. Goodness-of-fit criteria In the process of model comparison and selection of the optimal model, the adjusted coefficient of determination (R2a), mean squared error (MSE), Akaike information criterion (AIC) and Bayesian information criterion (BIC) were employed as goodness-of-fit metrics. A higher adjusted coefficient of determination and lower values of MSE, AIC and BIC indicate the model that provides the best fit. The criteria were calculated as follows: Adjusted coefficient of determination (R 2 a ) = 1-((n-1)/(n-p)) (1-R 2 ) where R 2 = 1-(RSS/TSS) Mean square error = RSS/(n-p) Akaike information criteria (AIC) = n*ln(RSS/n) + 2p Bayes information criterion (BIC) = n*ln(RSS/n) + p*ln(n) The residual sum of squares (RSS) represents the sum of the squares of the residuals; the total sum of squares (TSS) indicates the total variation in the data. The coefficient of determination (R²) quantifies the proportion of variance explained by the model. In this context, n denotes the number of observations, while p signifies the number of parameters. Additionally, ln refers to the natural logarithm of a number. In comparing the subgroups of the factors (gender, birth type, tier, and year) regarding growth curve parameters, 95% confidence intervals for these parameters were utilized. The daily gain and average daily gain were calculated using body weights predicted by the models as follows: Daily gain = BW t – BW t−1 , Average daily gain = (BW t – BW 0 )/t where BW t is the body weight at day t and BW 0 is the birth weight estimated; t is the age of lamb in days. The optimum slaughter age was determined when the average daily gain reached its maximum and began to decline. Additionally, body weight at slaughter age was predicted using the model employed in this study. Results and Discussion General Growth Performance The changes in the average body weights of the breeds examined in the study over time are illustrated in Fig. 1 . The body weight of the breeds exhibited a similar increasing trend during the first 50 days, beginning with significant differences in birth weight. Throughout this period, Eşme consistently demonstrated higher average body weights while Karya maintained an intermediate position between Eşme and Kıvırcık during the initial 90 days. However, between days 90 and 120, Karya exhibited higher live weights, surpassing Eşme breed. Kıvırcık breed consistently recorded lower body weights at all ages compared to the other two breeds, with this disparity becoming more pronounced between days 70 and 165. Although Eşme breed had slightly higher live weights than Karya after day 145, the body weights of both sheep breeds displayed significantly greater variability during this final period. After the 165th day, as variability increased, the average body weights of the breeds began to converge. Goodness-of-Fit Results of the models Goodness-of-Fit Results of Non-Linear Models The goodness-of-fit results for the nonlinear models used to estimate the growth curves of Eşme, Karya, and Kıvırcık, based on individual body weights and mean body weights for each age, presented in Table 3 . All nonlinear models exhibit similar goodness-of-fit results, with high R 2 values ranging from 0.848 to 0.886 when using individual body weights, and from 0.966 to 0.985 when using mean body weights. These indicate that all nonlinear models used effectively predict the growth curves of the sheep breeds studied. However, when considering MSE, AIC and BIC, the Gompertz model emerged as the best fit for Eşme and Kıvırcık, while the Logistic model was the best fit for Karya using individual body weights (Table 3 ). This aligns with the findings of Ozturk et al. ( 2023 ), who proposed the Gompertz function as reliable tool for modeling Kıvırcık lamb growth. On the other hand, the Logistic model demonstrated the best performance for the Eşme and Karya sheep breeds in terms of R 2 , MSE, AIC, and BIC when using mean body weights at each age (Table 3 ). The Gompertz and Bertalanffy models exhibited an equal fit for the Kıvırcık sheep breed and were found to be more successful than the Logistic model. A slight difference in the estimations derived from two different data sets using nonlinear models was observed in the Eşme sheep breed. Based on differences in AIC and BIC, the most successful model for the Eşme sheep breed was the Gompertz model when individual data were utilized, whereas the Logistic model was preferred when mean body weights were employed. Overall, due to the proximity of the results, the Logistic model was regarded as the best model for Eşme and Karya sheep breeds, while the Gompertz model was deemed the best for the Kıvırcık sheep breed. Table 3 Goodness-of-fit statistics for nonlinear growth curve models using individual or mean body weights for each age point Model Using individual weights Using mean weights Breeds Breeds Criteria Eşme Karya Kıvırcık Eşme Karya Kıvırcık Gompertz MSE 22.26 30.006 21.093 3.697 2.901 1.291 R 2 a 0.886 0.849 0.861 0.968 0.972 0.985 AIC 386776 1380898 340048 247 202 51 BIC 386805 1380931 340077 257 212 61 Bertalanffy MSE 22.34 30.291 21.15 4.013 3.443 1.292 R 2 a 0.886 0.848 0.860 0.966 0.967 0.985 AIC 387234 1384736 340347 263 234 51 BIC 387263 1384769 340376 272 244 61 Logistic MSE 22.3 29.56 21.222 3.145 1.863 1.479 R 2 a 0.886 0.852 0.860 0.973 0.982 0.983 AIC 387001 1374827 340725 217 119 76 BIC 387031 1374860 340754 227 129 86 MSE: Mean square error; R 2 a : Coefficient of determination; AIC: Akaike information criteria; BIC: Bayesian information criteria; Criteria for the best model in each breed are given in bold. The coefficient of determination levels (0.85–0.89) obtained for the nonlinear models fitted to individual observations are lower than those reported by Kopuzlu et al. ( 2014 ) for Hemşin sheep (≥ 0.97), Keskin et al. ( 2009 ) for Konya Merino sheep (≥ 0.96), and Karakus et al. ( 2008 ) for Norduz sheep (≥ 0.99). However, these levels are consistent with the R 2 values (> 0.97) derived from the estimations of the same models based on mean body weights. In some studies, all models yield remarkably similar results regarding the quality of fit, observed in this study. In this case, some researchers may propose multiple models rather than identifying a single best model. Conversely, other researchers may still advocate for the best model, even while acknowledging that they consider all the models they have evaluated to be successful. Among the non-linear models, Akbaş et al. ( 1999 ) reported that the Brody model was the most effective for predicting the growth of Kıvırcık and Dağlıç lambs. In addition to this finding, several studies have also identified the Brody model (Esenbuga et al., 2000 ; Bangar et al., 2018 ; Ali et al., 2020 ; Deribe et al., 2023 ) as the most successful in predicting the growth curves of sheep. However, other research has indicated that alternative models, such as the Bertalanffy (Topal et al., 2004 ; Domínguez-Viveros et al., 2019 ; Boujenane 2022 ), Gompertz (Eyduran et al., 2008 ; Yıldız et al., 2009 ; Ozturk et al., 2023 ), Logistic (Daskiran et al., 2010 ; Hossein-Zadeh 2017 ), and Richards models (Hossein-Zadeh and Golshani 2016 ) may also be effective. While Hossein-Zadeh and Golshani ( 2016 ) proposed the Richards model as the most successful for predicting the growth of Shall lambs, Tahtalı et al. (2020) found it to be the least effective and recommended the Cubic Spline model as the best for growth curve of Romanov lambs. Lupi et al. ( 2015 ) reported that Brody, Bertalanffy, Verhulst, Logistic and Gompertz models are suitable for describing the growth of the Segureña sheep. However, the Bertalanffy model is the most appropriate for explaining the biological growth of the Segureña breed from birth to maturity, while the logistic model is the most suitable for representing the commercial growth curve from birth to slaughter age (80 days). The commercial growth curve is particularly significant, even though the slaughter age is influenced by cultural factors, as it relates to the carcass size demanded by consumers. Topal et al. ( 2004 ) estimated the growth curves of Morkaraman and Awassi lambs from birth to 360 days of age using the Brody, Gompertz, Logistic and Bertalanffy models. They reported that the Bertalanffy model provided the best fit for Awassi lambs, while the Gompertz model was the most suitable for Morkaraman lambs. Goodness-of-Fit Results of Linear Models Goodness-of-fit results of linear models utilizing mean body weight for each age point are significantly superior to those obtained from analysis using individual body weights (Table 4 ). When comparing the Simple Linear, Quadratic and Cubic models in terms of fit, it was found that the Cubic model exhibited a better fit for the Eşme and Karya sheep breeds. Notably, in the estimations conducted for Kıvırcık sheep, the best linear model is Cubic when individual data are employed, whereas it is Quadratic when mean body weights are utilized. Table 4 Goodness-of-fit statistics of linear models using individual weights (individual) or mean body weights for each age (Mean) Eşme Karya Kıvırcık Model Criteria Individual Mean Individual Mean Individual Mean Simple Linear MSE 22.849 9.985 31.576 14.494 22.768 4.338 R 2 a 0.883 0.914 0.842 0.859 0.850 0.950 AIC 390043 432 1401612 502 348565 276 BIC 390062 439 1401634 508 348584 283 p 2 2 2 2 2 2 Quadratic MSE 22.617 4.138 30.954 2.944 21.350 1.406 R 2 a 0.885 0.964 0.845 0.971 0.859 0.984 AIC 388770 268 1393528 205 341395 67 BIC 388799 278 1393561 215 341424 77 p 3 3 3 3 3 3 Cubic MSE 22.157 2.926 29.511 1.386 21.196 1.414 R 2 a 0.887 0.975 0.852 0.987 0.860 0.984 AIC 386208 205 1374146 65 340589 69 BIC 386247 218 1374189 78 340627 82 p 4 4 4 4 4 4 MSE: Mean square error; R 2 : Coefficient of determination; AIC: Akaike information criteria; BIC: Bayesian information criteria; p: number of parameters in the model. Criteria for the best model in each breed are given in bold. Although Lambe et al. ( 2006 ) assert that the fastest growth phase is often assumed to be linear, as observed in young animals, they found that the R 2 values of linear models were significantly lower ( 0.98) for Texel and Scottish Blackface lambs. They reported that the Richards and Gompertz models provided better predictions of growth than the logistic, Gompertz, Richards and Exponential models. While the Richards model includes the D parameter, which offers additional insights into growth, this parameter does not enhance the model’s fit. Consequently, the Gompertz model, which has fewer parameters and demonstrates a good fit, is preferred in this context (Lambe et al., 2006 ). When three linear and three nonlinear models were compared simultaneously based on all fit criteria, the Cubic model was determined to be the best fit for Eşme and Karya, while the Gompertz model was the best fit for Kıvırcık. Akbaş et al. ( 1999 ) indicated that the Quadratic model provided the best fit for the growth of Kıvırcık lambs, whereas the Simple Linear model was the best fit for Dağlıç lambs. It is believed that environmental factors, such as varying rearing and feeding practices, along with genetic differences, significantly influence the outcomes of different models. Additionally, the timing of body weight measurements and the frequency of periodic assessments are crucial factors. The optimal approach for estimating growth curves is to begin weighing at birth and continue until adult weight is achieved. Growth Curve Parameters Non-linear growth curve parameters using individual body weights The changes in the growth curve parameters estimated by nonlinear models, both in general and according to gender, birth type, tier and years are presented in Tables 5 , 6 , and 7 for the Eşme, Karya and Kıvırcık breeds, respectively. The estimated growth curves summarize body weight data using a limited number of growth curve parameters. da Silva et al. ( 2012 ) stated that nonlinear models enhance our understanding and management of growth curve parameters that can also be interpreted biologically. Table 5 Non-linear growth curve parameters using individual body weights in the Eşme sheep breed Gompertz Bertalanffy Logistic Groups A SE B SE C SE A SE B SE C SE A SE B SE C SE Overall - 50.507 0.187 2.476 0.005 0.015 0.000 57.681 0.301 0.582 0.001 0.011 0.000 42.058 0.088 8.478 0.036 0.030 0.000 Gender Female 45.969 0.206 2.406 0.006 0.016 0.000 51.236 0.314 0.569 0.001 0.012 0.000 39.285 0.104 8.079 0.047 0.031 0.000 Male 56.174 0.337 2.554 0.007 0.014 0.000 66.420 0.585 0.598 0.001 0.010 0.000 45.183 0.146 8.908 0.055 0.029 0.000 Birth type 1 51.027 0.264 2.415 0.007 0.015 0.000 58.025 0.422 0.573 0.001 0.011 0.000 42.670 0.126 8.007 0.047 0.030 0.000 2 50.889 0.286 2.537 0.007 0.015 0.000 58.552 0.467 0.592 0.001 0.010 0.000 42.064 0.133 8.959 0.058 0.030 0.000 3 45.528 0.671 2.579 0.022 0.017 0.000 51.430 1.060 0.595 0.004 0.012 0.000 38.414 0.323 9.500 0.199 0.033 0.000 4+ 41.968 2.264 2.603 0.093 0.018 0.001 47.258 3.635 0.597 0.014 0.013 0.001 35.663 1.041 9.968 0.895 0.035 0.002 Tier Base 49.701 0.204 2.460 0.005 0.015 0.000 56.512 0.326 0.579 0.001 0.011 0.000 41.595 0.097 8.366 0.041 0.030 0.000 Multiplier 54.038 0.461 2.542 0.010 0.014 0.000 62.977 0.774 0.594 0.002 0.010 0.000 44.005 0.208 8.951 0.080 0.029 0.000 Year 2013 51.946 0.720 2.458 0.014 0.014 0.000 62.982 1.306 0.588 0.003 0.009 0.000 40.847 0.304 7.818 0.089 0.028 0.000 2014 48.217 0.501 2.444 0.015 0.015 0.000 53.142 0.731 0.572 0.002 0.011 0.000 41.830 0.276 8.605 0.116 0.030 0.000 2015 46.441 0.560 2.365 0.015 0.016 0.000 52.262 0.878 0.564 0.003 0.011 0.000 39.261 0.268 7.748 0.110 0.031 0.000 2016 67.923 1.211 2.734 0.017 0.011 0.000 93.080 2.781 0.639 0.003 0.007 0.000 48.003 0.414 9.382 0.113 0.026 0.000 2017 41.891 0.512 2.276 0.019 0.018 0.000 45.377 0.734 0.545 0.003 0.013 0.000 36.895 0.266 7.368 0.141 0.033 0.000 2018 41.639 0.285 2.308 0.018 0.022 0.000 43.360 0.365 0.543 0.003 0.017 0.000 38.867 0.183 8.012 0.144 0.038 0.000 2019 62.467 0.891 2.713 0.015 0.013 0.000 78.588 1.754 0.626 0.003 0.008 0.000 47.597 0.347 9.907 0.123 0.028 0.000 2020 56.776 0.806 2.567 0.016 0.014 0.000 69.546 1.524 0.604 0.003 0.009 0.000 44.254 0.315 8.704 0.112 0.030 0.000 2021 49.736 0.514 2.476 0.014 0.016 0.000 56.931 0.848 0.582 0.002 0.011 0.000 41.386 0.230 8.529 0.106 0.032 0.000 2022 48.369 0.644 2.446 0.018 0.016 0.000 55.403 1.069 0.578 0.003 0.011 0.000 40.237 0.283 8.238 0.133 0.032 0.000 2023 58.595 1.017 2.629 0.021 0.014 0.000 70.292 1.836 0.610 0.004 0.009 0.000 46.476 0.421 9.444 0.171 0.029 0.000 Table 6 Non-linear growth curve parameters using individual body weights in the Karya sheep breed Gompertz Bertalanffy Logistic Groups A SE B SE C SE A SE B SE C SE A SE B SE C SE Overall 46.052 0.091 2.456 0.003 0.017 0.000 51.396 0.140 0.576 0.000 0.012 0.000 39.628 0.045 8.548 0.024 0.033 0.000 Gender Female 42.801 0.106 2.393 0.004 0.017 0.000 47.341 0.159 0.566 0.001 0.013 0.000 37.148 0.054 8.063 0.028 0.033 0.000 Male 49.529 0.147 2.520 0.004 0.017 0.000 55.813 0.231 0.586 0.001 0.012 0.000 42.233 0.072 9.063 0.037 0.033 0.000 Birth type 1 46.481 0.125 2.395 0.004 0.017 0.000 51.283 0.187 0.566 0.001 0.013 0.000 40.497 0.065 8.117 0.031 0.033 0.000 2 45.809 0.135 2.506 0.004 0.017 0.000 51.579 0.213 0.584 0.001 0.012 0.000 39.062 0.066 8.931 0.036 0.033 0.000 3 44.955 0.477 2.614 0.015 0.017 0.000 52.126 0.809 0.604 0.002 0.011 0.000 37.302 0.213 9.613 0.134 0.034 0.000 4+ 47.411 1.702 2.721 0.049 0.016 0.001 56.152 2.989 0.651 0.008 0.011 0.001 38.541 0.744 10.311 0.457 0.034 0.001 Tier Base 44.670 0.098 2.431 0.003 0.017 0.000 49.660 0.150 0.572 0.001 0.012 0.000 38.591 0.050 8.338 0.026 0.033 0.000 Multiplier 49.930 0.207 2.514 0.006 0.017 0.000 56.296 0.328 0.585 0.001 0.012 0.000 42.496 0.099 9.035 0.051 0.033 0.000 Year 2013 40.626 0.252 2.297 0.009 0.018 0.000 44.578 0.374 0.550 0.002 0.013 0.000 35.606 0.131 7.381 0.067 0.033 0.000 2014 40.690 0.227 2.290 0.009 0.019 0.000 44.139 0.324 0.548 0.001 0.014 0.000 36.049 0.124 7.386 0.063 0.034 0.000 2015 44.818 0.319 2.399 0.011 0.018 0.000 49.584 0.485 0.566 0.002 0.013 0.000 38.810 0.160 8.258 0.086 0.034 0.000 2016 38.451 0.185 2.308 0.010 0.021 0.000 40.806 0.251 0.547 0.002 0.016 0.000 35.130 0.110 7.747 0.077 0.037 0.000 2017 47.875 0.346 2.510 0.009 0.016 0.000 54.760 0.559 0.587 0.002 0.011 0.000 40.051 0.165 8.768 0.076 0.033 0.000 2018 52.918 0.379 2.549 0.009 0.015 0.000 61.371 0.632 0.595 0.001 0.011 0.000 43.845 0.175 8.919 0.071 0.031 0.000 2019 57.280 0.539 2.660 0.010 0.014 0.000 71.646 1.070 0.619 0.002 0.009 0.000 44.349 0.205 9.555 0.080 0.030 0.000 2020 48.844 0.320 2.531 0.009 0.017 0.000 55.550 0.516 0.589 0.002 0.012 0.000 41.294 0.153 9.021 0.078 0.033 0.000 2021 43.646 0.239 2.440 0.010 0.019 0.000 47.500 0.346 0.570 0.002 0.014 0.000 38.556 0.128 8.715 0.082 0.035 0.000 2022 48.673 0.388 2.515 0.011 0.016 0.000 56.207 0.658 0.588 0.002 0.011 0.000 40.505 0.169 8.881 0.087 0.033 0.000 2023 47.305 0.267 2.502 0.009 0.018 0.000 52.153 0.398 0.581 0.001 0.013 0.000 41.283 0.140 9.081 0.081 0.033 0.000 Table 7 Non-linear growth curve parameters using individual body weights in the Kıvırcık sheep breed Gompertz Bertalanffy Logistic Groups A SE B SE C SE A SE B SE C SE A SE B SE C SE Overall 38.368 0.106 2.283 0.005 0.018 0.000 41.408 0.148 0.546 0.001 0.013 0.000 34.181 0.060 7.282 0.034 0.033 0.000 Gender Female 35.528 0.123 2.229 0.006 0.019 0.000 37.986 0.169 0.537 0.001 0.014 0.000 31.997 0.072 6.966 0.044 0.033 0.000 Male 41.268 0.170 2.334 0.007 0.017 0.000 44.915 0.244 0.556 0.001 0.013 0.000 36.426 0.095 7.612 0.052 0.032 0.000 Birth type 1 38.953 0.130 2.251 0.006 0.018 0.000 41.976 0.182 0.541 0.001 0.013 0.000 34.749 0.074 7.068 0.040 0.033 0.000 2 37.430 0.182 2.347 0.009 0.018 0.000 40.513 0.257 0.557 0.001 0.013 0.000 33.260 0.103 7.720 0.067 0.033 0.000 3 33.881 0.671 2.376 0.034 0.018 0.001 36.900 0.944 0.563 0.005 0.014 0.001 29.885 0.392 7.745 0.252 0.034 0.001 4 39.560 3.572 2.567 0.098 0.014 0.001 47.022 6.173 0.604 0.017 0.009 0.001 32.431 1.698 8.586 0.724 0.027 0.002 Tier Base 37.788 0.113 2.275 0.005 0.018 0.000 40.721 0.158 0.545 0.001 0.014 0.000 33.715 0.064 7.255 0.037 0.033 0.000 Multiplier 40.687 0.277 2.310 0.012 0.017 0.000 44.104 0.393 0.552 0.002 0.012 0.000 36.166 0.157 7.386 0.088 0.031 0.000 Year 2013 36.377 0.342 2.191 0.015 0.017 0.000 39.321 0.480 0.532 0.003 0.013 0.000 32.268 0.194 6.589 0.097 0.030 0.000 2014 34.926 0.314 2.165 0.017 0.021 0.000 36.908 0.415 0.524 0.003 0.016 0.000 31.909 0.191 6.598 0.111 0.036 0.000 2015 37.259 0.345 2.233 0.016 0.019 0.000 39.829 0.472 0.537 0.003 0.014 0.000 33.608 0.201 6.990 0.112 0.033 0.000 2016 38.194 0.348 2.300 0.016 0.017 0.000 41.133 0.486 0.549 0.003 0.013 0.000 34.081 0.198 7.459 0.120 0.031 0.000 2017 38.134 0.375 2.322 0.016 0.017 0.000 41.619 0.543 0.554 0.003 0.013 0.000 33.571 0.204 7.521 0.115 0.032 0.000 2018 43.045 0.444 2.344 0.017 0.017 0.000 46.739 0.632 0.556 0.003 0.013 0.000 38.036 0.246 7.845 0.130 0.032 0.000 2019 38.376 0.322 2.258 0.015 0.018 0.000 41.439 0.457 0.543 0.002 0.013 0.000 34.296 0.179 7.113 0.106 0.032 0.000 2020 43.000 0.488 2.356 0.015 0.015 0.000 48.462 0.752 0.565 0.003 0.011 0.000 36.442 0.252 7.332 0.101 0.028 0.000 2021 41.913 0.418 2.352 0.015 0.016 0.000 46.232 0.614 0.560 0.003 0.012 0.000 36.300 0.225 7.593 0.114 0.031 0.000 2022 36.444 0.258 2.311 0.016 0.020 0.000 38.546 0.344 0.547 0.003 0.015 0.000 33.379 0.159 7.743 0.121 0.036 0.000 2023 40.779 0.366 2.380 0.015 0.018 0.000 44.906 0.543 0.564 0.002 0.013 0.000 35.548 0.193 7.843 0.116 0.034 0.000 Parameter A (Mature Size) Parameter A, which represents the asymptotic limit of body weight as age approaches infinity, provides valuable information about adult body weight, irrespective of fluctuations caused by genetic and environmental factors (Lupi et al., 2015 ). This parameter, significant for understanding growth and development, is typically included as a criterion in selection processes. The parameters A, estimated using the Logistic, Gompertz and Bertalanffy models, were 42.1, 50.5 and 57.7 kg for the Eşme sheep breed, respectively (Table 5 ). The same parameter was estimated to be 39.6, 46.1 and 51.4 kg for the Karya sheep breed (Table 6 ), and 34.2, 38.4 and 41.4 kg for the Kıvırcık sheep breed (Table 7 ), respectively. The differences in parameter A among the models within the same breed are statistically significant (p < 0.05). Across all sheep breeds, the logistic model produced the lowest estimates parameter A, whereas the Bertalanffy model yielded the highest estimates. The differences in breeds within the same model regarding parameter A were found to be significant (p < 0.05). It was determined that all breeds differed from one another in terms of parameter A, and this finding was consistent across all models. Males exhibited higher values than females for parameter A (p < 0.05). This difference between the sexes were 10.2, 15.2, 5.9 kg in Eşme; 6.7, 8.5 and 5.1 kg in Karya; and 5.7, 6.9 and 4.4 kg in Kıvırcık, as determined by the Gompertz, Bertalanffy and Logistic models, respectively. The sexual differences for each breed were found to be greatest with the Bertalanffy model and least with the Logistic model. Depending on the increase in the number of lambs born per lambing ewe, a general decrease was observed in parameter A. According to the results of the Gompertz model, as the birth type increased from 1 to 4, parameter A was recorded as follows: 51, 51, 46, and 42 kg in Eşme; 47, 46, 45, and 47 kg in Karya; and 39, 37, 34, and 40 kg in Kıvırcık, respectively. The decrease was evident in the first three birth types and was statistically significant. The lower weights of twins or triplets can be attributed to the limited capacity of dams to provide adequate nourishment for the development of multiple fetuses, as well as insufficient milk production for newborn lambs, which may explain their reduced performance (Hojjati and Hossein-Zadeh 2018 ). However, the final birth type group (4+) exhibited high variation due to the inclusion of four or more lambs with a small number of observations. In the Gompertz, Bertalanffy and Logistic models, the multiplier herds exhibited significantly higher values for parameter A compared to the base herds (p < 0.05). Comparing the same models for differences in parameter A between the multiplier and base herds were 4.3, 6.5, and 2.4 kg in Eşme; 5.3, 6.6, and 3.9 kg in Karya; and 2.9, 3.4, and 2.5 kg in Kıvırcık, respectively. The superiority of the multiplier elites in each breed was highest in the Bertalanffy model and lowest in the Logistic model. It was observed that an increase in body weight was achieved in elite herds due to the selection program implemented in these herds. The estimates for parameter A exhibited fluctuating change over the years (Table 5 , 6 , and 7 ). However, when comparing the values from 2013 to 2023, significant increases were observed (p < 0.05), with approximately 6–7 kg in Eşme, 5–7 kg in Karya and 4–6 kg in Kıvırcık, depending on the models used. Therefore, the breeding program should be reviewed to identify potential reasons for the fluctuations in body weight over the years. Non-linear growth curve parameters using mean body weights The growth curve parameters estimated by Gompertz, Bertalanffy and Logistic models based on the mean body weights at each age point, are presented in Table 8 . The parameter A, estimated using the Logistic, Gompertz and Bertalanffy models, was 41.2, 43.6, and 45.2 kg for the Eşme sheep breed, respectively (Table 8 ). For the Karya sheep breed, the same parameter was estimated at 37.4, 38.6, and 39.5 kg, while for the Kıvırcık sheep breed it was estimated at 37.2, 39.6, and 41.2 kg, respectively. Table 8 Growth curve parameters estimated by Gompertz, Bertalanffy and Logistic models based on mean body weights for ages Gompertz Bertalanffy Logistic Breeds A SE B SE C SE A SE B SE C SE A SE B SE C SE Eşme 43.620 a 0.648 A 2.445 a 0.079 A 0.018 a 0.001 A 45.173 a 0.839 A 0.599 a 0.016 B 0.015 a 0.001 B 41.230 a 0.398 B 6.544 a 0.323 C 0.028 a 0.001 C Karya 38.638 b 0.371 A 2.871 b 0.107 A 0.024 b 0.001 A 39.445 b 0.475 A 0.684 b 0.022 B 0.020 b 0.001 B 37.359 b 0.221 B 8.415 b 0.407 C 0.035 b 0.001 C Kıvırcık 39.615 b 0.448 A 2.251 a 0.045 A 0.017 a 0.000 A 41.164 b 0.558 A 0.564 a 0.009 B 0.014 a 0.000 B 37.184 b 0.314 B 5.547 a 0.194 C 0.026 a 0.001 C a, b: Same parameter differences between sheep breeds containing different letters within the same model are significant (P < 0.05); A, B: Same parameter differences between models containing different letters within the same sheep breeds are significant (P < 0.05); SE standard error of parameter A, B or C mean While there was no significant difference in parameter A between Karya and Kıvırcık sheep breeds, Eşme exhibited a higher parameter A (p < 0.05) compared to the others (Table 8 ). When comparing models within the same breed, the Logistic model provided lower estimates (p < 0.05) than the other two models across all breeds (Table 8 ). Additionally, no significant difference was observed between the Gompertz and Bertalanffy models. The parameter A estimated from the mean body weights in Eşme and Karya (Table 8 ) was found to be lower than the values estimated from the individual body weights (Tables 5 and 6 ). The differences observed were more pronounced in the Gompertz and Bertalanffy models compared to the Logistic model. However, in Kıvırcık, the results were either the opposite (for the Logistic, Gompertz models) or comparable (for the Bertalanffy model) (Tables 7 and 8 ). Adult weight is influenced by various factors, including species, breed, selection method, management system, and environmental conditions (Malhado et al., 2009 ). The retention of body weight records until adulthood significantly impacts the accuracy of the estimates obtained. It is reported that the body weight of adult females of the Eşme sheep breed is approximately 55–60 kg, while that of males is around 65–70 kg (Anonymous 2025 ). Since the body weights recorded during the first 200 days were utilized in this study, the estimates for adult weight were lower than the expected values for the Eşme breed. Akbaş et al. ( 1999 ) estimated the adult age weight of Kıvırcık sheep breed to be significantly higher (88, 76, and 98 kg, respectively) than the results obtained in this study (38, 34, and 41 kg) using the Gompertz, logistic and Bertalanffy models. This discrepancy arises because this study utilized data from the first 200 days, whereas Akbaş et al. ( 1999 ) based their estimates on live weights from birth to the 420th day. Similarly, Eyduran et al. ( 2008 ) estimated the adult weight of Kıvırcık sheep to be higher (61 and 52 kg) than the findings of this study using the Gompertz, and Logistic models based on the first 180 days data. In contrast, Özturk et al. (2023) reported adult weights for Kıvırcık sheep that align more closely with the levels determined in this study (39, 32, and 44 kg, respectively) using the Gompertz, logistic, Bertalanffy models. These results underscore the significance of the duration of weight data collection in accurately estimating adult weight. Aytekin and Zülkadir ( 2013 ) estimated the parameter A in Malya sheep by analyzing monthly body weights from weaning to two years of age, yielding values of 71.17 kg and 70.15 kg using the Gompertz and Logistic models, respectively. Since the analysis was conducted up to two years of age, the estimated parameter A is significantly higher than the values reported in other studies. In contrast, parameter A for Mehraban sheep was estimated at 54.05 kg and 51.30 kg using the Gompertz and Logistic models, respectively, while the Brody model, which provided the best fit, yielded a value of 66.90 kg. This elevated adult weight can be attributed to the fact that the Mehraban sheep breed is primarily a meat type breed. Since a growth curve study on Eşme and Karya sheep breeds has not been conducted previously, no research exists to directly compare the results of the growth curve parameter. Parameter B The parameter B estimated using nonlinear models (Logistic, Gompertz and Bertalanffy) based on individual body weights was determined to be 8.474, 2.476 and 0.582 in Eşme sheep breed, respectively (Table 5 ). In the Karya sheep breed, the same parameter was found to be 8.548, 2.456, and 0.576 (Table 6 ), while in the Kıvırcık sheep breed, it was 7.282, 2.282, and 0.546 (Table 7 ), respectively. The differences in parameter B among the models were significant (p < 0.05), and the trends in parameter B across the models were similar for all sheep breeds. Using the mean body weights at each age derived from the Logistic, Gompertz and Bertalanffy models, parameter B was estimated to be 6.5, 2.5, and 0.6 in Eşme sheep breed, respectively (Table 8 ). In the Karya sheep breed the same parameter was estimated as 8.4, 2.9, and 0.7, while for the Kıvırcık sheep breed, the estimates were 5.6, 2.3, and 0.6, respectively. Karya sheep breed exhibited significantly higher values (p < 0.05) for parameter B across all nonlinear models based on mean body weights compared to the other sheep breeds, whereas no significant difference was observed between the Eşme and Kıvırcık sheep breeds (Table 8 ). The differences among the models within the same breed were also found to be quite significant (Table 8 ). Comparing the results from individual and mean body weights in relation to parameter B, the difference was found to be insignificant in Eşme and Kıvırcık sheep breed, while significant for Karya breed according to the Gompertz and Bertalanffy models. Conversely, the Logistic model indicated significant differences for Eşme and Kıvırcık sheep breeds, but an insignificant difference for Karya sheep breed. Differences in parameter B among sheep breeds were found to be significant (P < 0.05) in the Bertalanffy model. In contrast, in the Logistic and Gompertz models indicated that Eşme and Karya sheep breeds exhibited similar values for parameter B, while Kıvırcık sheep breed demonstrated statistically lower (p < 0.05) B values compared to both Eşme and Karya sheep breeds (Table 5 , 6 , and 7 ). Males exhibited significantly higher parameter B values (p < 0.05) than females across all sheep breeds (Tables 5 , 6 , and 7 ). Generally, parameter B was greater in multiplier flocks compared to the values of base flocks. However, this difference was found to be insignificant (p > 0.05) in Kıvırcık sheep breed (Table 7 ), while it was significant (p < 0.05) in the Eşme and Karya sheep breeds (Tables 5 and 6 ). Parameter B increased with the number of lambs born per lambing ewe. However, the rate of this increase tended to diminish as the number of lambs increased. In each breed and model, the level of the parameter B for single lambs was found to be lower than the other groups. The differences among the birth type subgroups varied according to the models and breeds. While there was no significant difference in parameter B among the birth type subgroups, except for single in the Eşme and Kıvırcık sheep breeds according to the Gompertz and Bertalanffy models. Significant differences (p < 0.05) were observed among the first three birth type groups in the Karya sheep breed across all models as in the Eşme sheep breed using the Logistic model. Although the estimates of the B parameter fluctuate over the years, a comparison between the first year (2013) and the last year (2023) revealed that the B parameter increased by 0.19 with the Gompertz model, 0.03 with the Bertalanffy model, and 1.53 with the Logistic model across all sheep breeds. The significant changes (p < 0.05) observed between the years in all models and breeds indicate that the B parameter increased as a result of the breeding programs implemented in the populations studied. Using the Logistic, Gompertz and Bertalanffy models, Akbaş et al. ( 1999 ) estimated the parameter B for Kıvırcık to be 6.25, 2.35, and 0.59, respectively. These values are comparable to the results obtained in this study, which are 7.28, 2.28, and 0.55 (Table 7 ). In contrast, Özturk et al. (2023) reported slightly different values for the same parameter: 4.36, 1.97, and 0.51. The value estimated by Eyduran et al. ( 2008 ) for parameter B using the Logistic model was 7.38, which is very close to the value of 7.28 found in this study for Kıvırcık sheep breed. However, the value of 0.014 obtained from the Gompertz model was significantly different from the value of 2.28 reported in this study. Dominguez-Viveros et al. (2019), in their research on seven different breeds, estimated the parameter B to range from 6.05 to 8.48 using the Logistic model, from 2.14 to 2.54 with the Gompertz model, and from 0.524 to 0.597 with the Bertalanffy model. In addition to researchers who argue that the integration constant B lacks a biological interpretation (Fitzhugh 1976 ; Behzadi et al., 2014 ; Kozakli et al., 2022 ) or is related to initial weight (Santos et al., 2014 ; Bangar et al., 2018 ), some researchers (Gbangboche et al., 2008 ; Hojjati and Hossein-Zadeh 2018 ) assert that B represent the proportion of asymptotic mature weight to be gained after birth, determined by the initial weight and time. Furthermore, the results obtained indicate that parameter B from different models does not reflect the same phenomenon. Parameter C (Maturation Rate) The parameter C can be interpreted as the rate of maturation in animals, indicating the speed at which they reach their asymptotic weight. Consequently, a higher value of parameter C reflects a greater degree of precocity in the animal (Santos et al., 2014 ). However, animals exhibiting increased precocity do not necessarily have a higher likelihood of attaining greater weights at maturity compared to those that grow more slowly. The parameter C estimates from non-linear models (Logistic, Gompertz and Bertalanffy) using individual body weights, was found to be 0.030, 0.015, and 0.011 in Eşme sheep breed, respectively (Table 5 ). In Karya sheep breed, the same parameter was recorded as 0.033, 0.017, and 0.012 (Table 6 ), while in Kıvırcık sheep breed, it was 0.033, 0.018, and 0.013 (Table 7 ), respectively. The differences in parameter C among the models are significant across all breeds (p < 0.05). The Logistic model exhibited the highest parameter C in all sheep breeds, whereas the Bertalanffy model displayed the lowest C parameter. When breeds were compared in parameter C, significant differences (p < 0.05) were observed among all breeds in the Gompertz and Bertalanffy models. However, in the logistic model, Karya and Kıvırcık sheep breeds exhibited similar maturation rates (0.033), while Eşme demonstrated a statistically lower maturation rate (0.030) compared to the other breeds (p < 0.05). Based on mean body weights at each age, the parameter C estimated by the Logistic, Gompertz and Bertalanffy models was found to be 0.028, 0.018, and 0.015 for the Eşme sheep breed; 0.035, 0.024, and 0.020 for the Karya sheep breed; and 0.026, 0.017, and 0.014 for the Kıvırcık sheep breed, respectively (Table 8 ). In this analysis, the Karya sheep breed exhibited significantly higher values (p < 0.05) in maturation speed compared to the other two breeds. No significant difference was observed between Eşme and Kıvırcık sheep breeds regarding parameter C. All models within the same breed demonstrated significant differences (p < 0.05) in the parameter C. The lowest parameter C was obtained using the Bertalanffy model, while the Logistic model yielded the highest value. When comparing the results from individual and mean body weights in relation to parameter C, the differences were found to be insignificant in Kıvırcık sheep breed, while significant differences were observed in Eşme and Karya sheep breeds using the Gompertz and Bertalanffy models. In contrast, in the Logistic model revealed significant differences in Karya and Kıvırcık sheep breeds, but an insignificant difference in Eşme sheep breed. The difference in the parameter C between males and females was found to be significant (p 0.05) in the Karya sheep breed (Table 6 ). Females exhibited higher C values than males in Eşme and Kıvırcık sheep breed, indicating that females reach adult weight earlier than males. Various studies conducted on different sheep breeds, including Mehraban, Akkaraman, Segureña, Hemsin sheep breeds that the parameter C is higher in females (Kopuzlu et al., 2014 ; Lupi et al., 2015 ; Hojjati and Hossein-Zadeh 2018 ; Kozakli et al., 2022 ). In this study, the estimated levels of parameter C were found to be lower than those reported in some studies (Bilgin and Esenbuga 2003 ; Kopuzlu et al., 2014 ) and comparable to others (Domínguez-Viveros et al., 2019 ). Conversely, these levels were determined to be higher than those in several studies (Gbangboche et al., 2008 ; Aytekin and Zülkadir 2013 ; Lupi et al., 2015 ). The observed differences may be attributed to genetic factors both between and within breeds, the time unit used (month instead of day as in the present study), and the various growth functions employed (Goliomytis et al., 2006 ). The difference between multiplier and base flock in parameter C was generally found to be significant (p 0.05) in Karya sheep breed (Table 6 ). Base flocks exhibited higher values of parameter C in Eşme and Kıvırcık sheep breed, although the difference was not significant when using the Gompertz model in Eşme sheep breed. The variation in parameter C, in relation to the increase in the number of lambs born per ewe, differed across models and breeds. Due to the very low standard errors of parameter C, even small differences were found to be statistically significant. The change in the parameter C, as estimated by the Gompertz and Logistic models in relation to the increase in the number of lambs at birth, was significant in the Eşme and Kıvırcık sheep breeds but insignificant in Karya sheep breed. Conversely, the change determined by the Bertalanffy model was significant in Karya and Kıvırcık sheep breeds, but insignificant in Eşme sheep breed. In Eşme sheep breed, the significant difference was observed between the first two and the last two subgroups, while in Karya sheep breed, the first group differed from the others, and in Kıvırcık sheep breed, the last group was distinct from the others. In instances where the differences between groups were significant, the level of parameter C tended to increase with the number of offspring in Eşme sheep breed, whereas it tended to decrease in the other two breeds. Single-born lambs generally exhibited lower values for the parameter C compared to other levels; they reached mature weight later but ultimately attained a higher mature weight (Ozturk et al., 2023 ). The estimates for parameter C exhibited significant fluctuations over the years. When comparing the parameter C values from the first and the last years examined in the study, it is evident that the overall change is generally insignificant. The only exception is observed in the Logistic model for Kıvırcık sheep breed, which demonstrated an increase from 0.030 in 2013 to 0.034 in 2023 (p < 0.05). However, the fluctuations over the years indicate that there is no stable and significant change in the maturation rate as a result of the breeding program implemented in the populations. In farm animals, growth rate and adult live weight should be managed by taking into account factors such as difficult births, the need for increased space in housing and transportation, increased nutrient requirements and genetic potential (Owens et al., 1993 ; Nasholm and Danell 1996 ; Aytekin et al., 2009 ). The actual and estimated body weights for each age, based on the models using individual data for the Eşme, Karya and Kıvırcık sheep breeds, are presented in Fig. 2 , 3 , and 4 , respectively. Correlations between Growth Curve Parameters In nonlinear growth curve models, a positive correlation was observed between parameters A and B, while a strong negative correlation (less than − 0.757) was identified between parameters A and C (Table 9 ). The negative correlation between parameters A and C suggests that early-maturing animals tend to achieve smaller mature weights. Indeed, several studies (Sarmento et al., 2006 ; Malhado et al., 2009 ) have estimated this negative correlation between the parameters A and C, noting that more precocious animals are less likely to attain higher weights in adulthood. Lambe et al. ( 2006 ) reported that altering the growth curve pattern through selective breeding is feasible, as it can enhance early growth while limiting mature size. Table 9 Correlations between nonlinear growth curve model parameters Gompertz Bertalanffy Logistic Breeds Parameter A B C A B C A B C Eşme A 1 0.504 -0.949 1,000 ,688 -,978 1 0.195 -0.81 B 0.504 1 -0.263 ,688 1,000 -,560 0.195 1 0.343 C -0.949 -0.263 1 -,978 -,560 1,000 -0.81 0.343 1 Karya A 1 0.343 -0.939 1 0.534 -0.971 1 0.092 -0.798 B 0.343 1 -0.082 0.534 1 -0.378 0.092 1 0.426 C -0.939 -0.082 1 -0.971 -0.378 1 -0.798 0.426 1 Kıvırcık A 1 0.297 -0.916 1 0.44 -0.955 1 0.114 -0.757 B 0.297 1 -0.005 0.44 1 -0.25 0.114 1 0.433 C -0.916 -0.005 1 -0.955 -0.25 1 -0.757 0.433 1 Age and Weight at Point of Inflection The rate of increase in body weight rises until a certain age and then begins to decline. The point at which this rate changes is referred to as the inflection point. The average ages at the inflection points for the Eşme, Karya and Kıvırcık sheep breeds was 61, 55, and 48 days, respectively (Table 10 ). At these ages, the average live weights of the same breeds were found to be 19, 17, and 14 kg, respectively. The logistic model yielded the highest average inflection point age at 66 days, while Bertalanffy model produced the lowest average at 47 days. The Gompertz model had values that fell between the other two models, with an average of 54 days. Table 10 Estimated age and weight at point of inflection from Gompertz, Logistic and Bertalanffy models using individual body weights Breeds Gompertz Bertalanffy Logistic Mean Mean T poi BW poi T poi BW poi T poi BW poi T poi BW poi Eşme General 60 19 51 17 71 21 61 19 Sex Female 55 17 45 15 67 20 56 17 Male 67 21 58 20 75 23 67 21 Birth type BT 1 59 19 49 17 69 21 59 19 BT 2 62 19 57 17 73 21 64 19 BT 3 56 17 48 15 68 19 57 17 BT 4+ 53 15 45 14 66 18 55 16 Tier Base 60 18 50 17 71 21 60 19 Multiplier 67 20 58 19 76 22 67 20 Karya General 53 17 46 15 65 20 55 17 Sex Female 51 16 41 14 63 19 52 16 Male 54 18 47 17 67 21 56 19 Birth type BT 1 51 17 41 15 63 20 52 17 BT 2 54 17 47 15 66 20 56 17 BT 3 57 17 54 15 67 19 59 17 BT 4+ 63 17 61 17 69 19 64 18 Tier Base 52 16 45 15 64 19 54 17 Multiplier 54 18 47 17 67 21 56 19 Kıvırcık General 46 14 38 12 60 17 48 14 Sex Female 42 13 34 11 59 16 45 13 Male 50 15 39 13 63 18 51 15 Birth type BT 1 45 14 37 12 59 17 47 14 BT 2 47 14 39 12 62 17 49 14 BT 3 48 12 37 11 60 15 48 13 BT 4+ 67 15 66 14 80 16 71 15 Tier Base 46 14 35 12 60 17 47 14 Multiplier 49 15 42 13 65 18 52 15 Overall Mean 54 16 47 15 66 19 56 17 BT: Birth type; Tpoi: age at inflection (day); BWpoi body weight (kg) at point of inflection representing the end of the growth acceleration phase and the beginning of the deceleration phase. The estimated ages of female at the inflection point were lower than those of males for all models and breeds. It was determined that females and base herds entered the growth acceleration phase and transitioned through the deceleration phase earlier than males and individuals in the multiplier herd. Slaughter Age and Weight Considering the best-fit model, the logistic model for Eşme and Karya sheep breeds and the Gompertz model for Kıvırcık sheep breed, it was determined that the slaughter age and weight were 67 days and 21.50 kg for Eşme sheep breed; 62 days and 20.35 kg for Karya sheep breed; and 78 days and 21.79 kg for Kıvırcık sheep breed, based on mean body weight (Table 11 ). However, slaughter ages determined using individual body weights from the same models were found to be longer in Eşme (84 days) and Karya sheep (76 days) breeds, but shorter for Kıvırcık sheep breed (73 days). Consequently, live weights also varied accordingly (Table 11 ). Table 11 Estimated slaughter age (day) and weight, daily gain (kg/day) and average daily gain (kg/day) at slaughter age estimated from the non-linear models using individual weights or mean daily weights Eşme Karya Kıvırcık Model Criteria Individual Mean Individual Mean Individual Mean Gompertz Daily gain (kg/day) 0.246 0.250 0.255 0.290 0.230 0.220 Average gain (kg/day) 0.248 0.258 0.257 0.292 0.231 0.226 Slaughter age (day) 97 79 85 70 73 78 Slaughter weight (kg) 28.34 24.18 25.81 22.63 20.77 21.79 Bertalanffy Daily gain (kg/day) 0.248 0.230 0.256 0.260 0.228 0.200 Average gain (kg/day) 0.252 0.240 0.260 0.261 0.231 0.207 Slaughter age (day) 105 100 96 88 89 98 Slaughter weight (kg) 31.41 29.49 28.13 26.94 23.53 25.93 Logistic Daily gain (kg/day) 0.261 0.270 0.254 0.300 0.225 0.230 Average gain (kg/day) 0.262 0.277 0.256 0.308 0.226 0.240 Slaughter age (day) 84 67 76 62 63 65 Slaughter weight (kg) 25.00 21.50 23.36 20.35 17.89 19.01 Daily gain = BWt – BWt-1; Average daily gain = (BWt – BW0)/t; t is age in days. Karaca et al.(2021a) reported that Eşme lambs were weaned and marketed at an average age of 92.53 days, which is approximately 3 months. Their average live weight was recorded 27.70 kg. For Karya lambs, the reported slaughter age ranged from approximately 90–120 days, with slaughter weights varying between 26.67 and 30.97 kg (Karaca et al., 2021b ). Similarly, for Kıvırcık lambs, the slaughter age was also reported to be between 90 and120 days, weights ranging from 27.7 to 28.09 kg (Karaca et al., 2018 ). It was determined that the slaughter age and weight values estimated using the Bertalanffy model were more closely aligned with the values reported in the literature, whereas the values obtained from other models predicted lower slaughter ages and weights compared to field applications. Furthermore, it is believed that the longer duration animals are kept in field applications as opposed to the slaughter ages determined by the models in this study, is due to the practice of waiting until a certain slaughter age (approximately 3 to 4 months) without conducting an economic analysis of body weight changes. It is known that Kıvırcık sheep breed raised in the Marmara, North Aegean and western parts of Central Anatolia are typically sent to slaughter as suckling lambs at a wide range of approximately 3.5 to 5 months of age (Alarslan and Aygün 2019 ). The differences in slaughter live weight can be attributed to variations in care and feeding practices within the farms, as well as differences in slaughter age. Linear Model Parameters The relationship between age and body weight was also examined using linear models that incorporated individual body weights as well as mean body weights throughout the lifespan. The results are presented in Tables 12 and 13 , respectively. Table 12 Analysis of age-related changes in individual body weights using linear models. Simple Linear (Y=ß 0 + ß 1 *X) Quadratic (Y= ß 0 + ß 1 *X+ ß 2 *X 2 ) Cubic (Y= ß 0 + ß 1 *X+ ß 2 *X 2 + ß 3 *X 3 ) Breeds ß 0 SE ß 1 SE ß 0 SE ß 1 SE ß 2 SE ß 0 SE ß 1 SE ß 2 SE ß 3 SE Eşme 4.337 0.018 0.245 0.000252 4.178 0.019 0.276 0.001 -0.000263 0.000007 4.283 0.019 0.163 0.002 0.002 0.00004 -0.000008 0.00000016 Karya 4.188 0.012 0.244 0.000166 3.885 0.012 0.296 0.001 -0.000425 0.000005 4.151 0.012 0.119 0.001 0.003 0.000023 -0.000014 0.0000001 Kıvırcık 4.286 0.019 0.204 0.000257 3.813 0.019 0.271 0.001 -0.001 0.000006 3.901 0.019 0.218 0.002 0.000396 0.000032 -0.000004 0.0000001 Y = Body weight; X = Age (day) Table 13 Results from linear models using mean body weights (Y) and age in day (X) relations Simple Linear (Y=ß 0 + ß 1 *X) Quadratic (Y= ß 0 + ß 1 *X+ ß 2 *X 2 ) Cubic (Y= ß 0 + ß 1 *X+ ß 2 *X 2 + ß 3 *X 3 ) Breeds ß 0 SE ß 1 SE ß 0 SE ß 1 SE ß 2 SE ß 0 SE ß 1 SE ß 2 SE ß 3 SE Eşme 7.899 0.512 0.189 0.004 0.209NS 0.578 0.384 0.012 -0.0009 0.000056 5.513 0.775 0.144 0.029 0.00177 0.000309 -0.000008 0.00000096 Karya 8.546 0.616 0.173 0.005 -2.249 0.487 0.446 0.010 -0.00128 0.000047 3.740 0.533 0.176 0.020 0.001749 0.000212 -0.000010 0.00000066 Kıvırcık 7.224 0.337 0.167 0.003 1.783 0.337 0.304 0.007 -0.00064 0.000033 1.881 0.539 0.300 0.020 -0.000594 0.000215 -0.00000016NS 0.00000067 Mean 7.890 0.176 -0.086 0.378 -0.001 3.71 0.207 0.001 0.000 Y = Body weight; X = Age (day); NS is not significant The parameter ß0, estimated using linear models represents the initial value, specifically, the birth weight. Birth weight is a strong indicator of animals that are likely to achieve better weights in adulthood. The actual mean birth weights for Eşme, Karya, and Kıvırcık sheep breeds are 4.27, 4.13, and 3.94 kg, respectively. The Cubic model utilizing individual data provided the most accurate estimate of birth weight among linear models (Table 12 ). The estimated birth weights were 4.34, 4.18, and 4.29 kg, derived from the simple linear model using individual body weights (Table 12 ). The largest discrepancy, at 9%, between the actual birth weights and those estimated by the simple linear model occurred in the Kıvırcık sheep breed. However, when mean body weights were utilized, the birth weights estimated by the simple linear model significantly deviated from the actual values, being nearly double the estimates (Table 13 ). In the estimations conducted using the Quadratic model with individual data, birth weights were estimated to be 2% to 6% lower (Table 12 ). However, when the same model was applied to average body weights, the degree of deviation increased significantly ranging from 55% to 154%, with estimates approaching zero (Table 13 ). In both data sets, the cubic model exhibited the least deviation in birth weight compared to the linear models. When individual data was utilized, the deviation percentage from actual birth weights was approximately 1%. However, this deviation increased when average weights were employed. The level of deviation was positive in Eşme sheep breed (29%), it was negative in Karya sheep breed (-9%) and Kıvırcık sheep breed (-52%). The confidence interval for the estimated birth weights from Cubic model based on individual data encompasses the actual birth weights of the breeds in question. In response to one-day increase in age, body weights increased by an average of 176, 378 and 207 grams in the Simple linear, Quadratic, and Cubic models, respectively using mean body weight data (Table 12 ). While no difference was observed between breeds regarding the parameter B in the Simple linear model, all breeds were found to differ from one another in the Quadratic model. In the Cubic model, a significant difference was identified between the Kıvırcık and the other sheep breeds. The daily live weight gains estimated for the Kıvırcık, Karya and Eşme sheep breeds from the Cubic model using mean body weights were 300, 176, and 144 g, respectively. These values determined for Kıvırcık and Karya sheep breeds align with those reported by Altın et al. ( 2005 ), which were 250 g and 182 g, respectively. Consequently, Kıvırcık sheep breed is positioned to compete effectively with many breeds in daily live weight gain. An examination of the live weight change graph of the breeds (Fig. 2 ) reveals that the Kıvırcık sheep breed continues to increase, particularly after 120 days, demonstrating a growth pattern distinct from the other two sheep breeds. A significant portion of the time-dependent growth change in sheep can be explained by a simple linear model. Indeed, Kocabaş et al. ( 1997 ) demonstrated that body weight growth in sheep follows a straightforward linear pattern during the initial phase of life. A similar finding was reported in another study (Kozakli et al., 2022 ) that examined the first four months of growth in Akkaraman lambs. Additionally, Akbaş et al. ( 1999 ) indicated that the simple linear model was quite adequate for explaining the growth of Kıvırcık and Dağlıç sheep breeds up to 420th day of life. However, the direction of growth can change with age due to environmental and genetic factors. Understanding and managing the course of this change is crucial, which is many researchers have focused on growth curves. It is important to note that the timing of body weight measurements significantly influences the appropriateness of the model used. The fastest phase of growth, typically observed in young animals, is often assumed to be linear (Lambe et al., 2006 ; Kozakli et al., 2022 ), as it was the case in this study during the first 120 days. However, when considering the entire 200-day duration, the Quadratic and the Cubic models become more prominent due to the curvature observed after 120 days and the transition of the growth curve into a stationary phase. To accurately estimate the growth curves of sheep breeds, the dataset must contain a sufficient number of observations. Kozaklı et al. (2022) discussed the results of their study, which was conducted with data from 109 individuals, and stated that further research is necessary to generalize the findings to the Akkaraman breed. In studies estimating growth curves, the frequency of measurement and the duration of the measurement period are crucial, in addition to the number of individuals included in the analysis. In this study, the growth curves of the three breeds were estimated and compared using a substantial amount of data on the first 200-day live weights, as presented in Table 1 . Conclusions In this study growth curve was estimated at the population level, considering daily live weights of 124 657, 405 980, and 111 528 lambs from birth to the 200th day of age in the Eşme, Karya and Kıvırcık sheep breeds raised under breeder’ conditions. Estimating growth curves at the population level, rather than on an individual basis, using large datasets can be a viable alternative for determining and comparing the growth curves of different breeds. This approach is particularly beneficial because obtaining weights of the same individual over time is quite challenging in large breeder herds and requires significant labor, time, and financial resources. Additionally, animal movements and deaths due to slaughter or the sale of breeding stocks to other herds can negatively impact this process over time. Consequently, most studies on growth curves in sheep have been conducted in small research herds. In the current study, the fitting performances of nonlinear and linear growth curve models were comparable in terms of goodness-of-fit criteria. These results indicate that the application of all models was appropriate for predicting the growth curves of the Eşme, Karya, and Kıvırcık sheep breeds. However, when the models were ranked according to the fitting criteria, it was determined that the most suitable model for Eşme and Karya sheep breeds was the Logistic model, while the Gompertz model was found to be the best fit for the Kıvırcık sheep breed. Among the linear models, the Cubic model emerged as the most effective across all breeds. When sheep breeds were compared based on adult weight estimated from the models that provided the best fit, Eşme exhibited a potential weight of 42.1 kg, Karya 39.6 kg, and Kıvırcık 38.4 kg. While Karya was the fastest breed to reach this adult weight, the Kıvırcık breed demonstrated the slowest maturation. When selecting a model for growth curve, emphasis should be placed on the structure of the data, the ease of estimation, and biological interpretability of the model parameters. In the nonlinear models considered in this study, parameter A estimates adult weight, while parameter C provides information about the maturation rate. Since the correlation between parameters A and C is negative, fast-maturing animals are less likely to achieve high adult weights. This relationship can be a key factor if the trajectory growth is to be altered through breeding. Another factor influencing model selection is the fluctuations in live weight associated with age. These fluctuations can arise from the physiological differences among individuals, as well as variations in their environments. For instance, factors that induce sudden changes in live weight — such as the timing of sexual maturity, climate conditions, production levels, diseases, and stress —can significantly impact the shape of growth curves and, consequently, model selection. In addition to environmental influences that contribute to variation in lamb growth, differences in the genetic backgrounds of breeds can also result in significant discrepancies. Understanding the age-related growth patterns of economically significant livestock can provide valuable opportunities for producers. This knowledge aids in determining the most cost-effective slaughter age, evaluating feeding practices used, identifying any growth deficiencies, and comparing different breeds in terms of growth performance. Additionally, it allows for the introduction of specific curve parameters as selection criteria in breeding programs, which can enhance commercial traits or modify the growth curve’s shape. The differences in growth curve patterns among breeds reflect their varying genetic potential. In addition to market expectations, it is essential to utilize growth curves to identify the most economically viable slaughter age. Although the slaughter ages estimated by the models were lower than those observed in field applications for these breeds, the economic slaughter age for Kıvırcık was found to be later than that of the other two breeds. Instead of averaging body weights for each age group, estimating the growth curve at the population level based on individual body weights across various ages allows for a more accurate estimation of parameters, as it takes individual variability into account. This approach is both important and practical for providing the general growth curve of the material being studied. Furthermore, if body weights are measured at specific age intervals throughout the lifespan of individuals in the population, it becomes possible to estimate individual growth curves. This information can then be utilized to modify growth trajectories in a targeted manner by using the estimated growth curve parameters as criteria in breeding studies. Declarations Acknowledgements We would like to express our gratitude to the General Directorate of Agricultural Research and Policies for providing financial support. We would also like to extend our appreciation to the breeders who participated in the Kıvırcık Sheep Breeding Project (Project No: TAGEM/09KIV2012-01), the Eşme Sheep Breeding Project (Project No: 64ESM2011-01), and the Karya Sheep Development Projects (Project Nos: 20KAR2006-01, 20KAR2011-02, and 09KAR2006-01). Author contributions YA and OY: Conceptualization, data preparation and writing-original draft. OY, İC, NA and OK: Funding acquisition, Project administration, data collection. YA: Methodology and statistical analysis. YA, OY, İC, NA and OK: Writing-review and editing. Data availability: The data that support the findings of this study are available from the corresponding author upon reasonable request. Ethical approval: Not applicable. Conflict of interest: The authors declare no competing interests. References Abegaz S, Van Wyk JB, Olivier JJ (2010) Estimation of genetic and phenotypic parameters of growth curve and their relationship with early growth and productivity in Horro sheep. Arch Tierzucht 53:85-94. Akbaş Y (1996) Büyüme eğrisi parametreleri ve ıslah kriteri olarak kullanımı olanakları. Ege Üniv. Ziraat Fakültesi Dergisi 33:241-248. Akbaş Y, Taşkın T, Demirören E (1999) Farklı modellerin Kıvırcık ve Dağlıç erkek kuzularının büyüme eğrilerine uyumunun karşılaştırılması. Turkish Journal of Veterinary and Animal Science 23:537-544. Alarslan E, Aygün T (2019) Determination of growth and some morphological traits of Kıvırcık lambs in Yalova. Journal of Animal Production 60:39-50. Ali A, Javed K, Zahoor Anjum KM (2020) Determination of the best non-linear function to describe the growth of Kajli sheep. S Afr J Anim Sci 50:452-459. Altın T, Karaca O, Cemal İ, Yılmaz M, Yılmaz O (2005). Kıvırcık ve Karya kuzularda besi ve karkas özellikleri. Hayvansal Üretim 46:19-29. Anonymous (2025) https://www.koyuncuk.com/dokumanlar-2/koyun-irklari/yerli-koyun-irklari/esme-koyunu/. Aytekin I, Karabacak A, Zülkadir U, Keskin I, Boztepe S (2009) Usage of some models for describing the growth curves of Akkaraman and Anatolia Merino lambs raised in open and closed sheepfolds at the fattening period. Selçuk Journal of Agriculture and Food Sciences 23: 30-35. Aytekin I, Zülkadir U, Keskin I, Boztepe S (2010) Fitting of different mathematic models to the growth curves of female Malya lambs weaned at two different live weights. Trends in Animal and Veterinary Sciences 1:19-23. Aytekin RG, Zülkadir U (2013) The Determination of Growth Curve Models in Malya Sheep from Weaning to Two Years of Age. J Agr Sci 19: 71-78. Bangar YC, Lawar VS, Nimase RG, Nimbalkar CA (2018) Comparison of non-linear growth models to describe the growth behaviour of Deccani sheep. Agr Res 7: 490-494. Behzadi MRB, Aslaminejad AA, Sharifi AR, Simianer H (2014) Comparison of mathematical models for describing the growth of Baluchi sheep. J Agr Sci Tech-Iran 16: 57-68. Bilgin OC, Emsen E, Davis ME (2004) Comparison of non-linear models for describing the growth of scrotal circumference in Awassi male lambs. Small Ruminant Res 52: 155-160. Bilgin ÖC, Esenbuga N (2003) Parameter estimation in nonlinear growth models. Hayvansal Üretim 44:81-90. Boujenane I (2022) Comparison of nonlinear models for describing the pre-weaning growth of Timahdite lambs and genetic and non-genetic effects for curve parameters. Small Ruminant Res 216:106800. Brunner N, Kühleitner M (2020) The growth of domestic goats and sheep: A meta study with Bertalanffy-Putter models. Vet Anim Sci 10:100135. Cemal I, Ata N, Yılmaz O, Karaca O (2018) Milk production abilities of Kivircik ewes. YYÜ Tarım Bilimleri Dergisi 28: 384-388. Çelikeloğlu K, Tekerli M (2014) A comparison of goodness of fit for different growth curve models to body measurements of Pirlak lambs. Lalahan Hayvancılık Araştırma Enstitüsü Dergisi 54: 63-69. da Silva LSA, Fraga AB, da Silva FD, Beelen PMG, Silva RMD, Tonhati H, Barros CD (2012) Growth curve in Santa Ines sheep. Small Ruminant Res 105:182-185. Daskiran I, Koncagul S, Bingol M (2010) Growth characteristics of indigenous Norduz female and male lambs. J Agr Sci-Tarim Bili 16:62-69. Dehsahraei AR, Ghaderi-Zefrehei M, Rafeie F, Zakizadeh S, Shamsabadi JS, Torshizi ME, Neysi S, Rahmatalla SA (2023) Genetic analysis of growth curve in Moghani Sheep using Bayesian and restricted maximum likelihood. J Anim Sci 101:skad203. Deribe B, Tesema Z, Lakew M, Zegeye A, Kefale A, Shibesh M, Yizengaw L, Belayneh N (2023) Growth and growth curve analysis in Dorper ×Tumele crossbred sheep under a smallholder management system. Translational Animal Science 7: txad034. Domínguez-Viveros J, Canul-Santos E, Rodríguez-Almeida FA, Burrola-Barraza ME, Ortega-Gutiérrez JÁ, Castillo-Rangel F (2019) Defining growth curves with nonlinear models in seven sheep breeds in Mexico. Revista mexicana de ciencias pecuarias 10:664-675. Esenbuga N, Bilgin OC, Macit M, Karaoglu M (2000) İvesi, Morkaraman ve Tuj kuzularında büyüme eğrileri. Atatürk Üniversitesi Ziraat Fakültesi Dergisi 31(1):37-41. Eyduran E, Kücük M, Karakuş K, Özdemir T (2008) New approaches to determination of the best nonlinear function describing growth at early phases of Kıvırcık and Morkaraman breeds. Journal of Animal and Veterinary Advances 7: 799-804. Fitzhugh JH (1976) Analysis of growth curves and strategies for altering their shape. J Anim Sci 42, 1036-1051. Foroutanifar S, Khaldari M (2024) The comparisons of non‐linear models to describe the growth performance of Lori‐Bakhtiari sheep. Veterinary Medicine and Science 10: e1527. Gbangboche AB, Glele-Kakai R, Salifou S, Albuquerque LG, Leroy PL (2008) Comparison of non-linear growth models to describe the growth curve in West African Dwarf sheep. Animal 2: 1003-1012. Goliomytis M, Orfanos S, Panopoulou E, Rogdakis E (2006) Growth curves for body weight and carcass components, and carcass composition of the Karagouniko sheep, from birth to 720d of age. Small Ruminant Res 66: 222-229. Hojjati F, Hossein-Zadeh NG (2018) Comparison of non-linear growth models to describe the growth curve of Mehraban sheep. J Appl Anim Res 46: 499-504. Hossein-Zadeh NG (2017) Modelling growth curve in Moghani sheep: comparison of non-linear mixed growth models and estimation of genetic relationship between growth curve parameters. J Agr Sci-Cambridge 155: 1150-1159. Hossein-Zadeh NG (2015) Modeling the growth curve of Iranian Shall sheep using non-linear growth models. Small Ruminant Res 130: 60-66. Hossein-Zadeh NG, Golshani M (2016) Comparison of non-linear models to describe growth of Iranian Guilan sheep. Rev Colomb Cienc Pec 29: 199-209. Karaca O, Ata N, Canaz K, Cemal İ, Yilmaz O (2024) Investigation of morphological variations in Eşme and Pırlak sheep raised in breeder’s conditions. Journal of Animal Production 65: 9-17. Karaca O, Cemal İ, Yılmaz O (2012) National Genetic Improvement Project for Small Ruminants at Breeders' Conditions Projects Aydın-Denizli-Uşak Province Sub-Projects Workshop Notes. www.researchgate.net. In: Karaca, O. (Ed.), Aydın. Karaca O, Cemal İ, Yılmaz O, Ata N (2018) Halk Elinde Hayvan Islahi Ülkesel Projesi “Aydın Kıvırcık Koyunu Islahı” Alt Projesi . Birinci 5 Yıllık Dönem Ara Sonuç Raporu. Karaca O, Cemal İ, Yılmaz O, Ata N (2021a) Halk Elinde Hayvan Islahi Ülkesel Projesi “Eşme Koyunu Islahı” Alt Projesi. İkinci 5 Yıllık Dönem Ara Sonuç Raporu. Karaca O, Cemal İ, Yılmaz O, Ata N (2021b) Halk Elinde Hayvan Islahi Ülkesel Projesi “Karya Koyunu Islahı” Alt Projeleri, Üçüncü 5 Yıllık Dönem Ara Sonuç Raporu. Karaca O, Cemal İ, Yılmaz O, Yılmaz M (2009) Karya koyunu. Türkiye Ulusal Koyunculuk Kongresi. Ege Universitesi, İzmir, pp. 225-234. Karakus K, Eyduran E, Kum D, Ozdemir T, Cengiz F (2008) Determination of the best growth curve and measurement interval in Norduz male lambs. Journal of Animal and Veterinary Advances 7, 1464-1466. Kaymakçı M, Oğuz I, Ün C, Bilgen G, Taşkın T (2001) Basic characteristics of some Turkish indigenous sheep breeds. Pakistan Journal of Biological Sciences 47: 916-919. Keskin I, Dag B, Sariyel V, Gokmen M (2009) Estimation of growth curve parameters in Konya Merino sheep. S Afr J Anim Sci 39: 163-168. Kocabaş Z, Kesici T, Eliçin A (1997) Akkaraman, İvesi x Akkaraman ve Malya x Akkaraman kuzularında büyüme eğrisi. Turkish Journal of Veterinary and Animal Science 21: 267-275. Kopuzlu S, Sezgin E, Esenbuga N, Bilgin OC (2014) Estimation of growth curve characteristics of Hemsin male and female sheep. J Appl Anim Res 42: 228-232. Kozakli O, Ceyhan A, Firat MZ (2022) Modeling of growth curve models according to sex in Akkaraman lambs with different methods: Logistic and Gompertz modeling example. Journal Of Agriculture and Nature 25:916-926. Kum D, Karakus K, Ozdemir T (2010) The best non- linear function for body weight at early phase of Norduz female lambs. Trakia Journal of Science 8:62-67. Laird AK, Tyler SA, Barton AD (1965) Dynamics of normal growth. Growth 29:233-248. Lambe NR, Navajas EA, Simm G, Bünger L (2006) A genetic investigation of various growth models to describe growth of lambs of two contrasting breeds. J Anim Sci 84:2462-2654. Lewis R, Brotherstone S (2002) A genetic evaluation of growth in sheep using random regression techniques. Anim Sci 74:63-70. Lupi TM, Nogales S, León JM, Barba C, Delgado JV (2015) Characterization of commercial and biological growth curves in the Segureña sheep breed. Anim Biotechnol 9:1341-1348. Makgopa LF, Mathapo MC, Tyasi TL (2024) A systematic review of estimation of growth curve in goats. Trop Anim Health Pro 56:14. Malhado CHM, Carneiro PLS, Alfonso PRAM, Souza JAAO, Sarmento JLR (2009) Growth curves in Dorper sheep crossed with the local Brazilian breeds, Morada Nova, Rabo Largo and Santa Ines. Small Ruminant Res 84:16-21. Masari HAP, Hafezian SH, Mokhtari M, Mianji GR, Abdollahi-Arpanahi R (2021) Inferring causal relationships among growth curve traits of Lori-Bakhtiari sheep using structural equation models. Small Ruminant Res 203:106489. Nasholm A, Danell O (1996) Genetic relationships of lamb weight, maternal ability, and mature ewe weight in Swedish Finewool sheep. J Anim Sci 74:329-339. Nelder JJB (1961) The fitting of a generalization of the logistic curve. Biometrics 71:89-110. Owens FN, Dubeski P, Hanson CF (1993) Factors that alter the growth and development of ruminants. J Anim Sci 71:3138-3150. Ozturk N, Kecici PD, Serva L, Ekiz B, Magrin L (2023) Comparison of nonlinear growth models to estimate growth curves in Kivircik sheep under a semi-intensive production system. Animals-Basel 13:2379. Öner Y, Üstüner H, Orman A, Yilmaz O, Yilmaz A (2014) Genetic diversity of Kivircik sheep breed reared in different regions and its relationship with other sheep breeds in Turkey. Ital J Anim Sci 13:3382. Santos NPS, de Oliveira Neto CB, Sarmento JLR, Bezerra LR, Oliveira RL, dos Santos GV, Neto AAR, Biagiotti D (2014) Carcass traits and growth curve parameters in Santa Inês sheep. J Agr Sci-Cambridge 6:180-187. Sarmento JLR, Regazzi AJ, Sousa WH, Torres RA, Breda FC, Menezes GRO (2006) Estudo de curvas de crescimento de ovinos Santa Inês. Rev Bras Zootecn 35:435-444. Tahtali Y, Sahin M, Bayyurt L (2020) Comparison of different growth curve models in Romanov lambs. Kafkas Üniversitesi Veteriner Fakültesi Dergisi 26:609-615. Tariq MM, Bajwa MA, Waheed A, Eyduran E, Abbas F, Bokhari A, Akbar A (2011) Growth curve in Menagali sheep breed of Balochistan. Journal of Animal and Plant Sciences 21:5-7. Tariq MM, Iqbal F, Eyduran E, Bajwa MA, Huma, ZE, Waheed A (2013) Comparison of non-linear functions to describe the growth in Mengali sheep breed of Balochistan. Pak J Zool 45:661-665. Topal M, Ozdemir M, Aksakal V, Yildiz N, Dogru U (2004) Determination of the best nonlinear function in order to estimate growth in Morkaraman and Awassi lambs. Small Ruminant Res 55:229-232. von Bertalanffy L (1957) Quantitative laws in metabolism and growth. The Quarterly Review of Biology 32:217-231. Yıldız G, Soysal MI, Gürcan EK (2009) Tekirdağ ilinde yetiştirilen Karacabey merinosu kıvırcık melezi kuzularda büyüme eğrilerinin farklı modellerle belirlenmesi. Tekirdağ Ziraat Fakültesi Dergisi 6:11-19. Yilmaz O, Cemal İ, Karaca O (2013) Estimation of mature live weight using some body measurements in Karya sheep. Trop Anim Health Pro 45:397-403. Yücel İ (2022) Göçer Karya koyunu yetiştiriciliğinin sürdürülebilirlik değerlendirmesi. Fen Bilimleri Enstitüsü. Aydın Adnan Menderes Üniversitesi, Aydın p. 47. 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-7676389","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525041915,"identity":"f2a7e5eb-73bf-4854-8df7-06383a829d47","order_by":0,"name":"Yavuz Akbaş","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBADAwMGBsYHEHYC8VqYDeBaDhCphU2CKC0Gx8+YSf5gsDM2Zz/+rOLDLxsGfvYcA+aPe/BoOZNjJiHBkGxm2ZNjdnNmXxqDZM8bA4YDz/BoOZCWJmHAwGxjcCCH7TZvz2EGgxs5QC14XGZw/lmaRAJDvY3B+efPiv/2/GewJ6jlRvIxiQMMh80MbiSYMTP8OMBgIEFAi+SNx4ctGwyOG1vOeGMs2duQzCNx5lnBgTN4tPCdT2y8+aOi2nA7f/rDDz/+2MnxtydvfFCBR4sCWA4ahwyMbQw8IBqPBgYG+QYU7h98akfBKBgFo2CkAgAuIlZyq00X4AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-6863-6732","institution":"Ege University: Ege Universitesi","correspondingAuthor":true,"prefix":"","firstName":"Yavuz","middleName":"","lastName":"Akbaş","suffix":""},{"id":525041916,"identity":"28977b74-3f8b-4a81-9805-ffb415c84bd3","order_by":1,"name":"Onur Yilmaz","email":"","orcid":"","institution":"Adnan Menderes University: Adnan Menderes Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Onur","middleName":"","lastName":"Yilmaz","suffix":""},{"id":525041917,"identity":"b45422d2-1136-4895-9e71-ac4026e41cbf","order_by":2,"name":"İbrahim Cemal","email":"","orcid":"","institution":"Adnan Menderes University: Adnan Menderes Universitesi","correspondingAuthor":false,"prefix":"","firstName":"İbrahim","middleName":"","lastName":"Cemal","suffix":""},{"id":525041918,"identity":"44740010-6fac-4775-900d-a47cb931b31a","order_by":3,"name":"Nezih Ata","email":"","orcid":"","institution":"Adnan Menderes University: Adnan Menderes Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Nezih","middleName":"","lastName":"Ata","suffix":""},{"id":525041919,"identity":"5eff1c83-c487-4638-b4b9-16e674b8df9e","order_by":4,"name":"Orhan Karaca","email":"","orcid":"","institution":"Adnan Menderes University: Adnan Menderes Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Orhan","middleName":"","lastName":"Karaca","suffix":""}],"badges":[],"createdAt":"2025-09-22 13:17:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7676389/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7676389/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93720072,"identity":"5cb1e3cc-2ed3-463e-89d6-458026b79a55","added_by":"auto","created_at":"2025-10-16 22:03:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1851971,"visible":true,"origin":"","legend":"","description":"","filename":"Figures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/9474606b4e7a4629a3896f21.docx"},{"id":93720073,"identity":"7353517f-b61f-4082-bbfa-322e8c01baad","added_by":"auto","created_at":"2025-10-16 22:03:08","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120229,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/9cc5df6cd1879a8952c6c837.docx"},{"id":93719973,"identity":"2c6847f5-4f6f-4d13-b8e2-250a429e1618","added_by":"auto","created_at":"2025-10-16 21:55:08","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9606,"visible":true,"origin":"","legend":"","description":"","filename":"tropTROPD2501834.xml","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/3f686aa67390940889a6e526.xml"},{"id":93719970,"identity":"c0da72ab-a4ee-45d1-ad57-7e00afcaef9e","added_by":"auto","created_at":"2025-10-16 21:55:08","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":996,"visible":true,"origin":"","legend":"","description":"","filename":"TROPD250183451042.go.xml","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/55aeecd287ca539ca735d882.xml"},{"id":93719982,"identity":"6b19427c-1958-4b6e-bbc6-107af9c51139","added_by":"auto","created_at":"2025-10-16 21:55:08","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":906,"visible":true,"origin":"","legend":"","description":"","filename":"TROPD2501834Import.xml","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/661ddf79e1dda640c7360246.xml"},{"id":93720075,"identity":"df37a561-7e07-41e9-858c-dfc2c16823b0","added_by":"auto","created_at":"2025-10-16 22:03:08","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342159,"visible":true,"origin":"","legend":"","description":"","filename":"TROPD25018340enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/35465061b2fbf166ffa5b4ad.xml"},{"id":93719991,"identity":"f1b55f25-ee8d-460f-a6d5-bc9952182fcb","added_by":"auto","created_at":"2025-10-16 21:55:09","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1721986,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/79d56ec4a85e45f7b23abf3f.jpeg"},{"id":93720078,"identity":"96d7dacd-1de2-4169-905b-0c9e12931fed","added_by":"auto","created_at":"2025-10-16 22:03:08","extension":"emf","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":466032,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.emf","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/aecd44d4fee855521eae2a85.emf"},{"id":93719980,"identity":"631c97dc-9eb6-4df7-9612-34a90dbdf21a","added_by":"auto","created_at":"2025-10-16 21:55:08","extension":"emf","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":394708,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.emf","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/fd30bcc2d6f0e056c959c7d3.emf"},{"id":93719992,"identity":"1bc7d367-3dc9-4031-88b5-5c0da115662b","added_by":"auto","created_at":"2025-10-16 21:55:09","extension":"emf","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":469300,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.emf","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/0e7355a49d6dd4694c895ed3.emf"},{"id":93720074,"identity":"d8563fb7-0a07-4491-b2a9-513026e3deae","added_by":"auto","created_at":"2025-10-16 22:03:08","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/1c7698044b8cccb327f8349c.png"},{"id":93719977,"identity":"7bf2f044-68ea-4a35-9e14-2fa004791621","added_by":"auto","created_at":"2025-10-16 21:55:08","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/898bfaaf51ff4738d566ae80.png"},{"id":93720076,"identity":"4b45ffd4-bb5a-442c-b72b-b3bcd885ae84","added_by":"auto","created_at":"2025-10-16 22:03:08","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/3746a00b07687b485743d7ae.png"},{"id":93719990,"identity":"fb579e3c-a10f-45a1-8c96-b426357034e2","added_by":"auto","created_at":"2025-10-16 21:55:09","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20406,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/22ccb2966fa16afe6ad6c0ec.png"},{"id":93720077,"identity":"b0e11964-76a4-4900-9be6-bdd076a1c25b","added_by":"auto","created_at":"2025-10-16 22:03:08","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":23383,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/105f6cefa2f5e0b4a06a29b1.png"},{"id":93719984,"identity":"0b106f5a-e265-43b6-944e-7fa55a5034ef","added_by":"auto","created_at":"2025-10-16 21:55:08","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22036,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/f1cc6f30104ec5432d4b7ff0.png"},{"id":93719978,"identity":"77e3c657-3a82-4dee-ad2e-abd0a9e92547","added_by":"auto","created_at":"2025-10-16 21:55:08","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":19996,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/bc503a94cfa5675b1ae8ee36.png"},{"id":93719985,"identity":"d45f5f37-a286-463e-83c9-700d1a36d837","added_by":"auto","created_at":"2025-10-16 21:55:08","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/e8ef429b3404cb101ece2c3e.png"},{"id":93719981,"identity":"05a45c3c-2b49-4f22-8302-37234fa9fc2d","added_by":"auto","created_at":"2025-10-16 21:55:08","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/4e0e90496377d408f89e2f58.png"},{"id":93719986,"identity":"a12cbbc0-448f-416c-b7cb-27d837325362","added_by":"auto","created_at":"2025-10-16 21:55:08","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/73e3f7befdd69f5422305ae6.png"},{"id":93719993,"identity":"6a329ed4-fee9-4d1f-8b4e-b87b7c614850","added_by":"auto","created_at":"2025-10-16 21:55:09","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":340892,"visible":true,"origin":"","legend":"","description":"","filename":"TROPD25018340structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/add556bb1b3b7c3c832545a4.xml"},{"id":93719994,"identity":"20067458-42c3-4f03-983d-abd3f0e9497c","added_by":"auto","created_at":"2025-10-16 21:55:09","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":343563,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/fca19545b04db641be7964fc.html"},{"id":93719974,"identity":"60f28e5e-2348-4020-90ad-1a2d904edfeb","added_by":"auto","created_at":"2025-10-16 21:55:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":368686,"visible":true,"origin":"","legend":"\u003cp\u003eBody weight change of sheep breeds according to age (days)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/33a43108024f43ae7a5730d9.png"},{"id":93719969,"identity":"21828d97-be8d-450c-80e7-8488ce56bb59","added_by":"auto","created_at":"2025-10-16 21:55:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304291,"visible":true,"origin":"","legend":"\u003cp\u003eActual and estimated body weights (kg) as a function of age (days) obtained with the Gompertz, Logistic, Bertalanffy and Cubic growth models for Eşme lambs.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/c765fa0722763f13848d1dfb.png"},{"id":93719972,"identity":"89ae274d-398b-4f62-9cf8-7528023b0b4d","added_by":"auto","created_at":"2025-10-16 21:55:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":282601,"visible":true,"origin":"","legend":"\u003cp\u003eActual and estimated body weights (kg) as a function of age (days) obtained with the Gompertz, Logistic, Bertalanffy and Cubic growth models for Karya lambs.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/bc1db6e2b9e0e8698b357882.png"},{"id":93720071,"identity":"02f117e6-a962-446d-8f5d-e79460e2b88f","added_by":"auto","created_at":"2025-10-16 22:03:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":276461,"visible":true,"origin":"","legend":"\u003cp\u003eActual and predicted body weights (kg) as a function of age (days) obtained with the Gompertz, Logistic, Bertalanffy and Cubic growth models for Kıvırcık lambs\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/467b12f41ad1813c580d5cf8.png"},{"id":102445689,"identity":"95063ce6-73c1-4ec0-abd0-2da5e8f2980c","added_by":"auto","created_at":"2026-02-11 17:35:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4027442,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7676389/v1/21b73250-0aeb-4590-9bab-5ab0940399bc.pdf"}],"financialInterests":"","formattedTitle":"Growth Curves and Optimum Lamb Slaughter Ages for Eşme, Karya and Kıvırcık Sheep Breeds","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWithin the scope of the \u0026ldquo;National Project for Animal Improvement at Breeders\u0026rsquo; Conditions\u0026rdquo;, breeding studies are conducted with various species and breeds across several regions of T\u0026uuml;rkiye. Aydın Adnan Menderes University is also actively engaged in breeding research on three distinct sheep breeds: Eşme, Karya and Kıvırcık, under breeder conditions (Karaca et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). One of these studies focuses on the improvement of the Kıvırcık breed, which is raised in Aydin province as well as in many Western Anatolian provinces (Karaca et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Notable for its high fertility and productivity, Kıvırcık is one of the original and prominent breeds in T\u0026uuml;rkiye\u0026rsquo;s livestock sector. The Kıvırcık breed originates from the Thrace and Aegean regions is particularly prevalent in the fertile pastures of Marmara and Aegean. Kıvırcık has significant potential for meat production and exhibits a moderate level of performance in terms of milk yield (Kaymak\u0026ccedil;ı et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; \u0026Ouml;ner et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cemal et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnother project focused on improving Eşme sheep, which hold a significant position in Western Anatolian sheep breeding and are raised in Eşme district of Uşak Province, T\u0026uuml;rkiye. The Eşme breed was formed by transforming the native Dağlı\u0026ccedil; sheep breed into a thin-tailed form using mainly Kıvırcık and partly Chios rams, and then continuing the breeding studies towards this form. Eşme sheep not only excel in meat production and quality but also possess a noteworthy level of fertility. In other words, it is a combined breed (Karaca et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Karaca et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe third project focuses on improving the Karya sheep, which is widely raised under breeders\u0026rsquo; conditions in Western Anatolia (Karaca et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). The objectives include enhancing litter size of ewes, growth characteristics of lambs up to weaning and meat quality of lambs. The Karya sheep breed had initially emerged by breeders through unsystematic crossbreeding of fat-tailed sheep breeds, including \u0026Ouml;demiş, \u0026Ccedil;ine \u0026Ccedil;aparı, and Dağlı\u0026ccedil;, with Sakız and Kıvırcık rams. Following the initiation of a breeding program in 1996 aimed at improving the Karya sheep population in the region, along with extensive scientific research, the Karya sheep breed, which has become increasingly preferred by breeders, was officially registered as a national breed in 2013 (Yilmaz et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Due to its advantages in terms of performance and adaptation compared to the Kıvırcık and Sakız breeds, the Karya has gained popularity in Western Anatolia (Karaca et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Y\u0026uuml;cel \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Historically, the dominant sheep breed in Aydın region was \u0026Ccedil;ine \u0026Ccedil;aparı; however, the Karya, known for its high milk production and litter size, has become widespread in the plains, while the Kıvırcık breed has mostly taken place in the mountainous parts of the region (Altın et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn any species, body weight serves as a crucial key performance indicator. Changes in body weight over time are among the most frequently observed phenomena in nature and commonly utilized as indicators of biological systems to enhance production efficiency. Consequently, understanding the growth performance of animals, particularly meat producing animals, is essential for facilitating favorable changes in early muscling, fat cover and weight gain (Santos et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, fluctuations in weight over time have been a frequent subject of investigation, as the transition from a young age to adulthood represents a critical biological phenomenon for all farm animals. The use of growth curves to analyze growth over time is a common practice. Growth curve parameters are utilized in both herd management and animal breeding to assess and optimize growth performance (Abegaz et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Deribe et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the studies examining growth curves, the live weights, body measurements, carcass components, and carcass composition of animals over time are typically determined and modelled using various equations which can be either linear or nonlinear (Akbaş \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Goliomytis et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; \u0026Ccedil;elikeloğlu and Tekerli \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Growth curves, which encapsulate the growth process through numerous measurements, are a widely utilized method for understanding growth by employing a limited number of biologically interpretable parameters from growth curve models. The parameters describe specific characteristics of growth over time. Sigmoidal growth curves are well-known and applied in numerous fields including biology, agriculture, pharmacology, toxicology, and other biological and environmental processes (Brunner and K\u0026uuml;hleitner \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, modeling the live weight growth of livestock is considered important in practice (Makgopa et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Model selection can vary depending on traits being investigated, such as body weight, body dimensions, carcass components or carcass composition. It also influenced by the structure of data, including initial and final measurement times, the frequency of observation throughout life span, and fluctuations in observations over time due to physiological, environmental, and primarily genetic differences. For instance, factors that cause sudden changes in live weight - such as reaching sexual maturity at different times, climate conditions, production levels, sex, diseases, and stress \u0026ndash; can significantly impact model selection and the shape of growth curves. In previous studies various growth curve models have been employed to analyze sheep growth. The most used models, ranked according to their effectiveness for sheep growth curves, are Brody, Gompertz, Logistic, Bertalanffy and Richards. Other models that are used less frequently include Linear, Quadratic, Cubic, Negative Exponential, Cubic Spline, Morgan\u0026ndash;Mercer\u0026ndash;Flodin, Meloun I, Meloun IV, and Mitscherlich, as well as Janoschek.\u003c/p\u003e\u003cp\u003eThe growth curves of various sheep breeds have been estimated, including Akkaraman (Kocabaş et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Kozakli et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)vırcık (Akbaş et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Eyduran et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), Dağlı\u0026ccedil; (Akbaş et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), Awassi (Esenbuga et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Topal et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ali et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Morkaraman (Esenbuga et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Topal et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), Tuj (Esenbuga et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), Suffolk (Lewis and Brotherstone \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Dom\u0026iacute;nguez-Viveros et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Texel (Lambe et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), Scottish Blackface (Lambe et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), Dorper (Dom\u0026iacute;nguez-Viveros et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Gulian (Hossein-Zadeh and Golshani \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Mengali (Tariq et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tariq et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Moghani (Hossein-Zadeh \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dehsahraei et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Santa Ines (da Silva et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Santos et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Norduz (Daskiran et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kum et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), Hemsin (Kopuzlu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Baluchi (Behzadi et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Mehraban (Hojjati and Hossein-Zadeh \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Kaji (Ali et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Lori-Bakhtiari (Masari et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Foroutanifar and Khaldari \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Malya (Aytekin et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), Romanov (Tahtali et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) sheep breeds. The model that best fits the data set varies across studies and breeds. For instance, in the Morkaraman breed, Topal et al.(2004) and Eyduran et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) recommended the Gompertz model as the most effective, while Esenbuga et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and Bilgin et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) identified the Brody model as the most successful. In addition to studies advocating for the simple linear model (Kocabaş et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Akbaş et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) in estimating growth curves of sheep, there are also studies that propose the cubic model (Aytekin et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), the Brody model (Aytekin et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), the Gompertz model (Lewis and Brotherstone \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), the Richards model (Hossein-Zadeh \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and the Logistic model (Daskiran et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), among others.\u003c/p\u003e\u003cp\u003eAlthough some characteristics of Eşme, Karya and Kıvırcık sheep have been documented in literature, no studies have been conducted on the growth curves of Eşme and Karya sheep breeds. Only limited number of studies (Akbaş et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Eyduran et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ozturk et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have focused on the growth curve of Kıvırcık sheep, and these studies contain a small amount of data. The aims of this study are (1) to estimate the growth curves of the Eşme, Karya and Kıvırcık sheep breeds using the Gompertz, Bertalanffy and Logistic models; (2) to determine how the parameters vary by gender, birth type, tier, and year; (3) to establish the optimum slaughter age for these breeds based on the growth curves; and (4) to compare the growth curve results derived from individual body weights estimates with those obtained from mean body weight estimates at each age point.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData\u003c/h2\u003e\u003cp\u003eLive body weight data for the Kıvırcık, Eşme, and Karya sheep breeds were collected between 2013 and 2023 as part of the Kıvırcık Sheep Breeding Sub-Project (Project No: TAGEM/09KIV2012-01), the Eşme Sheep Breeding Sub-Project (Project No: 64ESM2011-01), and the Karya Sheep Development Sub-Projects (Project No: 20KAR2006-01, 20KAR2011-02, and 09KAR2006-01). These projects were conducted by Aydın Adnan Menderes University under the National Animal Breeding Projects at the breeder conditions (Karaca et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Karaca et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Karaca et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe total number of animals and observations is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, detailing changes by breed, gender, birth type, tier, and year, both as numbers and percentages. Specifically, for the Eşme breed, 124,657 records from 57,867 individuals were included. In contrast, there are 405,980 live weight records from 201,632 individuals of the Karya sheep breed raised in the Aydın and Denizli provinces, while the study also includes 111,528 records from 55,956 Kıvırcık individuals registered in Aydın province. Excluding birth weight, the mean number of observations for each test day was 334 for Eşme, 1,090 for Karya, and 298 for Kıvırcık, respectively.\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\u003eNumber of animals and of observations in Eşme, Karya and Kıvırcık sheep breed populations and distribution of observation to subgroups (number and percentage)\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\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e\u003cp\u003eBreeds\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eEşme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eKarya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eKıvırcık\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTotal number of individuals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e201632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e55956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTotal number of observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e405980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e111528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e204959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e50.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e51.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e201021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e53822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e48.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e185767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e45.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e74654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e66.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57960\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e199291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e30.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026ge;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultiplier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e309469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e76.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e94721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e84.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.5\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\u003eWith minor variations among breeds, 48\u0026ndash;49% of the data pertains to males, while 51\u0026ndash;52% pertains to females. Although the majority of the data across all breeds is associated with the first two birth types (singles and twins), there are notable differences in the distribution of these birth types among the breeds. In the Kıvırcık sheep breed, single births account for a higher percentage (66.9%), whereas the rates of single and twin births in the Eşme sheep breed are relatively similar. Conversely, in the Karya sheep breed, the occurrence of twins (49.1%) surpasses that of single births (45.8%).\u003c/p\u003e\u003cp\u003eEach breed comprises two tiers: multiplier flocks and base flocks within the breeding program. While multiplier flocks account for approximately 20% of the population, base flocks make up 80%. The distribution of the dataset across the years for each sheep breed typically falls within the range of 8\u0026ndash;10%. Even at the lowest of these rates, the presence of 6,666 observations (from the year 2023 for Eşme) is sufficient to estimate the growth curve for each year (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe birth weights of lambs were recorded using electronic hand scales within the first 24 hours after birth, and the lambs were identified with plastic ear tags. Consequently, the birth weights of all individuals are documented. Although births are primarily concentrated between November and March, they occur throughout nearly the entire year. This distribution of births results in periodic body weight inspections on the farms being conducted to be various times throughout the year. The live weights of all animals were measured during the farm visits. Although the birth weights of all individuals were available, it was not possible to track the live body weights of the same individuals at different time points because the data consisted of herds at the breeders\u0026rsquo; level. Consequently, age-related live weight changes, or growth curves, was examined at the population level rather than individual level. When considering live weights at the population level, daily live weights were available for each breed up to the 200th day after birth. Due to the high number of observations at each age, it was assumed that the growth curve obtained at the population level would accurately reflect the general growth curve of the sheep breeds. Because of the limited availability of live weight data after day 200, only data up to 200 days were included in this study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eGrowth curve analyses were conducted separately for each breed using two distinct approaches. In the first approach, the individual body weights of each animal were utilized and referred as the analysis using individual body weights. Initially, each model was fit to a dataset containing body weights of all lambs in the breed, and subsequently, this analyze was conducted for subgroups based on gender, birth type, tier, and year for each breed.\u003c/p\u003e\u003cp\u003eIn the second approach, growth curve analyses were conducted on the average body weights for each age points and referred as analysis using mean body weights. In this analysis, an additional data set was generated using the daily mean live body weight from birth to 200 days of age for each breed. This approach effectively eliminated individual variations in daily live body weights, allowing for a more practical representation of the mean growth curves for the breeds.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eModels\u003c/h3\u003e\n\u003cp\u003eTo describe the growth curves of Eşme, Karya, and Kıvırcık sheep, we employed three commonly used non-linear growth functions: the Bertalanffy model (von Bertalanffy \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1957\u003c/span\u003e), the Logistic model (Nelder \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1961\u003c/span\u003e), and the Gompertz model (Laird et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1965\u003c/span\u003e). The equations for the Bertalanffy, Logistic and Gompertz models are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The model parameters along with their 95% confidence limits, were estimated using the Levenberg-Marquardt algorithm within the non-linear regression procedure (NLIN) of IBM SPSS version 25.\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\u003eLinear and nonlinear growth curve models used in this study\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEquation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge at point of inflection*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWeight at point of inflection*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBertalanffy \u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u0026thinsp;=\u0026thinsp;A*(1\u0026thinsp;\u0026minus;\u0026thinsp;B*exp(\u0026minus;\u0026thinsp;C*\u0026#119905;))**3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Ln 3*B)/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8*A/27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLogistic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u0026thinsp;=\u0026thinsp;A /(1\u0026thinsp;+\u0026thinsp;B*\u0026#119890;xp(\u0026minus;\u0026thinsp;C*\u0026#119905;))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eln(B)/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA/2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGompertz \u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u0026thinsp;=\u0026thinsp;A*\u0026#119890;xp(-B*\u0026#119890;xp(\u0026minus;\u0026thinsp;C*\u0026#119905;))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eln(B)/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA/e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimple Linear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u0026thinsp;=\u0026thinsp;A\u0026thinsp;+\u0026thinsp;B*t\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuadratic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u0026thinsp;=\u0026thinsp;A\u0026thinsp;+\u0026thinsp;B*t\u0026thinsp;+\u0026thinsp;C*t\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCubic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u0026thinsp;=\u0026thinsp;A\u0026thinsp;+\u0026thinsp;B*t\u0026thinsp;+\u0026thinsp;C*t\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;D*t\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Dehsahraei et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the models presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Y represented body weights in kilograms (kg); A denotes the asymptotic body weight as time (t) approaches infinity, interpreted as the adult weight of animal; B is the integration constant that relates to the proportion of the asymptotic mature weight gained after birth, determined by the initial values of weight and time (Hojjati and Hossein-Zadeh \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); exp refers to the base of the natural logarithm; C signifies the maturation rate, which indicates the rate of weight change in relation to mature weight, reflecting how quickly the animal approaches its adult weight; and t represents the animal's age in days. The age at point of inflection, where the maximal growth rate is achieved and the growth rate transitions increasing to decreasing, as well as the weight at point of inflection, were also calculated using the equations provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eBoth linear (simple linear, quadratic, cubic) and non-linear growth curve models (Gompertz, Bertalanffy and logistic) were fitted to the datasets in question, and the relationship between body weight and age were investigated.\u003c/p\u003e\n\u003ch3\u003eGoodness-of-fit criteria\u003c/h3\u003e\n\u003cp\u003eIn the process of model comparison and selection of the optimal model, the adjusted coefficient of determination (R2a), mean squared error (MSE), Akaike information criterion (AIC) and Bayesian information criterion (BIC) were employed as goodness-of-fit metrics. A higher adjusted coefficient of determination and lower values of MSE, AIC and BIC indicate the model that provides the best fit. The criteria were calculated as follows:\u003c/p\u003e\u003cp\u003eAdjusted coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ea\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;1-((n-1)/(n-p)) (1-R\u003csup\u003e2\u003c/sup\u003e) where R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1-(RSS/TSS)\u003c/p\u003e\u003cp\u003eMean square error\u0026thinsp;=\u0026thinsp;RSS/(n-p)\u003c/p\u003e\u003cp\u003eAkaike information criteria (AIC)\u0026thinsp;=\u0026thinsp;n*ln(RSS/n)\u0026thinsp;+\u0026thinsp;2p\u003c/p\u003e\u003cp\u003eBayes information criterion (BIC)\u0026thinsp;=\u0026thinsp;n*ln(RSS/n)\u0026thinsp;+\u0026thinsp;p*ln(n)\u003c/p\u003e\u003cp\u003eThe residual sum of squares (RSS) represents the sum of the squares of the residuals; the total sum of squares (TSS) indicates the total variation in the data. The coefficient of determination (R\u0026sup2;) quantifies the proportion of variance explained by the model. In this context, n denotes the number of observations, while p signifies the number of parameters. Additionally, ln refers to the natural logarithm of a number.\u003c/p\u003e\u003cp\u003eIn comparing the subgroups of the factors (gender, birth type, tier, and year) regarding growth curve parameters, 95% confidence intervals for these parameters were utilized.\u003c/p\u003e\u003cp\u003eThe daily gain and average daily gain were calculated using body weights predicted by the models as follows:\u003c/p\u003e\u003cp\u003eDaily gain\u0026thinsp;=\u0026thinsp;BW\u003csub\u003et\u003c/sub\u003e \u0026ndash; BW\u003csub\u003et\u0026minus;1\u003c/sub\u003e,\u003c/p\u003e\u003cp\u003eAverage daily gain = (BW\u003csub\u003et\u003c/sub\u003e \u0026ndash; BW\u003csub\u003e0\u003c/sub\u003e)/t\u003c/p\u003e\u003cp\u003ewhere BW\u003csub\u003et\u003c/sub\u003e is the body weight at day t and BW\u003csub\u003e0\u003c/sub\u003e is the birth weight estimated; t is the age of lamb in days.\u003c/p\u003e\u003cp\u003eThe optimum slaughter age was determined when the average daily gain reached its maximum and began to decline. Additionally, body weight at slaughter age was predicted using the model employed in this study.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eGeneral Growth Performance\u003c/h2\u003e\u003cp\u003eThe changes in the average body weights of the breeds examined in the study over time are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The body weight of the breeds exhibited a similar increasing trend during the first 50 days, beginning with significant differences in birth weight. Throughout this period, Eşme consistently demonstrated higher average body weights while Karya maintained an intermediate position between Eşme and Kıvırcık during the initial 90 days. However, between days 90 and 120, Karya exhibited higher live weights, surpassing Eşme breed. Kıvırcık breed consistently recorded lower body weights at all ages compared to the other two breeds, with this disparity becoming more pronounced between days 70 and 165. Although Eşme breed had slightly higher live weights than Karya after day 145, the body weights of both sheep breeds displayed significantly greater variability during this final period. After the 165th day, as variability increased, the average body weights of the breeds began to converge.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGoodness-of-Fit Results of the models\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eGoodness-of-Fit Results of Non-Linear Models\u003c/h2\u003e\u003cp\u003eThe goodness-of-fit results for the nonlinear models used to estimate the growth curves of Eşme, Karya, and Kıvırcık, based on individual body weights and mean body weights for each age, presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. All nonlinear models exhibit similar goodness-of-fit results, with high R\u003csup\u003e2\u003c/sup\u003e values ranging from 0.848 to 0.886 when using individual body weights, and from 0.966 to 0.985 when using mean body weights. These indicate that all nonlinear models used effectively predict the growth curves of the sheep breeds studied. However, when considering MSE, AIC and BIC, the Gompertz model emerged as the best fit for Eşme and Kıvırcık, while the Logistic model was the best fit for Karya using individual body weights (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This aligns with the findings of Ozturk et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who proposed the Gompertz function as reliable tool for modeling Kıvırcık lamb growth. On the other hand, the Logistic model demonstrated the best performance for the Eşme and Karya sheep breeds in terms of R\u003csup\u003e2\u003c/sup\u003e, MSE, AIC, and BIC when using mean body weights at each age (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Gompertz and Bertalanffy models exhibited an equal fit for the Kıvırcık sheep breed and were found to be more successful than the Logistic model. A slight difference in the estimations derived from two different data sets using nonlinear models was observed in the Eşme sheep breed. Based on differences in AIC and BIC, the most successful model for the Eşme sheep breed was the Gompertz model when individual data were utilized, whereas the Logistic model was preferred when mean body weights were employed. Overall, due to the proximity of the results, the Logistic model was regarded as the best model for Eşme and Karya sheep breeds, while the Gompertz model was deemed the best for the Kıvırcık sheep breed.\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGoodness-of-fit statistics for nonlinear growth curve models using individual or mean body weights for each age point\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eUsing individual weights\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003eUsing mean weights\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eBreeds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003eBreeds\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCriteria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEşme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKarya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKıvırcık\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEşme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKarya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eKıvırcık\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eGompertz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e22.26\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e21.093\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.697\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e1.291\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.886\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.861\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.985\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e386776\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1380898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e340048\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e386805\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1380931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e340077\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eBertalanffy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.292\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.985\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e387234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1384736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e340347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e387263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1384769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e340376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eLogistic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e29.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e3.145\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e1.863\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.479\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.852\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.973\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.982\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.983\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e387001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1374827\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e340725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e217\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e119\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e387031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1374860\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e340754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e227\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e129\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eMSE: Mean square error; R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ea\u003c/sub\u003e: Coefficient of determination; AIC: Akaike information criteria; BIC: Bayesian information criteria; Criteria for the best model in each breed are given in bold.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe coefficient of determination levels (0.85\u0026ndash;0.89) obtained for the nonlinear models fitted to individual observations are lower than those reported by Kopuzlu et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) for Hemşin sheep (\u0026ge;\u0026thinsp;0.97), Keskin et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) for Konya Merino sheep (\u0026ge;\u0026thinsp;0.96), and Karakus et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) for Norduz sheep (\u0026ge;\u0026thinsp;0.99). However, these levels are consistent with the R\u003csup\u003e2\u003c/sup\u003e values (\u0026gt;\u0026thinsp;0.97) derived from the estimations of the same models based on mean body weights.\u003c/p\u003e\u003cp\u003eIn some studies, all models yield remarkably similar results regarding the quality of fit, observed in this study. In this case, some researchers may propose multiple models rather than identifying a single best model. Conversely, other researchers may still advocate for the best model, even while acknowledging that they consider all the models they have evaluated to be successful. Among the non-linear models, Akbaş et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) reported that the Brody model was the most effective for predicting the growth of Kıvırcık and Dağlı\u0026ccedil; lambs. In addition to this finding, several studies have also identified the Brody model (Esenbuga et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Bangar et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ali et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Deribe et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) as the most successful in predicting the growth curves of sheep. However, other research has indicated that alternative models, such as the Bertalanffy (Topal et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Dom\u0026iacute;nguez-Viveros et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Boujenane \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Gompertz (Eyduran et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Yıldız et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ozturk et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Logistic (Daskiran et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hossein-Zadeh \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and Richards models (Hossein-Zadeh and Golshani \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) may also be effective. While Hossein-Zadeh and Golshani (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) proposed the Richards model as the most successful for predicting the growth of Shall lambs, Tahtalı et al. (2020) found it to be the least effective and recommended the Cubic Spline model as the best for growth curve of Romanov lambs.\u003c/p\u003e\u003cp\u003eLupi et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported that Brody, Bertalanffy, Verhulst, Logistic and Gompertz models are suitable for describing the growth of the Segure\u0026ntilde;a sheep. However, the Bertalanffy model is the most appropriate for explaining the biological growth of the Segure\u0026ntilde;a breed from birth to maturity, while the logistic model is the most suitable for representing the commercial growth curve from birth to slaughter age (80 days). The commercial growth curve is particularly significant, even though the slaughter age is influenced by cultural factors, as it relates to the carcass size demanded by consumers.\u003c/p\u003e\u003cp\u003eTopal et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) estimated the growth curves of Morkaraman and Awassi lambs from birth to 360 days of age using the Brody, Gompertz, Logistic and Bertalanffy models. They reported that the Bertalanffy model provided the best fit for Awassi lambs, while the Gompertz model was the most suitable for Morkaraman lambs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eGoodness-of-Fit Results of Linear Models\u003c/h2\u003e\u003cp\u003eGoodness-of-fit results of linear models utilizing mean body weight for each age point are significantly superior to those obtained from analysis using individual body weights (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). When comparing the Simple Linear, Quadratic and Cubic models in terms of fit, it was found that the Cubic model exhibited a better fit for the Eşme and Karya sheep breeds. Notably, in the estimations conducted for Kıvırcık sheep, the best linear model is Cubic when individual data are employed, whereas it is Quadratic when mean body weights are utilized.\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGoodness-of-fit statistics of linear models using individual weights (individual) or mean body weights for each age (Mean)\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\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eEşme\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eKarya\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eKıvırcık\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCriteria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndividual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndividual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIndividual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimple Linear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.338\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.950\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e390043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1401612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e348565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e276\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e390062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1401634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e348584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e283\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuadratic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21.350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e1.406\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.885\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\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.984\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e388770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1393528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e341395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e67\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e388799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1393561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e341424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e77\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCubic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e22.157\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.926\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e29.511\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.386\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e21.196\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.414\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.887\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.975\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.852\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.987\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.860\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.984\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e386208\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e205\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1374146\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e340589\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e386247\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e218\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1374189\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e340627\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eMSE: Mean square error; R\u003csup\u003e2\u003c/sup\u003e: Coefficient of determination; AIC: Akaike information criteria; BIC: Bayesian information criteria; p: number of parameters in the model. Criteria for the best model in each breed are given in bold.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAlthough Lambe et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) assert that the fastest growth phase is often assumed to be linear, as observed in young animals, they found that the R\u003csup\u003e2\u003c/sup\u003e values of linear models were significantly lower (\u0026lt;\u0026thinsp;0.94) compared to non-linear models (\u0026gt;\u0026thinsp;0.98) for Texel and Scottish Blackface lambs. They reported that the Richards and Gompertz models provided better predictions of growth than the logistic, Gompertz, Richards and Exponential models. While the Richards model includes the D parameter, which offers additional insights into growth, this parameter does not enhance the model\u0026rsquo;s fit. Consequently, the Gompertz model, which has fewer parameters and demonstrates a good fit, is preferred in this context (Lambe et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhen three linear and three nonlinear models were compared simultaneously based on all fit criteria, the Cubic model was determined to be the best fit for Eşme and Karya, while the Gompertz model was the best fit for Kıvırcık. Akbaş et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) indicated that the Quadratic model provided the best fit for the growth of Kıvırcık lambs, whereas the Simple Linear model was the best fit for Dağlı\u0026ccedil; lambs.\u003c/p\u003e\u003cp\u003eIt is believed that environmental factors, such as varying rearing and feeding practices, along with genetic differences, significantly influence the outcomes of different models. Additionally, the timing of body weight measurements and the frequency of periodic assessments are crucial factors. The optimal approach for estimating growth curves is to begin weighing at birth and continue until adult weight is achieved.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eGrowth Curve Parameters\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003eNon-linear growth curve parameters using individual body weights\u003c/h2\u003e\u003cp\u003eThe changes in the growth curve parameters estimated by nonlinear models, both in general and according to gender, birth type, tier and years are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e for the Eşme, Karya and Kıvırcık breeds, respectively. The estimated growth curves summarize body weight data using a limited number of growth curve parameters. da Silva et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) stated that nonlinear models enhance our understanding and management of growth curve parameters that can also be interpreted biologically.\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 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNon-linear growth curve parameters using individual body weights in the Eşme sheep breed\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eGompertz\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eBertalanffy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003eLogistic\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroups\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eB\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eB\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eA\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eB\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eC\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.507\u003c/p\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.476\u003c/p\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e57.681\u003c/p\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.582\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e42.058\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.088\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e8.478\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.969\u003c/p\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.406\u003c/p\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.236\u003c/p\u003e\u003cp\u003e0.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.569\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e39.285\u003c/p\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.079\u003c/p\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.174\u003c/p\u003e\u003cp\u003e0.337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.554\u003c/p\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66.420\u003c/p\u003e\u003cp\u003e0.585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.598\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e45.183\u003c/p\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.908\u003c/p\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.027\u003c/p\u003e\u003cp\u003e0.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.415\u003c/p\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e58.025\u003c/p\u003e\u003cp\u003e0.422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.573\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e42.670\u003c/p\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.007\u003c/p\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.889\u003c/p\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.537\u003c/p\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e58.552\u003c/p\u003e\u003cp\u003e0.467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e42.064\u003c/p\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.959\u003c/p\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.528\u003c/p\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.579\u003c/p\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.430\u003c/p\u003e\u003cp\u003e1.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38.414\u003c/p\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.500\u003c/p\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.968\u003c/p\u003e\u003cp\u003e2.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.603\u003c/p\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47.258\u003c/p\u003e\u003cp\u003e3.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.597\u003c/p\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e35.663\u003c/p\u003e\u003cp\u003e1.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.968\u003c/p\u003e\u003cp\u003e0.895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.701\u003c/p\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.460\u003c/p\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56.512\u003c/p\u003e\u003cp\u003e0.326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.579\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e41.595\u003c/p\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.366\u003c/p\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultiplier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.038\u003c/p\u003e\u003cp\u003e0.461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.542\u003c/p\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62.977\u003c/p\u003e\u003cp\u003e0.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.594\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e44.005\u003c/p\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.951\u003c/p\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.946\u003c/p\u003e\u003cp\u003e0.720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.458\u003c/p\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62.982\u003c/p\u003e\u003cp\u003e1.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.588\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e40.847\u003c/p\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.818\u003c/p\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.217\u003c/p\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.444\u003c/p\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e53.142\u003c/p\u003e\u003cp\u003e0.731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.572\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e41.830\u003c/p\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.605\u003c/p\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.441\u003c/p\u003e\u003cp\u003e0.560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.365\u003c/p\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52.262\u003c/p\u003e\u003cp\u003e0.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.564\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e39.261\u003c/p\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.748\u003c/p\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.923\u003c/p\u003e\u003cp\u003e1.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.734\u003c/p\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e93.080\u003c/p\u003e\u003cp\u003e2.781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e48.003\u003c/p\u003e\u003cp\u003e0.414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.382\u003c/p\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.891\u003c/p\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.276\u003c/p\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e45.377\u003c/p\u003e\u003cp\u003e0.734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.545\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e36.895\u003c/p\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.368\u003c/p\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.639\u003c/p\u003e\u003cp\u003e0.285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.308\u003c/p\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43.360\u003c/p\u003e\u003cp\u003e0.365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.543\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38.867\u003c/p\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.012\u003c/p\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.467\u003c/p\u003e\u003cp\u003e0.891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.713\u003c/p\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e78.588\u003c/p\u003e\u003cp\u003e1.754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.626\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e47.597\u003c/p\u003e\u003cp\u003e0.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.907\u003c/p\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.776\u003c/p\u003e\u003cp\u003e0.806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.567\u003c/p\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69.546\u003c/p\u003e\u003cp\u003e1.524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.604\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e44.254\u003c/p\u003e\u003cp\u003e0.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.704\u003c/p\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.736\u003c/p\u003e\u003cp\u003e0.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.476\u003c/p\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56.931\u003c/p\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.582\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e41.386\u003c/p\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.529\u003c/p\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.369\u003c/p\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.446\u003c/p\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e55.403\u003c/p\u003e\u003cp\u003e1.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e40.237\u003c/p\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.238\u003c/p\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.595\u003c/p\u003e\u003cp\u003e1.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.629\u003c/p\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70.292\u003c/p\u003e\u003cp\u003e1.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.610\u003c/p\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e46.476\u003c/p\u003e\u003cp\u003e0.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.444\u003c/p\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003cp\u003e0.000\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=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNon-linear growth curve parameters using individual body weights in the Karya sheep breed\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eGompertz\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eBertalanffy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003eLogistic\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroups\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eB\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eB\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eA\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eB\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eC\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.052\u003c/p\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.456\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.396\u003c/p\u003e\u003cp\u003e0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.576\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e39.628\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e8.548\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.801\u003c/p\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.393\u003c/p\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47.341\u003c/p\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e37.148\u003c/p\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.063\u003c/p\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.529\u003c/p\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.520\u003c/p\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e55.813\u003c/p\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.586\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e42.233\u003c/p\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.063\u003c/p\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.481\u003c/p\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.395\u003c/p\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.283\u003c/p\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e40.497\u003c/p\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.117\u003c/p\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.809\u003c/p\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.506\u003c/p\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.579\u003c/p\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.584\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e39.062\u003c/p\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.931\u003c/p\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.955\u003c/p\u003e\u003cp\u003e0.477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.614\u003c/p\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52.126\u003c/p\u003e\u003cp\u003e0.809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.604\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e37.302\u003c/p\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.613\u003c/p\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.411\u003c/p\u003e\u003cp\u003e1.702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.721\u003c/p\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56.152\u003c/p\u003e\u003cp\u003e2.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38.541\u003c/p\u003e\u003cp\u003e0.744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e10.311\u003c/p\u003e\u003cp\u003e0.457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.670\u003c/p\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.431\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49.660\u003c/p\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.572\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38.591\u003c/p\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.338\u003c/p\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultiplier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.930\u003c/p\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.514\u003c/p\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56.296\u003c/p\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.585\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e42.496\u003c/p\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.035\u003c/p\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.626\u003c/p\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.297\u003c/p\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44.578\u003c/p\u003e\u003cp\u003e0.374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.550\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e35.606\u003c/p\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.381\u003c/p\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.690\u003c/p\u003e\u003cp\u003e0.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.290\u003c/p\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44.139\u003c/p\u003e\u003cp\u003e0.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.548\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e36.049\u003c/p\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.386\u003c/p\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.818\u003c/p\u003e\u003cp\u003e0.319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.399\u003c/p\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49.584\u003c/p\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38.810\u003c/p\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.258\u003c/p\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.451\u003c/p\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.308\u003c/p\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40.806\u003c/p\u003e\u003cp\u003e0.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e35.130\u003c/p\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.747\u003c/p\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.875\u003c/p\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.510\u003c/p\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e54.760\u003c/p\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.587\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e40.051\u003c/p\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.768\u003c/p\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.918\u003c/p\u003e\u003cp\u003e0.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.549\u003c/p\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e61.371\u003c/p\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e43.845\u003c/p\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.919\u003c/p\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.280\u003c/p\u003e\u003cp\u003e0.539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.660\u003c/p\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e71.646\u003c/p\u003e\u003cp\u003e1.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.619\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e44.349\u003c/p\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.555\u003c/p\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.844\u003c/p\u003e\u003cp\u003e0.320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.531\u003c/p\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e55.550\u003c/p\u003e\u003cp\u003e0.516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.589\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e41.294\u003c/p\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.021\u003c/p\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.646\u003c/p\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.440\u003c/p\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47.500\u003c/p\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.570\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38.556\u003c/p\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.715\u003c/p\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.673\u003c/p\u003e\u003cp\u003e0.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.515\u003c/p\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56.207\u003c/p\u003e\u003cp\u003e0.658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.588\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e40.505\u003c/p\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.881\u003c/p\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.305\u003c/p\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.502\u003c/p\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52.153\u003c/p\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.581\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e41.283\u003c/p\u003e\u003cp\u003e0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.081\u003c/p\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\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=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNon-linear growth curve parameters using individual body weights in the Kıvırcık sheep breed\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eGompertz\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eBertalanffy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003eLogistic\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroups\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eB\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eB\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eA\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eB\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eC\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e38.368\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.106\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.283\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41.408\u003c/p\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.546\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e34.181\u003c/p\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.282\u003c/p\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.528\u003c/p\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.229\u003c/p\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.986\u003c/p\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.537\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e31.997\u003c/p\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.966\u003c/p\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.268\u003c/p\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.334\u003c/p\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44.915\u003c/p\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.556\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e36.426\u003c/p\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.612\u003c/p\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.953\u003c/p\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.251\u003c/p\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41.976\u003c/p\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.541\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e34.749\u003c/p\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.068\u003c/p\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.430\u003c/p\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.347\u003c/p\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40.513\u003c/p\u003e\u003cp\u003e0.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.557\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e33.260\u003c/p\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.720\u003c/p\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.881\u003c/p\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.376\u003c/p\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.900\u003c/p\u003e\u003cp\u003e0.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e29.885\u003c/p\u003e\u003cp\u003e0.392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.745\u003c/p\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.560\u003c/p\u003e\u003cp\u003e3.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.567\u003c/p\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47.022\u003c/p\u003e\u003cp\u003e6.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.604\u003c/p\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e32.431\u003c/p\u003e\u003cp\u003e1.698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.586\u003c/p\u003e\u003cp\u003e0.724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.788\u003c/p\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.275\u003c/p\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40.721\u003c/p\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.545\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e33.715\u003c/p\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.255\u003c/p\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultiplier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.687\u003c/p\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.310\u003c/p\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44.104\u003c/p\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.552\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e36.166\u003c/p\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.386\u003c/p\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.377\u003c/p\u003e\u003cp\u003e0.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.191\u003c/p\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e39.321\u003c/p\u003e\u003cp\u003e0.480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.532\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e32.268\u003c/p\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.589\u003c/p\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.926\u003c/p\u003e\u003cp\u003e0.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.165\u003c/p\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.908\u003c/p\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.524\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e31.909\u003c/p\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.598\u003c/p\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.259\u003c/p\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.233\u003c/p\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e39.829\u003c/p\u003e\u003cp\u003e0.472\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.537\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e33.608\u003c/p\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.990\u003c/p\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.194\u003c/p\u003e\u003cp\u003e0.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.300\u003c/p\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41.133\u003c/p\u003e\u003cp\u003e0.486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.549\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e34.081\u003c/p\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.459\u003c/p\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.134\u003c/p\u003e\u003cp\u003e0.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.322\u003c/p\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41.619\u003c/p\u003e\u003cp\u003e0.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.554\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e33.571\u003c/p\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.521\u003c/p\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.045\u003c/p\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.344\u003c/p\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46.739\u003c/p\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.556\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38.036\u003c/p\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.845\u003c/p\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.376\u003c/p\u003e\u003cp\u003e0.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.258\u003c/p\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41.439\u003c/p\u003e\u003cp\u003e0.457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.543\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e34.296\u003c/p\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.113\u003c/p\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.000\u003c/p\u003e\u003cp\u003e0.488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.356\u003c/p\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48.462\u003c/p\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.565\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e36.442\u003c/p\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.332\u003c/p\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.913\u003c/p\u003e\u003cp\u003e0.418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.352\u003c/p\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46.232\u003c/p\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.560\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e36.300\u003c/p\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.593\u003c/p\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.444\u003c/p\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.311\u003c/p\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.546\u003c/p\u003e\u003cp\u003e0.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e33.379\u003c/p\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.743\u003c/p\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.779\u003c/p\u003e\u003cp\u003e0.366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.380\u003c/p\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44.906\u003c/p\u003e\u003cp\u003e0.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.564\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e35.548\u003c/p\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.843\u003c/p\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eParameter A (Mature Size)\u003c/h2\u003e\u003cp\u003eParameter A, which represents the asymptotic limit of body weight as age approaches infinity, provides valuable information about adult body weight, irrespective of fluctuations caused by genetic and environmental factors (Lupi et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This parameter, significant for understanding growth and development, is typically included as a criterion in selection processes.\u003c/p\u003e\u003cp\u003eThe parameters A, estimated using the Logistic, Gompertz and Bertalanffy models, were 42.1, 50.5 and 57.7 kg for the Eşme sheep breed, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The same parameter was estimated to be 39.6, 46.1 and 51.4 kg for the Karya sheep breed (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), and 34.2, 38.4 and 41.4 kg for the Kıvırcık sheep breed (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), respectively.\u003c/p\u003e\u003cp\u003eThe differences in parameter A among the models within the same breed are statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Across all sheep breeds, the logistic model produced the lowest estimates parameter A, whereas the Bertalanffy model yielded the highest estimates.\u003c/p\u003e\u003cp\u003eThe differences in breeds within the same model regarding parameter A were found to be significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). It was determined that all breeds differed from one another in terms of parameter A, and this finding was consistent across all models.\u003c/p\u003e\u003cp\u003eMales exhibited higher values than females for parameter A (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This difference between the sexes were 10.2, 15.2, 5.9 kg in Eşme; 6.7, 8.5 and 5.1 kg in Karya; and 5.7, 6.9 and 4.4 kg in Kıvırcık, as determined by the Gompertz, Bertalanffy and Logistic models, respectively. The sexual differences for each breed were found to be greatest with the Bertalanffy model and least with the Logistic model.\u003c/p\u003e\u003cp\u003eDepending on the increase in the number of lambs born per lambing ewe, a general decrease was observed in parameter A. According to the results of the Gompertz model, as the birth type increased from 1 to 4, parameter A was recorded as follows: 51, 51, 46, and 42 kg in Eşme; 47, 46, 45, and 47 kg in Karya; and 39, 37, 34, and 40 kg in Kıvırcık, respectively. The decrease was evident in the first three birth types and was statistically significant. The lower weights of twins or triplets can be attributed to the limited capacity of dams to provide adequate nourishment for the development of multiple fetuses, as well as insufficient milk production for newborn lambs, which may explain their reduced performance (Hojjati and Hossein-Zadeh \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, the final birth type group (4+) exhibited high variation due to the inclusion of four or more lambs with a small number of observations.\u003c/p\u003e\u003cp\u003eIn the Gompertz, Bertalanffy and Logistic models, the multiplier herds exhibited significantly higher values for parameter A compared to the base herds (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Comparing the same models for differences in parameter A between the multiplier and base herds were 4.3, 6.5, and 2.4 kg in Eşme; 5.3, 6.6, and 3.9 kg in Karya; and 2.9, 3.4, and 2.5 kg in Kıvırcık, respectively. The superiority of the multiplier elites in each breed was highest in the Bertalanffy model and lowest in the Logistic model. It was observed that an increase in body weight was achieved in elite herds due to the selection program implemented in these herds.\u003c/p\u003e\u003cp\u003eThe estimates for parameter A exhibited fluctuating change over the years (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). However, when comparing the values from 2013 to 2023, significant increases were observed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with approximately 6\u0026ndash;7 kg in Eşme, 5\u0026ndash;7 kg in Karya and 4\u0026ndash;6 kg in Kıvırcık, depending on the models used. Therefore, the breeding program should be reviewed to identify potential reasons for the fluctuations in body weight over the years.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eNon-linear growth curve parameters using mean body weights\u003c/h2\u003e\u003cp\u003eThe growth curve parameters estimated by Gompertz, Bertalanffy and Logistic models based on the mean body weights at each age point, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The parameter A, estimated using the Logistic, Gompertz and Bertalanffy models, was 41.2, 43.6, and 45.2 kg for the Eşme sheep breed, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). For the Karya sheep breed, the same parameter was estimated at 37.4, 38.6, and 39.5 kg, while for the Kıvırcık sheep breed it was estimated at 37.2, 39.6, and 41.2 kg, respectively.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGrowth curve parameters estimated by Gompertz, Bertalanffy and Logistic models based on mean body weights for ages\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\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eGompertz\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eBertalanffy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eLogistic\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreeds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eB\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eC\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eA\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eB\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eC\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEşme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.620 a\u003c/p\u003e\u003cp\u003e0.648\u003c/p\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.445 a\u003c/p\u003e\u003cp\u003e0.079\u003c/p\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.018 a\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.173 a\u003c/p\u003e\u003cp\u003e0.839\u003c/p\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.599 a\u003c/p\u003e\u003cp\u003e0.016\u003c/p\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.015 a\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e41.230 a\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.398\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e6.544 a\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.323\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.028 a\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKarya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.638 b\u003c/p\u003e\u003cp\u003e0.371\u003c/p\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.871 b\u003c/p\u003e\u003cp\u003e0.107\u003c/p\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.024 b\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39.445 b\u003c/p\u003e\u003cp\u003e0.475\u003c/p\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.684 b\u003c/p\u003e\u003cp\u003e0.022\u003c/p\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.020 b\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e37.359 b\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.221\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e8.415 b\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.407\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.035 b\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKıvırcık\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e39.615 b\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.448\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2.251 a\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.017 a\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.164 b\u003c/p\u003e\u003cp\u003e0.558\u003c/p\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.564 a\u003c/p\u003e\u003cp\u003e0.009\u003c/p\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.014 a\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e37.184 b\u003c/p\u003e\u003cp\u003e0.314\u003c/p\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.547 a\u003c/p\u003e\u003cp\u003e0.194\u003c/p\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.026 a\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003ea, b: Same parameter differences between sheep breeds containing different letters within the same model are significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); A, B: Same parameter differences between models containing different letters within the same sheep breeds are significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); SE standard error of parameter A, B or C mean\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhile there was no significant difference in parameter A between Karya and Kıvırcık sheep breeds, Eşme exhibited a higher parameter A (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to the others (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). When comparing models within the same breed, the Logistic model provided lower estimates (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than the other two models across all breeds (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Additionally, no significant difference was observed between the Gompertz and Bertalanffy models.\u003c/p\u003e\u003cp\u003eThe parameter A estimated from the mean body weights in Eşme and Karya (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) was found to be lower than the values estimated from the individual body weights (Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The differences observed were more pronounced in the Gompertz and Bertalanffy models compared to the Logistic model. However, in Kıvırcık, the results were either the opposite (for the Logistic, Gompertz models) or comparable (for the Bertalanffy model) (Tables\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Adult weight is influenced by various factors, including species, breed, selection method, management system, and environmental conditions (Malhado et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The retention of body weight records until adulthood significantly impacts the accuracy of the estimates obtained. It is reported that the body weight of adult females of the Eşme sheep breed is approximately 55\u0026ndash;60 kg, while that of males is around 65\u0026ndash;70 kg (Anonymous \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Since the body weights recorded during the first 200 days were utilized in this study, the estimates for adult weight were lower than the expected values for the Eşme breed. Akbaş et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) estimated the adult age weight of Kıvırcık sheep breed to be significantly higher (88, 76, and 98 kg, respectively) than the results obtained in this study (38, 34, and 41 kg) using the Gompertz, logistic and Bertalanffy models. This discrepancy arises because this study utilized data from the first 200 days, whereas Akbaş et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) based their estimates on live weights from birth to the 420th day. Similarly, Eyduran et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) estimated the adult weight of Kıvırcık sheep to be higher (61 and 52 kg) than the findings of this study using the Gompertz, and Logistic models based on the first 180 days data. In contrast, \u0026Ouml;zturk et al. (2023) reported adult weights for Kıvırcık sheep that align more closely with the levels determined in this study (39, 32, and 44 kg, respectively) using the Gompertz, logistic, Bertalanffy models. These results underscore the significance of the duration of weight data collection in accurately estimating adult weight.\u003c/p\u003e\u003cp\u003eAytekin and Z\u0026uuml;lkadir (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) estimated the parameter A in Malya sheep by analyzing monthly body weights from weaning to two years of age, yielding values of 71.17 kg and 70.15 kg using the Gompertz and Logistic models, respectively. Since the analysis was conducted up to two years of age, the estimated parameter A is significantly higher than the values reported in other studies. In contrast, parameter A for Mehraban sheep was estimated at 54.05 kg and 51.30 kg using the Gompertz and Logistic models, respectively, while the Brody model, which provided the best fit, yielded a value of 66.90 kg. This elevated adult weight can be attributed to the fact that the Mehraban sheep breed is primarily a meat type breed.\u003c/p\u003e\u003cp\u003eSince a growth curve study on Eşme and Karya sheep breeds has not been conducted previously, no research exists to directly compare the results of the growth curve parameter.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eParameter B\u003c/h2\u003e\u003cp\u003eThe parameter B estimated using nonlinear models (Logistic, Gompertz and Bertalanffy) based on individual body weights was determined to be 8.474, 2.476 and 0.582 in Eşme sheep breed, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the Karya sheep breed, the same parameter was found to be 8.548, 2.456, and 0.576 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), while in the Kıvırcık sheep breed, it was 7.282, 2.282, and 0.546 (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), respectively. The differences in parameter B among the models were significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the trends in parameter B across the models were similar for all sheep breeds. Using the mean body weights at each age derived from the Logistic, Gompertz and Bertalanffy models, parameter B was estimated to be 6.5, 2.5, and 0.6 in Eşme sheep breed, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). In the Karya sheep breed the same parameter was estimated as 8.4, 2.9, and 0.7, while for the Kıvırcık sheep breed, the estimates were 5.6, 2.3, and 0.6, respectively. Karya sheep breed exhibited significantly higher values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for parameter B across all nonlinear models based on mean body weights compared to the other sheep breeds, whereas no significant difference was observed between the Eşme and Kıvırcık sheep breeds (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The differences among the models within the same breed were also found to be quite significant (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eComparing the results from individual and mean body weights in relation to parameter B, the difference was found to be insignificant in Eşme and Kıvırcık sheep breed, while significant for Karya breed according to the Gompertz and Bertalanffy models. Conversely, the Logistic model indicated significant differences for Eşme and Kıvırcık sheep breeds, but an insignificant difference for Karya sheep breed.\u003c/p\u003e\u003cp\u003eDifferences in parameter B among sheep breeds were found to be significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the Bertalanffy model. In contrast, in the Logistic and Gompertz models indicated that Eşme and Karya sheep breeds exhibited similar values for parameter B, while Kıvırcık sheep breed demonstrated statistically lower (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) B values compared to both Eşme and Karya sheep breeds (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMales exhibited significantly higher parameter B values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than females across all sheep breeds (Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Generally, parameter B was greater in multiplier flocks compared to the values of base flocks. However, this difference was found to be insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in Kıvırcık sheep breed (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), while it was significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the Eşme and Karya sheep breeds (Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eParameter B increased with the number of lambs born per lambing ewe. However, the rate of this increase tended to diminish as the number of lambs increased. In each breed and model, the level of the parameter B for single lambs was found to be lower than the other groups. The differences among the birth type subgroups varied according to the models and breeds. While there was no significant difference in parameter B among the birth type subgroups, except for single in the Eşme and Kıvırcık sheep breeds according to the Gompertz and Bertalanffy models. Significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed among the first three birth type groups in the Karya sheep breed across all models as in the Eşme sheep breed using the Logistic model.\u003c/p\u003e\u003cp\u003eAlthough the estimates of the B parameter fluctuate over the years, a comparison between the first year (2013) and the last year (2023) revealed that the B parameter increased by 0.19 with the Gompertz model, 0.03 with the Bertalanffy model, and 1.53 with the Logistic model across all sheep breeds. The significant changes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) observed between the years in all models and breeds indicate that the B parameter increased as a result of the breeding programs implemented in the populations studied. Using the Logistic, Gompertz and Bertalanffy models, Akbaş et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) estimated the parameter B for Kıvırcık to be 6.25, 2.35, and 0.59, respectively. These values are comparable to the results obtained in this study, which are 7.28, 2.28, and 0.55 (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In contrast, \u0026Ouml;zturk et al. (2023) reported slightly different values for the same parameter: 4.36, 1.97, and 0.51. The value estimated by Eyduran et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) for parameter B using the Logistic model was 7.38, which is very close to the value of 7.28 found in this study for Kıvırcık sheep breed. However, the value of 0.014 obtained from the Gompertz model was significantly different from the value of 2.28 reported in this study. Dominguez-Viveros et al. (2019), in their research on seven different breeds, estimated the parameter B to range from 6.05 to 8.48 using the Logistic model, from 2.14 to 2.54 with the Gompertz model, and from 0.524 to 0.597 with the Bertalanffy model. In addition to researchers who argue that the integration constant B lacks a biological interpretation (Fitzhugh \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Behzadi et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kozakli et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) or is related to initial weight (Santos et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bangar et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), some researchers (Gbangboche et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Hojjati and Hossein-Zadeh \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) assert that B represent the proportion of asymptotic mature weight to be gained after birth, determined by the initial weight and time. Furthermore, the results obtained indicate that parameter B from different models does not reflect the same phenomenon.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eParameter C (Maturation Rate)\u003c/h2\u003e\u003cp\u003eThe parameter C can be interpreted as the rate of maturation in animals, indicating the speed at which they reach their asymptotic weight. Consequently, a higher value of parameter C reflects a greater degree of precocity in the animal (Santos et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, animals exhibiting increased precocity do not necessarily have a higher likelihood of attaining greater weights at maturity compared to those that grow more slowly. The parameter C estimates from non-linear models (Logistic, Gompertz and Bertalanffy) using individual body weights, was found to be 0.030, 0.015, and 0.011 in Eşme sheep breed, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In Karya sheep breed, the same parameter was recorded as 0.033, 0.017, and 0.012 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), while in Kıvırcık sheep breed, it was 0.033, 0.018, and 0.013 (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), respectively. The differences in parameter C among the models are significant across all breeds (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The Logistic model exhibited the highest parameter C in all sheep breeds, whereas the Bertalanffy model displayed the lowest C parameter. When breeds were compared in parameter C, significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed among all breeds in the Gompertz and Bertalanffy models. However, in the logistic model, Karya and Kıvırcık sheep breeds exhibited similar maturation rates (0.033), while Eşme demonstrated a statistically lower maturation rate (0.030) compared to the other breeds (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eBased on mean body weights at each age, the parameter C estimated by the Logistic, Gompertz and Bertalanffy models was found to be 0.028, 0.018, and 0.015 for the Eşme sheep breed; 0.035, 0.024, and 0.020 for the Karya sheep breed; and 0.026, 0.017, and 0.014 for the Kıvırcık sheep breed, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). In this analysis, the Karya sheep breed exhibited significantly higher values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in maturation speed compared to the other two breeds. No significant difference was observed between Eşme and Kıvırcık sheep breeds regarding parameter C. All models within the same breed demonstrated significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the parameter C. The lowest parameter C was obtained using the Bertalanffy model, while the Logistic model yielded the highest value.\u003c/p\u003e\u003cp\u003eWhen comparing the results from individual and mean body weights in relation to parameter C, the differences were found to be insignificant in Kıvırcık sheep breed, while significant differences were observed in Eşme and Karya sheep breeds using the Gompertz and Bertalanffy models. In contrast, in the Logistic model revealed significant differences in Karya and Kıvırcık sheep breeds, but an insignificant difference in Eşme sheep breed.\u003c/p\u003e\u003cp\u003eThe difference in the parameter C between males and females was found to be significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in Eşme and Kıvırcık sheep breeds (Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), while it was insignificant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in the Karya sheep breed (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Females exhibited higher C values than males in Eşme and Kıvırcık sheep breed, indicating that females reach adult weight earlier than males. Various studies conducted on different sheep breeds, including Mehraban, Akkaraman, Segure\u0026ntilde;a, Hemsin sheep breeds that the parameter C is higher in females (Kopuzlu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lupi et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hojjati and Hossein-Zadeh \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kozakli et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, the estimated levels of parameter C were found to be lower than those reported in some studies (Bilgin and Esenbuga \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Kopuzlu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and comparable to others (Dom\u0026iacute;nguez-Viveros et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Conversely, these levels were determined to be higher than those in several studies (Gbangboche et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Aytekin and Z\u0026uuml;lkadir \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lupi et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The observed differences may be attributed to genetic factors both between and within breeds, the time unit used (month instead of day as in the present study), and the various growth functions employed (Goliomytis et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe difference between multiplier and base flock in parameter C was generally found to be significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in Eşme and Kıvırcık sheep breeds (Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), while it was insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in Karya sheep breed (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Base flocks exhibited higher values of parameter C in Eşme and Kıvırcık sheep breed, although the difference was not significant when using the Gompertz model in Eşme sheep breed.\u003c/p\u003e\u003cp\u003eThe variation in parameter C, in relation to the increase in the number of lambs born per ewe, differed across models and breeds. Due to the very low standard errors of parameter C, even small differences were found to be statistically significant. The change in the parameter C, as estimated by the Gompertz and Logistic models in relation to the increase in the number of lambs at birth, was significant in the Eşme and Kıvırcık sheep breeds but insignificant in Karya sheep breed. Conversely, the change determined by the Bertalanffy model was significant in Karya and Kıvırcık sheep breeds, but insignificant in Eşme sheep breed. In Eşme sheep breed, the significant difference was observed between the first two and the last two subgroups, while in Karya sheep breed, the first group differed from the others, and in Kıvırcık sheep breed, the last group was distinct from the others. In instances where the differences between groups were significant, the level of parameter C tended to increase with the number of offspring in Eşme sheep breed, whereas it tended to decrease in the other two breeds. Single-born lambs generally exhibited lower values for the parameter C compared to other levels; they reached mature weight later but ultimately attained a higher mature weight (Ozturk et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe estimates for parameter C exhibited significant fluctuations over the years. When comparing the parameter C values from the first and the last years examined in the study, it is evident that the overall change is generally insignificant. The only exception is observed in the Logistic model for Kıvırcık sheep breed, which demonstrated an increase from 0.030 in 2013 to 0.034 in 2023 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the fluctuations over the years indicate that there is no stable and significant change in the maturation rate as a result of the breeding program implemented in the populations. In farm animals, growth rate and adult live weight should be managed by taking into account factors such as difficult births, the need for increased space in housing and transportation, increased nutrient requirements and genetic potential (Owens et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Nasholm and Danell \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Aytekin et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe actual and estimated body weights for each age, based on the models using individual data for the Eşme, Karya and Kıvırcık sheep breeds, are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eCorrelations between Growth Curve Parameters\u003c/h2\u003e\u003cp\u003eIn nonlinear growth curve models, a positive correlation was observed between parameters A and B, while a strong negative correlation (less than \u0026minus;\u0026thinsp;0.757) was identified between parameters A and C (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The negative correlation between parameters A and C suggests that early-maturing animals tend to achieve smaller mature weights. Indeed, several studies (Sarmento et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Malhado et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) have estimated this negative correlation between the parameters A and C, noting that more precocious animals are less likely to attain higher weights in adulthood. Lambe et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) reported that altering the growth curve pattern through selective breeding is feasible, as it can enhance early growth while limiting mature size.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelations between nonlinear growth curve model parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eGompertz\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eBertalanffy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003eLogistic\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreeds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEşme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e,688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-,978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e,688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-,560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-,978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-,560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKarya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.798\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.378\u003c/p\u003e\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-0.798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKıvırcık\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.757\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.433\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.25\u003c/p\u003e\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-0.757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eAge and Weight at Point of Inflection\u003c/h2\u003e\u003cp\u003eThe rate of increase in body weight rises until a certain age and then begins to decline. The point at which this rate changes is referred to as the inflection point. The average ages at the inflection points for the Eşme, Karya and Kıvırcık sheep breeds was 61, 55, and 48 days, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). At these ages, the average live weights of the same breeds were found to be 19, 17, and 14 kg, respectively. The logistic model yielded the highest average inflection point age at 66 days, while Bertalanffy model produced the lowest average at 47 days. The Gompertz model had values that fell between the other two models, with an average of 54 days.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimated age and weight at point of inflection from Gompertz, Logistic and Bertalanffy models using individual body weights\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\"\u003e\u003cp\u003eBreeds\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eGompertz\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eBertalanffy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eLogistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT\u003csub\u003epoi\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBW\u003csub\u003epoi\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT\u003csub\u003epoi\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBW\u003csub\u003epoi\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003epoi\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBW\u003csub\u003epoi\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eT\u003csub\u003epoi\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eBW\u003csub\u003epoi\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEşme\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeneral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e71\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e19\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBT 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBT 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBT 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBT 4+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultiplier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKarya\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeneral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBT 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBT 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBT 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBT 4+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultiplier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKıvırcık\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeneral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e46\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBT 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBT 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBT 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBT 4+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultiplier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e54\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e16\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e47\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e15\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e66\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e19\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e56\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003e17\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eBT: Birth type; Tpoi: age at inflection (day); BWpoi body weight (kg) at point of inflection representing the end of the growth acceleration phase and the beginning of the deceleration phase.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe estimated ages of female at the inflection point were lower than those of males for all models and breeds. It was determined that females and base herds entered the growth acceleration phase and transitioned through the deceleration phase earlier than males and individuals in the multiplier herd.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eSlaughter Age and Weight\u003c/h2\u003e\u003cp\u003eConsidering the best-fit model, the logistic model for Eşme and Karya sheep breeds and the Gompertz model for Kıvırcık sheep breed, it was determined that the slaughter age and weight were 67 days and 21.50 kg for Eşme sheep breed; 62 days and 20.35 kg for Karya sheep breed; and 78 days and 21.79 kg for Kıvırcık sheep breed, based on mean body weight (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). However, slaughter ages determined using individual body weights from the same models were found to be longer in Eşme (84 days) and Karya sheep (76 days) breeds, but shorter for Kıvırcık sheep breed (73 days). Consequently, live weights also varied accordingly (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimated slaughter age (day) and weight, daily gain (kg/day) and average daily gain (kg/day) at slaughter age estimated from the non-linear models using individual weights or mean daily weights\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\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eEşme\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eKarya\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eKıvırcık\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCriteria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndividual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndividual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIndividual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGompertz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDaily gain (kg/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.230\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.220\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage gain (kg/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.231\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.226\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlaughter age (day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlaughter weight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e20.77\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e21.79\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBertalanffy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDaily gain (kg/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage gain (kg/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlaughter age (day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlaughter weight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLogistic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDaily gain (kg/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.261\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.270\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.254\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.300\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage gain (kg/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.262\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.277\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.256\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.308\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlaughter age (day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e84\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e67\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e76\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlaughter weight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e25.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e21.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e23.36\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e20.35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eDaily gain\u0026thinsp;=\u0026thinsp;BWt \u0026ndash; BWt-1; Average daily gain = (BWt \u0026ndash; BW0)/t; t is age in days.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eKaraca et al.(2021a) reported that Eşme lambs were weaned and marketed at an average age of 92.53 days, which is approximately 3 months. Their average live weight was recorded 27.70 kg. For Karya lambs, the reported slaughter age ranged from approximately 90\u0026ndash;120 days, with slaughter weights varying between 26.67 and 30.97 kg (Karaca et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Similarly, for Kıvırcık lambs, the slaughter age was also reported to be between 90 and120 days, weights ranging from 27.7 to 28.09 kg (Karaca et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It was determined that the slaughter age and weight values estimated using the Bertalanffy model were more closely aligned with the values reported in the literature, whereas the values obtained from other models predicted lower slaughter ages and weights compared to field applications. Furthermore, it is believed that the longer duration animals are kept in field applications as opposed to the slaughter ages determined by the models in this study, is due to the practice of waiting until a certain slaughter age (approximately 3 to 4 months) without conducting an economic analysis of body weight changes. It is known that Kıvırcık sheep breed raised in the Marmara, North Aegean and western parts of Central Anatolia are typically sent to slaughter as suckling lambs at a wide range of approximately 3.5 to 5 months of age (Alarslan and Ayg\u0026uuml;n \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The differences in slaughter live weight can be attributed to variations in care and feeding practices within the farms, as well as differences in slaughter age.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eLinear Model Parameters\u003c/h2\u003e\u003cp\u003eThe relationship between age and body weight was also examined using linear models that incorporated individual body weights as well as mean body weights throughout the lifespan. The results are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e and \u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e, respectively.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of age-related changes in individual body weights using linear models.\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\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSimple Linear\u003c/p\u003e\u003cp\u003e(Y=\u0026szlig;\u003csub\u003e0\u003c/sub\u003e+ \u0026szlig;\u003csub\u003e1\u003c/sub\u003e*X)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eQuadratic\u003c/p\u003e\u003cp\u003e(Y= \u0026szlig;\u003csub\u003e0\u003c/sub\u003e+ \u0026szlig;\u003csub\u003e1\u003c/sub\u003e*X+ \u0026szlig;\u003csub\u003e2\u003c/sub\u003e*X\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003eCubic\u003c/p\u003e\u003cp\u003e(Y= \u0026szlig;\u003csub\u003e0\u003c/sub\u003e+ \u0026szlig;\u003csub\u003e1\u003c/sub\u003e*X+ \u0026szlig;\u003csub\u003e2\u003c/sub\u003e*X\u003csup\u003e2\u003c/sup\u003e+ \u0026szlig;\u003csub\u003e3\u003c/sub\u003e*X\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreeds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEşme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.337\u003c/p\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003cp\u003e0.000252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.178\u003c/p\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.000263\u003c/p\u003e\u003cp\u003e0.000007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.283\u003c/p\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003cp\u003e0.00004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.000008\u003c/p\u003e\u003cp\u003e0.00000016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKarya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.188\u003c/p\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003cp\u003e0.000166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.885\u003c/p\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.000425\u003c/p\u003e\u003cp\u003e0.000005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.151\u003c/p\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003cp\u003e0.000023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.000014\u003c/p\u003e\u003cp\u003e0.0000001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKıvırcık\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.286\u003c/p\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003cp\u003e0.000257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.813\u003c/p\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003cp\u003e0.000006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.901\u003c/p\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.000396\u003c/p\u003e\u003cp\u003e0.000032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.000004\u003c/p\u003e\u003cp\u003e0.0000001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eY\u0026thinsp;=\u0026thinsp;Body weight; X\u0026thinsp;=\u0026thinsp;Age (day)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults from linear models using mean body weights (Y) and age in day (X) relations\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\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSimple Linear\u003c/p\u003e\u003cp\u003e(Y=\u0026szlig;\u003csub\u003e0\u003c/sub\u003e+ \u0026szlig;\u003csub\u003e1\u003c/sub\u003e*X)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eQuadratic\u003c/p\u003e\u003cp\u003e(Y= \u0026szlig;\u003csub\u003e0\u003c/sub\u003e+ \u0026szlig;\u003csub\u003e1\u003c/sub\u003e*X+ \u0026szlig;\u003csub\u003e2\u003c/sub\u003e*X\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003eCubic\u003c/p\u003e\u003cp\u003e(Y= \u0026szlig;\u003csub\u003e0\u003c/sub\u003e+ \u0026szlig;\u003csub\u003e1\u003c/sub\u003e*X+ \u0026szlig;\u003csub\u003e2\u003c/sub\u003e*X\u003csup\u003e2\u003c/sup\u003e+ \u0026szlig;\u003csub\u003e3\u003c/sub\u003e*X\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreeds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026szlig;\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEşme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.899\u003c/p\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.209NS\u003c/p\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.384\u003c/p\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0009\u003c/p\u003e\u003cp\u003e0.000056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.513\u003c/p\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00177\u003c/p\u003e\u003cp\u003e0.000309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.000008\u003c/p\u003e\u003cp\u003e0.00000096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKarya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.546\u003c/p\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.249\u003c/p\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.446\u003c/p\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.00128\u003c/p\u003e\u003cp\u003e0.000047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.740\u003c/p\u003e\u003cp\u003e0.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.001749\u003c/p\u003e\u003cp\u003e0.000212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.000010\u003c/p\u003e\u003cp\u003e0.00000066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKıvırcık\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.224\u003c/p\u003e\u003cp\u003e0.337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.783\u003c/p\u003e\u003cp\u003e0.337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.00064\u003c/p\u003e\u003cp\u003e0.000033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.881\u003c/p\u003e\u003cp\u003e0.539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.000594\u003c/p\u003e\u003cp\u003e0.000215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.00000016NS\u003c/p\u003e\u003cp\u003e0.00000067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eY\u0026thinsp;=\u0026thinsp;Body weight; X\u0026thinsp;=\u0026thinsp;Age (day); NS is not significant\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe parameter \u0026szlig;0, estimated using linear models represents the initial value, specifically, the birth weight. Birth weight is a strong indicator of animals that are likely to achieve better weights in adulthood. The actual mean birth weights for Eşme, Karya, and Kıvırcık sheep breeds are 4.27, 4.13, and 3.94 kg, respectively. The Cubic model utilizing individual data provided the most accurate estimate of birth weight among linear models (Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe estimated birth weights were 4.34, 4.18, and 4.29 kg, derived from the simple linear model using individual body weights (Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). The largest discrepancy, at 9%, between the actual birth weights and those estimated by the simple linear model occurred in the Kıvırcık sheep breed. However, when mean body weights were utilized, the birth weights estimated by the simple linear model significantly deviated from the actual values, being nearly double the estimates (Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the estimations conducted using the Quadratic model with individual data, birth weights were estimated to be 2% to 6% lower (Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). However, when the same model was applied to average body weights, the degree of deviation increased significantly ranging from 55% to 154%, with estimates approaching zero (Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn both data sets, the cubic model exhibited the least deviation in birth weight compared to the linear models. When individual data was utilized, the deviation percentage from actual birth weights was approximately 1%. However, this deviation increased when average weights were employed. The level of deviation was positive in Eşme sheep breed (29%), it was negative in Karya sheep breed (-9%) and Kıvırcık sheep breed (-52%). The confidence interval for the estimated birth weights from Cubic model based on individual data encompasses the actual birth weights of the breeds in question.\u003c/p\u003e\u003cp\u003eIn response to one-day increase in age, body weights increased by an average of 176, 378 and 207 grams in the Simple linear, Quadratic, and Cubic models, respectively using mean body weight data (Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). While no difference was observed between breeds regarding the parameter B in the Simple linear model, all breeds were found to differ from one another in the Quadratic model. In the Cubic model, a significant difference was identified between the Kıvırcık and the other sheep breeds. The daily live weight gains estimated for the Kıvırcık, Karya and Eşme sheep breeds from the Cubic model using mean body weights were 300, 176, and 144 g, respectively. These values determined for Kıvırcık and Karya sheep breeds align with those reported by Altın et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), which were 250 g and 182 g, respectively. Consequently, Kıvırcık sheep breed is positioned to compete effectively with many breeds in daily live weight gain. An examination of the live weight change graph of the breeds (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) reveals that the Kıvırcık sheep breed continues to increase, particularly after 120 days, demonstrating a growth pattern distinct from the other two sheep breeds.\u003c/p\u003e\u003cp\u003eA significant portion of the time-dependent growth change in sheep can be explained by a simple linear model. Indeed, Kocabaş et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) demonstrated that body weight growth in sheep follows a straightforward linear pattern during the initial phase of life. A similar finding was reported in another study (Kozakli et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) that examined the first four months of growth in Akkaraman lambs. Additionally, Akbaş et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) indicated that the simple linear model was quite adequate for explaining the growth of Kıvırcık and Dağlı\u0026ccedil; sheep breeds up to 420th day of life.\u003c/p\u003e\u003cp\u003eHowever, the direction of growth can change with age due to environmental and genetic factors. Understanding and managing the course of this change is crucial, which is many researchers have focused on growth curves. It is important to note that the timing of body weight measurements significantly influences the appropriateness of the model used. The fastest phase of growth, typically observed in young animals, is often assumed to be linear (Lambe et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Kozakli et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), as it was the case in this study during the first 120 days. However, when considering the entire 200-day duration, the Quadratic and the Cubic models become more prominent due to the curvature observed after 120 days and the transition of the growth curve into a stationary phase.\u003c/p\u003e\u003cp\u003eTo accurately estimate the growth curves of sheep breeds, the dataset must contain a sufficient number of observations. Kozaklı et al. (2022) discussed the results of their study, which was conducted with data from 109 individuals, and stated that further research is necessary to generalize the findings to the Akkaraman breed. In studies estimating growth curves, the frequency of measurement and the duration of the measurement period are crucial, in addition to the number of individuals included in the analysis. In this study, the growth curves of the three breeds were estimated and compared using a substantial amount of data on the first 200-day live weights, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study growth curve was estimated at the population level, considering daily live weights of 124 657, 405 980, and 111 528 lambs from birth to the 200th day of age in the Eşme, Karya and Kıvırcık sheep breeds raised under breeder\u0026rsquo; conditions. Estimating growth curves at the population level, rather than on an individual basis, using large datasets can be a viable alternative for determining and comparing the growth curves of different breeds. This approach is particularly beneficial because obtaining weights of the same individual over time is quite challenging in large breeder herds and requires significant labor, time, and financial resources. Additionally, animal movements and deaths due to slaughter or the sale of breeding stocks to other herds can negatively impact this process over time. Consequently, most studies on growth curves in sheep have been conducted in small research herds. In the current study, the fitting performances of nonlinear and linear growth curve models were comparable in terms of goodness-of-fit criteria. These results indicate that the application of all models was appropriate for predicting the growth curves of the Eşme, Karya, and Kıvırcık sheep breeds. However, when the models were ranked according to the fitting criteria, it was determined that the most suitable model for Eşme and Karya sheep breeds was the Logistic model, while the Gompertz model was found to be the best fit for the Kıvırcık sheep breed. Among the linear models, the Cubic model emerged as the most effective across all breeds.\u003c/p\u003e\u003cp\u003eWhen sheep breeds were compared based on adult weight estimated from the models that provided the best fit, Eşme exhibited a potential weight of 42.1 kg, Karya 39.6 kg, and Kıvırcık 38.4 kg. While Karya was the fastest breed to reach this adult weight, the Kıvırcık breed demonstrated the slowest maturation.\u003c/p\u003e\u003cp\u003eWhen selecting a model for growth curve, emphasis should be placed on the structure of the data, the ease of estimation, and biological interpretability of the model parameters. In the nonlinear models considered in this study, parameter A estimates adult weight, while parameter C provides information about the maturation rate. Since the correlation between parameters A and C is negative, fast-maturing animals are less likely to achieve high adult weights. This relationship can be a key factor if the trajectory growth is to be altered through breeding. Another factor influencing model selection is the fluctuations in live weight associated with age. These fluctuations can arise from the physiological differences among individuals, as well as variations in their environments. For instance, factors that induce sudden changes in live weight \u0026mdash; such as the timing of sexual maturity, climate conditions, production levels, diseases, and stress \u0026mdash;can significantly impact the shape of growth curves and, consequently, model selection. In addition to environmental influences that contribute to variation in lamb growth, differences in the genetic backgrounds of breeds can also result in significant discrepancies. Understanding the age-related growth patterns of economically significant livestock can provide valuable opportunities for producers. This knowledge aids in determining the most cost-effective slaughter age, evaluating feeding practices used, identifying any growth deficiencies, and comparing different breeds in terms of growth performance. Additionally, it allows for the introduction of specific curve parameters as selection criteria in breeding programs, which can enhance commercial traits or modify the growth curve\u0026rsquo;s shape. The differences in growth curve patterns among breeds reflect their varying genetic potential. In addition to market expectations, it is essential to utilize growth curves to identify the most economically viable slaughter age. Although the slaughter ages estimated by the models were lower than those observed in field applications for these breeds, the economic slaughter age for Kıvırcık was found to be later than that of the other two breeds. Instead of averaging body weights for each age group, estimating the growth curve at the population level based on individual body weights across various ages allows for a more accurate estimation of parameters, as it takes individual variability into account. This approach is both important and practical for providing the general growth curve of the material being studied. Furthermore, if body weights are measured at specific age intervals throughout the lifespan of individuals in the population, it becomes possible to estimate individual growth curves. This information can then be utilized to modify growth trajectories in a targeted manner by using the estimated growth curve parameters as criteria in breeding studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to the General Directorate of Agricultural Research and Policies for providing financial support. We would also like to extend our appreciation to the breeders who participated in the Kıvırcık Sheep Breeding Project (Project No: TAGEM/09KIV2012-01), the Eşme Sheep Breeding Project (Project No: 64ESM2011-01), and the Karya Sheep Development Projects (Project Nos: 20KAR2006-01, 20KAR2011-02, and 09KAR2006-01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYA and OY: Conceptualization, data preparation and writing-original draft. OY, İC, NA and OK: Funding acquisition, Project administration, data collection. YA: Methodology and statistical analysis. YA, OY, İC, NA and OK: Writing-review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e The data that support the findings of this study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbegaz S, Van Wyk JB, Olivier JJ (2010) Estimation of genetic and phenotypic parameters of growth curve and their relationship with early growth and productivity in Horro sheep. Arch Tierzucht 53:85-94.\u003c/li\u003e\n\u003cli\u003eAkbaş Y (1996) Büyüme eğrisi parametreleri ve ıslah kriteri olarak kullanımı olanakları. Ege Üniv. Ziraat Fakültesi Dergisi 33:241-248.\u003c/li\u003e\n\u003cli\u003eAkbaş Y, Taşkın T, Demirören E (1999) Farklı modellerin Kıvırcık ve Dağlıç erkek kuzularının büyüme eğrilerine uyumunun karşılaştırılması. Turkish Journal of Veterinary and Animal Science 23:537-544.\u003c/li\u003e\n\u003cli\u003eAlarslan E, Ayg\u0026uuml;n T (2019) Determination of growth and some morphological traits of Kıvırcık lambs in Yalova. Journal of Animal Production 60:39-50.\u003c/li\u003e\n\u003cli\u003eAli A, Javed K, Zahoor Anjum KM (2020) Determination of the best non-linear function to describe the growth of Kajli sheep. S Afr J Anim Sci 50:452-459.\u003c/li\u003e\n\u003cli\u003eAltın T, Karaca O, Cemal İ, Yılmaz M, Yılmaz O (2005). Kıvırcık ve Karya kuzularda besi ve karkas \u0026ouml;zellikleri. Hayvansal \u0026Uuml;retim 46:19-29.\u003c/li\u003e\n\u003cli\u003eAnonymous (2025) https://www.koyuncuk.com/dokumanlar-2/koyun-irklari/yerli-koyun-irklari/esme-koyunu/.\u003c/li\u003e\n\u003cli\u003eAytekin I, Karabacak A, Zülkadir U, Keskin I, Boztepe S (2009) Usage of some models for describing the growth curves of Akkaraman and Anatolia Merino lambs raised in open and closed sheepfolds at the fattening period. Selçuk Journal of Agriculture and Food Sciences 23: 30-35.\u003c/li\u003e\n\u003cli\u003eAytekin I, Zülkadir U, Keskin I, Boztepe S (2010) Fitting of different mathematic models to the growth curves of female Malya lambs weaned at two different live weights. Trends in Animal and Veterinary Sciences 1:19-23.\u003c/li\u003e\n\u003cli\u003eAytekin RG, Z\u0026uuml;lkadir U (2013) The Determination of Growth Curve Models in Malya Sheep from Weaning to Two Years of Age. J Agr Sci 19: 71-78.\u003c/li\u003e\n\u003cli\u003eBangar YC, Lawar VS, Nimase RG, Nimbalkar CA (2018) Comparison of non-linear growth models to describe the growth behaviour of Deccani sheep. Agr Res 7: 490-494.\u003c/li\u003e\n\u003cli\u003eBehzadi MRB, Aslaminejad AA, Sharifi AR, Simianer H (2014) Comparison of mathematical models for describing the growth of Baluchi sheep. J Agr Sci Tech-Iran 16: 57-68.\u003c/li\u003e\n\u003cli\u003eBilgin OC, Emsen E, Davis ME (2004) Comparison of non-linear models for describing the growth of scrotal circumference in Awassi male lambs. Small Ruminant Res 52: 155-160.\u003c/li\u003e\n\u003cli\u003eBilgin \u0026Ouml;C, Esenbuga N (2003) Parameter estimation in nonlinear growth models. Hayvansal Üretim 44:81-90.\u003c/li\u003e\n\u003cli\u003eBoujenane I (2022) Comparison of nonlinear models for describing the pre-weaning growth of Timahdite lambs and genetic and non-genetic effects for curve parameters. Small Ruminant Res 216:106800.\u003c/li\u003e\n\u003cli\u003eBrunner N, K\u0026uuml;hleitner M (2020) The growth of domestic goats and sheep: A meta study with Bertalanffy-Putter models. Vet Anim Sci 10:100135.\u003c/li\u003e\n\u003cli\u003eCemal I, Ata N, Yılmaz O, Karaca O (2018) Milk production abilities of Kivircik ewes. YY\u0026Uuml; Tarım Bilimleri Dergisi 28: 384-388.\u003c/li\u003e\n\u003cli\u003e\u0026Ccedil;elikeloğlu K, Tekerli M (2014) A comparison of goodness of fit for different growth curve models to body measurements of Pirlak lambs. Lalahan Hayvancılık Araştırma Enstit\u0026uuml;s\u0026uuml; Dergisi 54: 63-69.\u003c/li\u003e\n\u003cli\u003eda Silva LSA, Fraga AB, da Silva FD, Beelen PMG, Silva RMD, Tonhati H, Barros CD (2012) Growth curve in Santa Ines sheep. Small Ruminant Res 105:182-185.\u003c/li\u003e\n\u003cli\u003eDaskiran I, Koncagul S, Bingol M (2010) Growth characteristics of indigenous Norduz female and male lambs. J Agr Sci-Tarim Bili 16:62-69.\u003c/li\u003e\n\u003cli\u003eDehsahraei AR, Ghaderi-Zefrehei M, Rafeie F, Zakizadeh S, Shamsabadi JS, Torshizi ME, Neysi S, Rahmatalla SA (2023) Genetic analysis of growth curve in Moghani Sheep using Bayesian and restricted maximum likelihood. J Anim Sci 101:skad203.\u003c/li\u003e\n\u003cli\u003eDeribe B, Tesema Z, Lakew M, Zegeye A, Kefale A, Shibesh M, Yizengaw L, Belayneh N (2023) Growth and growth curve analysis in Dorper \u0026times;Tumele crossbred sheep under a smallholder management system. Translational Animal Science 7: txad034.\u003c/li\u003e\n\u003cli\u003eDom\u0026iacute;nguez-Viveros J, Canul-Santos E, Rodr\u0026iacute;guez-Almeida FA, Burrola-Barraza ME, Ortega-Guti\u0026eacute;rrez J\u0026Aacute;, Castillo-Rangel F (2019) Defining growth curves with nonlinear models in seven sheep breeds in Mexico. Revista mexicana de ciencias pecuarias 10:664-675.\u003c/li\u003e\n\u003cli\u003eEsenbuga N, Bilgin OC, Macit M, Karaoglu M (2000) İvesi, Morkaraman ve Tuj kuzularında büyüme eğrileri. Atatürk Üniversitesi Ziraat Fak\u0026uuml;ltesi Dergisi 31(1):37-41.\u003c/li\u003e\n\u003cli\u003eEyduran E, K\u0026uuml;c\u0026uuml;k M, Karakuş K, \u0026Ouml;zdemir T (2008) New approaches to determination of the best nonlinear function describing growth at early phases of Kıvırcık and Morkaraman breeds. Journal of Animal and Veterinary Advances 7: 799-804.\u003c/li\u003e\n\u003cli\u003eFitzhugh JH (1976) Analysis of growth curves and strategies for altering their shape. J Anim Sci 42, 1036-1051.\u003c/li\u003e\n\u003cli\u003eForoutanifar S, Khaldari M (2024) The comparisons of non‐linear models to describe the growth performance of Lori‐Bakhtiari sheep. Veterinary Medicine and Science 10: e1527.\u003c/li\u003e\n\u003cli\u003eGbangboche AB, Glele-Kakai R, Salifou S, Albuquerque LG, Leroy PL (2008) Comparison of non-linear growth models to describe the growth curve in West African Dwarf sheep. Animal 2: 1003-1012.\u003c/li\u003e\n\u003cli\u003eGoliomytis M, Orfanos S, Panopoulou E, Rogdakis E (2006) Growth curves for body weight and carcass components, and carcass composition of the Karagouniko sheep, from birth to 720d of age. Small Ruminant Res 66: 222-229.\u003c/li\u003e\n\u003cli\u003eHojjati F, Hossein-Zadeh NG (2018) Comparison of non-linear growth models to describe the growth curve of Mehraban sheep. J Appl Anim Res 46: 499-504.\u003c/li\u003e\n\u003cli\u003eHossein-Zadeh NG (2017) Modelling growth curve in Moghani sheep: comparison of non-linear mixed growth models and estimation of genetic relationship between growth curve parameters. J Agr Sci-Cambridge 155: 1150-1159.\u003c/li\u003e\n\u003cli\u003eHossein-Zadeh NG (2015) Modeling the growth curve of Iranian Shall sheep using non-linear growth models. Small Ruminant Res 130: 60-66.\u003c/li\u003e\n\u003cli\u003eHossein-Zadeh NG, Golshani M (2016) Comparison of non-linear models to describe growth of Iranian Guilan sheep. Rev Colomb Cienc Pec 29: 199-209.\u003c/li\u003e\n\u003cli\u003eKaraca O, Ata N, Canaz K, Cemal İ, Yilmaz O (2024) Investigation of morphological variations in Eşme and Pırlak sheep raised in breeder\u0026rsquo;s conditions. Journal of Animal Production 65: 9-17.\u003c/li\u003e\n\u003cli\u003eKaraca O, Cemal İ, Yılmaz O (2012) National Genetic Improvement Project for Small Ruminants at Breeders\u0026apos; Conditions Projects Aydın-Denizli-Uşak Province Sub-Projects Workshop Notes. www.researchgate.net. In: Karaca, O. (Ed.), Aydın.\u003c/li\u003e\n\u003cli\u003eKaraca O, Cemal İ, Yılmaz O, Ata N (2018) Halk Elinde Hayvan Islahi \u0026Uuml;lkesel Projesi \u0026ldquo;Aydın Kıvırcık Koyunu Islahı\u0026rdquo; Alt Projesi . Birinci 5 Yıllık D\u0026ouml;nem Ara Sonu\u0026ccedil; Raporu.\u003c/li\u003e\n\u003cli\u003eKaraca O, Cemal İ, Yılmaz O, Ata N (2021a) Halk Elinde Hayvan Islahi \u0026Uuml;lkesel Projesi \u0026ldquo;Eşme Koyunu Islahı\u0026rdquo; Alt Projesi. İkinci 5 Yıllık D\u0026ouml;nem Ara Sonu\u0026ccedil; Raporu.\u003c/li\u003e\n\u003cli\u003eKaraca O, Cemal İ, Yılmaz O, Ata N (2021b) Halk Elinde Hayvan Islahi \u0026Uuml;lkesel Projesi \u0026ldquo;Karya Koyunu Islahı\u0026rdquo; Alt Projeleri, \u0026Uuml;\u0026ccedil;\u0026uuml;nc\u0026uuml; 5 Yıllık D\u0026ouml;nem Ara Sonu\u0026ccedil; Raporu.\u003c/li\u003e\n\u003cli\u003eKaraca O, Cemal İ, Yılmaz O, Yılmaz M (2009) Karya koyunu. T\u0026uuml;rkiye Ulusal Koyunculuk Kongresi. Ege Universitesi, İzmir, pp. 225-234.\u003c/li\u003e\n\u003cli\u003eKarakus K, Eyduran E, Kum D, Ozdemir T, Cengiz F (2008) Determination of the best growth curve and measurement interval in Norduz male lambs. Journal of Animal and Veterinary Advances 7, 1464-1466.\u003c/li\u003e\n\u003cli\u003eKaymak\u0026ccedil;ı M, Oğuz I, \u0026Uuml;n C, Bilgen G, Taşkın T (2001) Basic characteristics of some Turkish indigenous sheep breeds. Pakistan Journal of Biological Sciences 47: 916-919.\u003c/li\u003e\n\u003cli\u003eKeskin I, Dag B, Sariyel V, Gokmen M (2009) Estimation of growth curve parameters in Konya Merino sheep. S Afr J Anim Sci 39: 163-168.\u003c/li\u003e\n\u003cli\u003eKocabaş Z, Kesici T, Eliçin A (1997) Akkaraman, İvesi x Akkaraman ve Malya x Akkaraman kuzularında büyüme eğrisi. Turkish Journal of Veterinary and Animal Science 21: 267-275.\u003c/li\u003e\n\u003cli\u003eKopuzlu S, Sezgin E, Esenbuga N, Bilgin OC (2014) Estimation of growth curve characteristics of Hemsin male and female sheep. J Appl Anim Res 42: 228-232.\u003c/li\u003e\n\u003cli\u003eKozakli O, Ceyhan A, Firat MZ (2022) Modeling of growth curve models according to sex in Akkaraman lambs with different methods: Logistic and Gompertz modeling example. Journal Of Agriculture and Nature 25:916-926.\u003c/li\u003e\n\u003cli\u003eKum D, Karakus K, Ozdemir T (2010) The best non- linear function for body weight at early phase of Norduz female lambs. Trakia Journal of Science 8:62-67.\u003c/li\u003e\n\u003cli\u003eLaird AK, Tyler SA, Barton AD (1965) Dynamics of normal growth. Growth 29:233-248.\u003c/li\u003e\n\u003cli\u003eLambe NR, Navajas EA, Simm G, Bünger L (2006) A genetic investigation of various growth models to describe growth of lambs of two contrasting breeds. J Anim Sci 84:2462-2654.\u003c/li\u003e\n\u003cli\u003eLewis R, Brotherstone S (2002) A genetic evaluation of growth in sheep using random regression techniques. Anim Sci 74:63-70.\u003c/li\u003e\n\u003cli\u003eLupi TM, Nogales S, León JM, Barba C, Delgado JV (2015) Characterization of commercial and biological growth curves in the Segureña sheep breed. Anim Biotechnol 9:1341-1348.\u003c/li\u003e\n\u003cli\u003eMakgopa LF, Mathapo MC, Tyasi TL (2024) A systematic review of estimation of growth curve in goats. Trop Anim Health Pro 56:14.\u003c/li\u003e\n\u003cli\u003eMalhado CHM, Carneiro PLS, Alfonso PRAM, Souza JAAO, Sarmento JLR (2009) Growth curves in Dorper sheep crossed with the local Brazilian breeds, Morada Nova, Rabo Largo and Santa Ines. Small Ruminant Res 84:16-21.\u003c/li\u003e\n\u003cli\u003eMasari HAP, Hafezian SH, Mokhtari M, Mianji GR, Abdollahi-Arpanahi R (2021) Inferring causal relationships among growth curve traits of Lori-Bakhtiari sheep using structural equation models. Small Ruminant Res 203:106489.\u003c/li\u003e\n\u003cli\u003eNasholm A, Danell O (1996) Genetic relationships of lamb weight, maternal ability, and mature ewe weight in Swedish Finewool sheep. J Anim Sci 74:329-339.\u003c/li\u003e\n\u003cli\u003eNelder JJB (1961) The fitting of a generalization of the logistic curve. Biometrics 71:89-110.\u003c/li\u003e\n\u003cli\u003eOwens FN, Dubeski P, Hanson CF (1993) Factors that alter the growth and development of ruminants. J Anim Sci 71:3138-3150.\u003c/li\u003e\n\u003cli\u003eOzturk N, Kecici PD, Serva L, Ekiz B, Magrin L (2023) Comparison of nonlinear growth models to estimate growth curves in Kivircik sheep under a semi-intensive production system. Animals-Basel 13:2379.\u003c/li\u003e\n\u003cli\u003e\u0026Ouml;ner Y, \u0026Uuml;st\u0026uuml;ner H, Orman A, Yilmaz O, Yilmaz A (2014) Genetic diversity of Kivircik sheep breed reared in different regions and its relationship with other sheep breeds in Turkey. Ital J Anim Sci 13:3382.\u003c/li\u003e\n\u003cli\u003eSantos NPS, de Oliveira Neto CB, Sarmento JLR, Bezerra LR, Oliveira RL, dos Santos GV, Neto AAR, Biagiotti D (2014) Carcass traits and growth curve parameters in Santa In\u0026ecirc;s sheep. J Agr Sci-Cambridge 6:180-187.\u003c/li\u003e\n\u003cli\u003eSarmento JLR, Regazzi AJ, Sousa WH, Torres RA, Breda FC, Menezes GRO (2006) Estudo de curvas de crescimento de ovinos Santa Inês. Rev Bras Zootecn 35:435-444.\u003c/li\u003e\n\u003cli\u003eTahtali Y, Sahin M, Bayyurt L (2020) Comparison of different growth curve models in Romanov lambs. Kafkas Üniversitesi Veteriner Fakültesi Dergisi 26:609-615.\u003c/li\u003e\n\u003cli\u003eTariq MM, Bajwa MA, Waheed A, Eyduran E, Abbas F, Bokhari A, Akbar A (2011) Growth curve in Menagali sheep breed of Balochistan. Journal of Animal and Plant Sciences 21:5-7.\u003c/li\u003e\n\u003cli\u003eTariq MM, Iqbal F, Eyduran E, Bajwa MA, Huma, ZE, Waheed A (2013) Comparison of non-linear functions to describe the growth in Mengali sheep breed of Balochistan. Pak J Zool 45:661-665.\u003c/li\u003e\n\u003cli\u003eTopal M, Ozdemir M, Aksakal V, Yildiz N, Dogru U (2004) Determination of the best nonlinear function in order to estimate growth in Morkaraman and Awassi lambs. Small Ruminant Res 55:229-232.\u003c/li\u003e\n\u003cli\u003evon Bertalanffy L (1957) Quantitative laws in metabolism and growth. The Quarterly Review of Biology 32:217-231.\u003c/li\u003e\n\u003cli\u003eYıldız G, Soysal MI, Gürcan EK (2009) Tekirdağ ilinde yetiştirilen Karacabey merinosu kıvırcık melezi kuzularda büyüme eğrilerinin farklı modellerle belirlenmesi. Tekirdağ Ziraat Fakültesi Dergisi 6:11-19.\u003c/li\u003e\n\u003cli\u003eYilmaz O, Cemal İ, Karaca O (2013) Estimation of mature live weight using some body measurements in Karya sheep. Trop Anim Health Pro 45:397-403.\u003c/li\u003e\n\u003cli\u003eY\u0026uuml;cel İ (2022) G\u0026ouml;\u0026ccedil;er Karya koyunu yetiştiriciliğinin s\u0026uuml;rd\u0026uuml;r\u0026uuml;lebilirlik değerlendirmesi. Fen Bilimleri Enstit\u0026uuml;s\u0026uuml;. Aydın Adnan Menderes \u0026Uuml;niversitesi, Aydın p. 47.\u003c/li\u003e\n\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":true,"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":"Growth curve parameters, Eşme, Karya, Kıvırcık sheep genotypes, optimum slaughter age","lastPublishedDoi":"10.21203/rs.3.rs-7676389/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7676389/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe aims of this study are (1) to estimate the growth curves of the Eşme, Karya and Kıvırcık sheep breeds from birth to the 200th day of age using the Gompertz, Bertalanffy and Logistic models; (2) to determine how the parameters vary by gender, birth type, tier, and year; (3) to establish the optimum slaughter age for these breeds based on the growth curves; and (4) to compare the growth curve parameters for each breed derived from individual body weights with those obtained from mean body weights at each age point allowing more practical representation of the mean growth curves for the breeds. Both linear (simple linear, quadratic, cubic) and non-linear growth curve models (Gompertz, Bertalanffy and logistic) were fitted and the relationship between body weight and age were investigated. Models were compared using the adjusted coefficient of determination (R2a), mean squared error (MSE), Akaike information criterion (AIC) and Bayesian information criterion (BIC). Estimating population-level growth curves using large datasets is a viable alternative for comparing different breeds. This method is beneficial as tracking individual weights over time in large herds is labor-intensive and costly. The results show that all models were suitable for predicting growth curves of Eşme, Karya, and Kıvırcık sheep breeds. The Logistic model was the best fit for Eşme and Karya breeds, while the Gompertz model was ideal for Kıvırcık sheep. Among linear models, the Cubic model was the most effective for all breeds.\u003c/p\u003e","manuscriptTitle":"Growth Curves and Optimum Lamb Slaughter Ages for Eşme, Karya and Kıvırcık Sheep Breeds","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 21:55:03","doi":"10.21203/rs.3.rs-7676389/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":"efc9ff25-eb9b-479d-8890-0a30f341f0fe","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T17:35:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 21:55:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7676389","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7676389","identity":"rs-7676389","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}