Estimates of genetic parameters for milk yield and reproductive traits in crossbreed dairy cattle at the Holeta Agricultural Research Center, Ethiopia

Estimates of genetic parameters for milk yield and reproductive traits in crossbreed dairy cattle at the Holeta Agricultural Research Center, Ethiopia | 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 Article Estimates of genetic parameters for milk yield and reproductive traits in crossbreed dairy cattle at the Holeta Agricultural Research Center, Ethiopia Asamenew Ayalew, Haile Welearegay, Zewdie Wondatir, Fikadu Wodajo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8865360/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 objective of this study was to estimate genetic parameters for production (milk yield) and reproduction traits in a dairy cattle breed at the Holeta Agricultural Research Center. The analysis utilized extensive records covering 13,116 observations collected over a 30-year period (1995 to 2024). The genetic parameters for milk yield and reproductive traits were estimated using WOMBAT software via multivariate analysis. The heritability estimates for lactation yield traits (LMY, DMY, and LL) were 0.180 ± 1.00, 0.235 ± 0.053, and 0.219 ± 0.077, respectively, and for reproductive traits (AFS, AFC, and CI) 0.0798 ± 0.034, 0.080 ± 0.033, and 0.180 ± 0.042, respectively. The results indicated that repeatability values of lactation yield traits were 0.589 ± 1.00 for LMY, 0.491 ± 0.227 for DMY, 0.735 ± 0.151 for LL, and 0.23 ± 0.01 for CI. The study also found positive direct genetic correlations between lactation yield traits, ranging from very weak (0.141 ± 0.073) to very strong (0.854 ± 0.304) genetic correlations. High correlation was observed between LMY and LL (0.854 ± 0.304). Positive genetic correlations ranging from very weak to weak were found among reproductive traits. AFS-AFC (0.228 ± 0.172),AFS-CI (0.181 ± 0.194), AFC-CI (0.063 ± 0.02). The study indicated that the genetic correlation among lactation yield and reproductive traits was closely related in some traits. Strong genetic correlation was found between CI-LL (0.785 ± 0.074), moderate genetic correlation between CI-LMY and AFC-LL (0.428 ± 0.098, and 0.40 ± 0.107), respectively. The low to moderate heritability estimates suggest that mass selection alone may be slow, and proper management plays a significant role in improving these traits. The favorable genetic correlations found indicate that selection for certain milk yield traits (like LMY or LL) could also lead to a positive correlated response in reproductive efficiency (e.g., shorter CI). Knowing these genetic parameters is crucial for designing effective breeding programs that prioritize traits showing high favorable correlations for overall animal performance improvement. Biological sciences/Genetics Biological sciences/Zoology Crossbred dairy cattle Ethiopia Genetic correlation Genetic parameters Heritability Milk yield Reproductive traits INTRODUCTION Ethiopia is recognized as Africa's leading livestock powerhouse, boasting the largest cattle population on the continent. The cattle population is estimated to consist of about 44 million males and 56 million females. Indigenous breeds dominate the population, accounting for 97.4% of the total cattle population in the country, while hybrid and exotic breeds represent approximately 2.3% and 0.31%, respectively (CSA, 2020/2021). Despite its large herd size, Ethiopia's dairy industry remains underdeveloped compared to other East African nations such as Kenya, Tanzania, and Uganda (Dinka, 2013). Estimating genetic parameters for various livestock traits has been a central theme in animal breeding for the past half-century. Accurate estimation of genetic parameters in tropical herds is crucial for designing and implementing effective breeding strategies (Choudhary et al., 2003). Estimates of these parameters are a fundamental strategic step in launching a breeding program, serving as an essential tool for implementing selective breed improvement within a population (Ayalew et al., 2017). Correct estimation of genetic parameters is, therefore, essential to guarantee the accurate prediction of an individual's genetic merit (Ayalew et al., 2017). However, the effective implementation of selection programs and the overall breeding strategy are often hindered by the absence of comprehensive genetic parameter analyses for key traits such as milk production and reproduction. Genetic improvement of any trait is primarily dictated by the extent of genetic variation available within a population (Zeleke, 2019). The most frequently utilized genetic parameters in breeding programs include heritability, repeatability, and genetic as well as phenotypic correlations (Yacob, 2008). From a genetic perspective, previous studies worldwide have frequently reported an unfavorable negative relationship between increasing milk yield and declining reproductive performance in dairy cattle (Van Dorp et al., 1998). In the Ethiopian context, there is a lack of specific information on the estimates of genetic parameters for productive and reproductive traits of Holstein Friesian-derived synthetic dairy cattle, especially within tropical environments. RESULTS AND DISCUSSION Heritability and Variance Components of Productive Traits Heritability plays a crucial role, alongside other factors, in determining the potential for genetic improvement in any given trait (Haile et al., 2007). In tropical and subtropical regions, environmental factors, diseases, and feed availability have a significant impact on animal performance, resulting in lower heritability estimates (Dechow et al., 2001; Wasike et al., 2006). Variance component heritability (h2), repeatability (r), and permanent environment effects (Vc) of productive traits are presented in Table 3. The current study shows that the heritability values for LMY (0.180 ± 1.00), DMY (0.235 ± 0.053), and LL (0.219 ± 0.077) and repeatability values for LMY (0.589 ± 1.00), DMY (0.491 ± 0.227), and LL (0.735 ± 0.151). The estimated heritability value for lactation milk yield (LMY) was 0.180 ± 1.00. This value is consistent with the findings of Demeke et al. (2004a) for various crossbred breeds in Ethiopia and 0.18. Das et al. (2013) also reported a similar value for Holstein X Sahiwal crossbred cattle. However, the heritability estimate in the present study is lower than that reported by Getahun et al. (2018) for Holstein Friesian × Boran crosses and Gebreyohannes et al. (2013), which were 0.25 ± 1 and 0.30 ± 0.04 for multi-breed cattle, respectively. In contrast, Birhanu et al. (2014) reported a higher value of 0.57 ± 0.02 for Ethiopian Holstein Friesian × Boran crosses. The wide variation among these studies may be due to the type of model used for the analysis and the number of records available. The heritability estimate for DMY in this study was 0.24 ± 0.05, which is similar to the findings of Getahun et al. (2018) at 0.28 ± 0.05 for Holstein Friesian × Boran crosses in the central highlands of Ethiopia and Gebreyohannes et al. (2014) at 0.26 ± 0.08 for various crossbreds. In contrast, lower estimates were reported by Demeke et al. (2004a) at 0.19 ± 0.03 for various crossbreds and Beneberu et al. (2020) at 0.12 ± 0.04 for pure Jersey breeds. Higher values were reported by Birhanu et al. (2014) at 0.52 ± 0.02 for Ethiopian Holstein Friesian × Borana crosses. The heritability estimate of lactation length (LL) was 0.22 ± 0.1, which is consistent with previous findings by Haile et al. (2009a) for Ethiopian Boran × Holstein Friesian (HF) crosses (0.26 ± 0.03) and Birhanu et al. (2014) for the same cross (0.27 ± 0.03). In contrast, a higher heritability value of 0.63 ± 0.02 was reported by Haile et al. (2009a) for HF × local breeds, while lower estimates were documented by Getahun et al. (2018) at 0.12 ± 0.04 for HF × Boran crosses and Beneberu et al. (2020) at 0.04 ± 0.02 for pure Jersey breeds. Estimate the variance components, heritability (h2 ± se) and repeatability (r ± se) for milk production traits from univariate analysis. Traits δ 2 e δ 2 a δ 2 p δ 2 c h 2 R LMY 315455 138225 769134 315454 0.180 ± 1.00 0.589 ± 1.00 DMY 2.727 1.258 5.359 1.374 0.235 ± 0.053 0.491± 0.227 LL 164135 135519 619251 319597 0.219 ± 0.077 0.735± 0.151 δ 2 a = additive variance, δ 2 c = permanent environmental variance, δ 2 e = error variance, δ 2 p = phenotypic variance, h2 =heritability and r = repeatability, LMY=lactation milk yield, DMY=daily milk yield, LL=lactation length. Estimation of heritability for reproductive traits The estimation of variance components, heritability (h2), and repeatability (r) for AFS, AFC, and CI are shown in the table. The current findings have shown that the heritability values of reproductive traits were low. The heritability estimate for AFS was 0.079 ± 0.034, which aligns with the findings of Beneberu et al. (2020), who reported a value of 0.05 ± 0.08 for pure Jersey breeds. This result is notably lower than the estimates provided by Getahun et al. (2018) at 0.22 ± 0.08 for Holstein Friesian × Boran crosses, and Zeleke et al. (2014) at 0.26 for Fogera × Holstein Friesian crosses. In contrast, higher heritability values were documented by Haile et al. (2009b) at 0.61 ± 0.15 for Boran × Holstein Friesian crosses, and by Berhanu and Ashim (2014) at 0.51 ± 0.10 for Ethiopian Boran × Holstein Friesian crosses. The heritability estimate for AFC derived from the univariate analysis was 0.080 ± 0.033. This finding is consistent with the results reported by Beneberu et al . (2020), who documented a heritability estimate of 0.05 ± 0.05 for pure Jersey breeds, indicating a relatively low genetic influence on this trait. However, this estimate is lower than that reported by Yosef et al . (2006), who found a heritability of 0.16 ± 0.06 for Jersey breeds, suggesting a moderate genetic component in that population. In contrast, significantly higher heritability estimates were reported by Haile et al . (2009b) for Ethiopian Boran × Holstein Friesian crosses at 0.7 ± 0.16 and by Gebeyehu et al . (2014) for Holstein breeds at 0.53 ± 0.116. These higher values imply a stronger genetic influence on AFC in these populations, which may be attributed to selective breeding practices and genetic variability within the respective breeds. The heritability estimate for calving interval obtained in the present study was 0.180 ± 0.042. This result is similar to that reported by Tadesse et al. (2014), who found a heritability of 0.16 ± 0.031 for Ethiopian Boran × Holstein Friesian crosses. Additionally, the current estimate is higher than the value reported by Getahun et al. (2018), which was 0.071 ± 0.03 for Holstein Friesian × Boran crosses. Notably, a significantly higher heritability estimate of 0.499 was reported by Ahmed et al. (2007) for Holstein and Jersey crosses with local breeds. It is important to note that the length of the calving interval is influenced by various factors, including the herd's reproductive management practices, which can significantly affect the genetic expression of this trait. These different estimates of heritability may be due to sample size used, genetic group/breed, and analysis methods as suggested by Sendeku et al. (2015). Estimate the variance components, heritability (h2 ± se) and repeatability (r ± se) for milk reproductive traits from univariate analysis. Trait δ 2 e δ 2 a δ 2 p δ 2 c h 2 r AFS 129.01 11.1 139.054 - 0.0798 ±0.034 - AFC 129.65 11.2 139.36 - 0.080 ±0.033 - CI 19007.4 4443.44 24730.0 1279.16 0.180 ± 0.042 0.23±0.01 δ 2 a = additive genetic variance, δ 2 c = permanent environmental variance, δ 2 e = residual variance, δ 2 p = phenotypic variance, h 2 = heritability r = repeatability AFS= age at first service, AFC= age at first calving, CI= calving interval. Estimation of Repeatability (r) for productive Traits The repeatability estimates for productive traits, such as lactation milk yield (LMY), daily milk yield (DMY), and lactation length (LL) were 0.589 ± 1.00, 0.491 ± 0.227, and 0.735 ± 0.151, respectively (Table 4). These high repeatability values indicate that cow performance is a reliable indicator across successive lactations, supporting culling decisions based on individual productivity. The results suggest the presence of substantial additive genetic and permanent environmental variance contributing to trait consistency. The repeatability estimate for LMY in this study is consistent with Getahun et al. (2018), who reported 0.50 ± -1 for Holstein Friesian × Boran crosses, and Ghorbani et al . (2011), who reported 0.505 for Iranian Holstein Friesian crosses. However, it exceeds the 0.33 reported by Beneberu et al . (2020) for pure Jersey breeds and the lower estimate of 0.17 by Haile et al . (2009a) for Holstein Friesian × Boran crosses. The repeatability estimate for daily milk yield (DMY) in this study was 0.46 ± 0.02, consistent with Getahun et al. (2018) for Holstein Friesian × Boran crosses, and higher than the 0.334 reported by Ghorbani et al. (2011) for Iranian Holstein Friesian crosses. However, Demeke et al. (2004b) documented lower repeatability values of 0.30 ± 0.02 for Holstein Friesian × Boran and Jersey × Boran crosses. The repeatability for lactation length observed in this study was approximately 0.70, as reported by Haile et al. (2009a) for Holstein Friesian × Boran crosses. This value was higher than the 0.23 ± 0.02 reported by Getahun et al. (2018) for the same crossbreds. In contrast, Tadesse et al. (2019) reported a notably lower repeatability estimate of 0.050 ± 0.07 for Holstein Friesian × Boran crosses. Regarding reproductive performance, the repeatability estimate for calving interval (CI) in this study was 0.23 ± 0.01. This value is lower than the 0.359 ± 0.06 reported by Tadesse et al . (2019) for Holstein Friesian × Boran crosses but higher than values reported by Beneberu et al . (2020) at 0.09 ± 0.02 for pure Jersey breeds and by Getahun et al . (2018) at 0.17 ± 0.02 for the same crossbreeds. The comparatively low repeatability observed here likely reflects a pronounced impact of transient environmental factors on individual records, thereby increasing within-animal variability and reducing trait consistency across repeated measurements. Genetic and phenotypic correlations Direct genetic and phenotypic correlations for productive traits of lactation milk yield (LMY), daily milk yield (DMY), and lactation length (LL) and reproductive traits (age at first service (AFS), age at first calving (AFC), and calving interval (CI) were estimated using multivariate analysis, as shown in Table below. The results indicated that direct genetic correlations were generally higher than phenotypic correlations for most traits, with some exceptions among reproductive traits. Direct genetic correlations reflect the influence of shared genetic factors, while phenotypic correlations encompass both genetic and environmental effects, as noted by Zeleke et al . (2019). The study found that traits with positive phenotypic correlations, such as CI and DMY, often aligned with genetic correlations, while other traits exhibited negative or antagonistic correlations. Genetic correlations The genetic correlation between productive traits were positive and the coefficients ranged from weak (0.14 ± 0.07) to very strong (0.93 ± 0.03). High correlation observed between LMY and DMY (0.926 ± 0.032). This signifies that the two traits are governed by the same gene. Similar to our finding Beneberu et al (2020) a high correlation coefficient of (0.98±0.07) between LMY and DMY. On the other hand, Tadesse (2014) reported moderate to very strong genetic correlation (0.589, 0.956 and 0.998) between DMY and LL, LMY and DMY and LMY and LL, respectively. However, weak genetic correlation obtained in the work of Das et al . (2013) i.e., 0.31 for LMY and LL and 0.30 for LMY and DM, respectively. Genetic correlation coefficients between reproductive traits in the present were weak but positive. AFS-AFC (0.228 ± 0.172), AFS-CI (0.181 ± 0.194), AFC-CI (0.063 ± 0.02).In agreement with this finding, Belay et a l.(2014) found a perfect positive genetic correlation (1) between AFS and AFC for Fogera cattle crosses. However, higher genetic correlation between reproductive traits was reported by Beneberu et al . (2020) for AFC and CI (0.30±0.61) and AFS and AFC (0.89±0.11) for pure Jersey breed. Strong genetic correlation looked between CI-LL (0.785 ± 0.074), moderate genetic correlation between CI-LMY and AFC-LL (0.428 ± 0.098, and 0.40 ± 0.107), respectively, very weak genetic correlation values were CI-DMY, AFC-LMY and AFS-LMY (0.142 ± 0.073, 0.024 ± 0.001, 0.129 ± 0.056), respectively and finally negative genetic correlation were appeared between AFC-DMY (-0.206 ±0.072), AFS-DMY (-0.196 ± 0.148) and AFS-LL (-0.020 ± 0.078).The negative genetic correlation AFC-DMY (-0.206 ±0.072 and AFS-LL (-0.020 ± 0.078) similar with the report of Getahun et al .(2018) AFC-DMY (-0.55) and AFS-LL ( -0.11). In general, a positive direct genetic correlation between traits in the current study showed that selection of one trait might be a vital for the improvement of other traits. Also, these high genetic correlation results are due to the phenomenon of a single gene affecting more than one trait and due to the occurrence of two or more loci that affect the same trait on the same chromosome Bourdon et al . (2014). Nevertheless, traits which have shown negative direct genetic correlations in the present study indicates that as one trait increases, the other trait tends to decrease which might be favorable or unfavorable depending on the combination of traits considered. Phenotypic correlations The phenotypic correlations estimated for production traits were positive very weak (0.017 ± 0.024) between DMY-LL strong (0.670 ± 0.012) between DMY-LMY and very strong (0.890 ± 0.078) between LMY-LL. The phenotypic correlation between LMY-LL in this study was similar with the report of Beneberu et al . (2018) (0.82±0.01) for pure jersey breed and Tadesse et al . (2014) (0.862) for Boran. The variation of the present study from others might be due to breed, number of observations and analysis methods. The phenotypic correlation among reproductive traits as indicated in the table were positive very weak (0.011 ± 0.026) between AFS-AFC and (0.051 ± 0.055) AFS-CI and negative (0.014 ±0001) between AFC-CI. Similar results was reported by Getahun et al .(2018) negative phenotypic correlation AFS- CI (-0.03) .The present study was positive and negative phenotypic correlation of these traits are strongly disagreed with the finding of Belay (2014) who found very strong phenotypic correlation between AFS and AFC (0.85463).The current study vary from others might be due to breed, number of observation studied and software procedure used for analysis. The phenotypic correlation between productive and reproductive traits was ranged from moderate positive to negative values. The present study lactation length was a negative phenotypic correlation with AFS and AFC. However, positive correlation was showed between AFS-LMY (0.017 ± 0.031), AFS-DMY (0.041 ± 0.032), AFC-LMY (0.002 ±0.037), LL-CI (0.447 ± 0.019), LMY-CI (0.215 ±0.023) and (0.017 ± 0.024).The phenotypic correlation among LL-CI in the current study similar with the report of Beneberu et al. (2020) 0.41±0.02 for pure jersey and higher than the report of Getahun et al . (2018) 0.18 for HF x Boran.The negative value of LL-AFS similar with the finding of Getahun et al . (2018) and Das et al . (2013). Estimates of genetic correlations (below diagonal) and phenotypic correlations (above diagonal) between reproductive and production traits Parameters AFS AFC CI LMY DMY LL AFS 0.011 ± 0.026 0.051±0.055 0.017± 0.031 0.041±0.032 -0.017±0.056 AFC 0.228 ±0.172 -0.014±0001 0.002 ±0.037 -0.122±0.036 -0.0062±0.018 CI 0.181 ±0.194 0.063 ±0.02 0.215 ±0.023 0.017± 0.024 0.447 ± 0.019 LMY 0.129 ±0.056 0.024 ± 0.001 0.428± 0.098 0.670± 0.012 0.890 ± 0.078 DMY -0.196±0.148 -0.206 ±0.072 0.142± 0.073 0.926 ± 0.032 0.017 ± 0.024 LL -0.020±0.078 0.40 ±0.107 0.785± 0.074 0.854 ± 0.304 0.141± 0.073 AFS=Age at First Service, AFC=Age at First Calving, CI=Calving Interval, LMY=Lactation Milk Yield, DMY=Daily Milk Yield, LL=Lactation Length. CONCLUSION The values of heritability and repeatability both productive and reproductive traits were none zero values and ranges from low to higher. Highest heritability estimate value was 0.235 ± 0.053 for DMY and the lowest was 0.180 ± 1.00 for LMY whereas highest LL (0.735± 0.151) and lowest DMY (0.491± 0.227) repeatability values were obtained. However, the current study shows that Low heritability and repeatability indicates that comparatively high environmental variance. The genetic correlations between the traits in the current study were higher than the corresponding phenotypic correlations among all traits. Knowing that all of the productive and reproductive traits in this study have only positive genetic correlations, it is likely that similar genes control them all. This indicates that selecting for one trait will improve other correlated traits in the desired direction, which will aid in the breeding process overall by improving all of the traits that are correlated with one another. The phenotypic correlation between productive traits was ranges from very weak to strong correlation. Strong phenotypic correlation was observed between LL and LMY. However, negative correlation was observed among AFC and LL. Therefore, it is recommended that future studies verify the lower estimates of certain traits by using larger datasets and applying multivariate models for both productive and reproductive traits. MATERIALS AND METHODS Description of the study area The research was conducted at the Holetta Agricultural Research Center (HARC), located in Ethiopia ’ s central highlands, approximately 35 kilometers west of Addis Ababa. The area is situated between 3°24′N and 14°53′N latitude and 33°00′E to 48°00′E longitude, at an elevation of 2,400 meters above sea level. It receives an average annual rainfall of 1,100 mm and has an average temperature of 15°C, with daily minimum and maximum temperatures of 6°C and 24°C, respectively (Gojam et al., 2016). The region experiences an average monthly relative humidity of 60% (Gebreyohanes et al., 2013). Overview of Dairy Cattle Research Farm The Holetta Research Center was established in 1966. Initially, the center focused on evaluating the preliminary characterization, milk production, and reproductive performances of selected indigenous cattle breeds at four experimental stations (Holetta, Horo, Melka-Werer, and Adamitulu). The indigenous breeds produced an average total lactation yield of 550 kg over a 6-month lactation period. However, due to the lower milk yield of indigenous cows and the high demand for milk and milk products driven by rapid human population growth, crossbreeding was proposed in 1972 by G. Winner, a FAO consultant. The first preliminary results of the long-term dairy cattle crossbreeding experiments in Ethiopia were reported in Sendros, (1987), 20 years after the start of the experiment. The results indicated that first generation (F 1 ) crossbred dairy cows in general produce three to five times more milk than indigenous cows. Kebede, (1992) conducted a comprehensive study and identified milk production as one of the breeding program's target goals, achieving significant success. Currently, due to fluctuations in the inheritance of exotic genes among crossbreds produced through breeding and the lack of an appropriate breeding program, efforts are underway to develop a 75% synthetic/composite dairy breed at Holeta Agricultural Research Center (HARC). Animal Management The herds were managed based on their breed group, pregnancy stage, lactation period, sex, and age. Consistent feeding and management protocols were applied to all animals within each specific category. During the day, animals were allowed to graze from early morning until evening. A concentrate mixture made up of wheat bran (54%), noug (Guizotia abyssinica) cake (45%), and salt (1%) was supplemented according to their body weight, productivity, and physiological status. Cows, heifers, and calves were supplemented with the concentrate mixture at rates of 4 kg, 1-1.5 kg, and 0.25-1 kg per day per animal, respectively. Calves were weighed and ear tagged within 24 hours of birth. After four days, they were moved to a calf rearing pen where they were provided with a dry diet and 260 kg of whole milk over 98 days through bucket feeding, except for the F1 calves who suckled their dams until weaning. Weaned calves were then transferred to another pen and kept indoors until they reached 6 months of age. Mating Design The Boran cattle, sourced from Boran pastoralist communities in southern Ethiopia, were used as the foundation stock for crossbreeding. Initially, pure Boran cows were inseminated with pure Holstein Friesian (HF) semen to produce 50% F1 crossbreeds. These F1 animals were then backcrossed with pure Holstein Friesian semen to generate the first-generation 75% Holstein Friesian–25% Boran offspring. The later generations (F2 and F3) were produced by mating 75% (HF X Boran) males with 75% (HF X Boran) females to create a synthetic breed with 75% HF and 25% Boran gene inheritance. Data Source and Data Collection The study utilized data collected over a 30-year period, from 1995 to 2024, at the Holetta Agricultural Research Center (HARC). In total, 13,116 crossbred dairy cattle performance records were used for this study (see Table 1). Milk production Traits Reproduction Traits Genotypes LMY DMY LL CI AFS AFC Total 50% F1 1665 1665 1665 1329 828 828 7980 50% F2 236 236 236 156 158 158 1180 50% F3 142 142 142 84 131 131 772 75% F1 436 436 436 304 515 515 2642 75% F2 85 85 85 35 126 126 542 Total 2564 2564 2564 1908 1758 1758 13,116 LMY=Lactation Milk Yield, DMY=Daily Milk Yield, LL=Lactation Length, CI=Calving Interval, AFS =Age At First Service, AFC=Age At First Calving. Table 2: Number of observations in pedigree records No. Pedigree data N 1 No. of animals with unknown sire 401 2 No. of animals with unknown dam 406 3 No. of animals with both parents unknown 378 4 No. of sires 438 5 No. of animals with paternal grandsire 1031 6 No. of animals with paternal grand dam 1067 Traits to be studied The traits analyzed in this study were classified into two groups: productive and reproductive traits. Productive traits included lactation milk yield (LMY), daily milk yield (DMY), and lactation length (LL). Reproductive traits included age at first service (AFS), age at first calving (AFC), and calving interval (CI). Statistical analysis Genetic Parameter Analysis Variance and covariance components, heritability, repeatability, and genetic correlations were estimated using WOMBAT software. Univariate and multivariate analyses were applied for genetic parameter estimation. The following animal model was applied, Y = Xb + Za + Wd + e. where; Y, is a vector of observations for the traits of interest b, is a vector of fixed effects (genetic group, calving year, calving season and parity). a, is a vector of random individual additive effects d, is a vector of permanent environmental effects X, matrices relating records to fixed effects Z, incidence matrices relating records to individual animal effect W, matrices of permanent environmental effects e, vector of random residual effect The model assumed the expected mean of zero and variances σa 2 , σc 2 and σe 2 , respectively. Pedigree data as the software already recognized the formula as follows; σp 2 = σa 2 + σc 2 +σe 2 σp 2 ; is phenotypic variance (total variance) h 2 = σa 2 /σp 2 σa 2 ; additive genetic variance r 2 = σa 2 +σc 2 /σp 2 σc 2 ; permanent environmental variance Ai = h 2 x P σe 2 ; residual variance Declarations Acknowledgments We would like to express our heartfelt gratitude to the reviewers for their comprehensive, thoughtful, and constructive feedback. Their in-depth recommendations significantly enhanced the clarity, organization, and scientific integrity of our manuscript. We are truly thankful for the time and expertise they contributed to refining our work. Authors ’ contributions AA contributed to design of the study, data analysis and interpretation, drafting and revising the manuscript. HW contributed to conception and design of the study, data collection, data analysis and interpretation and drafting the manuscript. ZW contributed to drafting and revising the manuscript. Funding Declaration The authors declare that no funding was received for this work. Availability of data and materials Not applicable. Ethics approval and consent to participate This study did not require official or institutional ethical approval. Consent for publication Not applicable. Prior publication Data have not been published previously. Competing interests The authors declare that they have no competing interests. Author details: Ethiopian Institute of Agricultural Research, Holeta Agricultural Research Center, P O Box 2003 Addis Ababa or 31 Holeta, Ethiopia. References Ahmed, M.-K., Teirab, A. B., Musa, L.-A., & Peters, K. J. (2007). Milk production and reproduction traits of different grades of zebu x Friesian crossbreds under semi-arid conditions. Archives Animal Breeding, 50 (3), 240-249. Ayalew, W., Aliy, M., & Negussie, E. (2017). Estimation of genetic parameters of the productive and reproductive traits in Ethiopian Holstein using multi-trait models. 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Genotype by environment interaction effect on lactation pattern and milk production traits in an Ethiopian dairy cattle population. Agriculture and Natural Resources, 48 (1), 38-51. Getahun, K., & Hundie, D. (2018). Genetic and non-genetic parameter estimation for productive and reproductive performances of crossbred dairy cattle at Holetta research center. Haramaya university, Ghorbani, A., Ashtiani, S. M., Noubr, S., Shahriar, H., & Nikzad, S. (2011). Estimation of genetic parameter in Iranian Holstein crossbred dairy cattle. Gojam, Y., Tadesse, M., Efffa, K., & Hunde, D. (2016). Performance of crossbred dairy cows suitable for smallholder production systems at Holetta Agricultural Research Centre. Ethiopian Journal of Agricultural Sciences, 27 (1), 121-131. Goshu, G., Singh, H., Petersson, K.-J., & Lundeheim, N. (2014). Heritability and correlation among first lactation traits in Holstein Friesian cows at Holeta Bull Dam Station, Ethiopia. International Journal of Livestock Production, 5 (3), 47-53. Haile, A., Joshi, B., Ayalew, W., Tegegne, A., & Singh, A. (2007). Economic comparison of Ethiopian Boran cattle and their crosses with Holstein Friesian in central Ethiopia. Ethiopian J. Anim. Prod, 7 (1), 77-87. Haile, A., Joshi, B., Ayalew, W., Tegegne, A., & Singh, A. (2009). Genetic evaluation of Ethiopian Boran cattle and their crosses with Holstein Friesian in central Ethiopia: milk production traits. Animal, 3 (4), 486-493. Kebede, B. (1992). Estimation of additive and nonadditive genetic effects for growth, milk yield and reproduction traits of crossbred (Bos taurus x Bos indicus) cattle in the wet and dry environments in Ethiopia : Cornell University. Philipsson, J., Rege, J., Zonabend König, E., & Okeyo Mwai, A. (2011). Sustainable breeding programmes for tropical low-and medium input farming systems. Sendeku, A. T. (2015). Estimation of genetic and non-genetic parameters for growth and reproductive performance traits of Fogera cattle breed. In: LAMBERT Academic Publishing, German. Sendros, D., Beyene, K., Tesfaye, K., Taye, B., & Hailu, G. (1987). Preliminary crossbreeding results of cattle crossbreeding (European x zebu) studies: Milk Production performances of F1 cows. Paper presented at the Proc. of 1st National Livestock Improvement Conference (NLIC). Tadesse, M., Hunde, D., & Galmessa, U. (2019). Breed additive, heterosis and recombination effects on milk production traits from Ethiopian Boran with Holstein Friesian crosses at Holetta Agricultural Research Center. Livestock Research Results , 282-292. Van Dorp, T., Dekkers, J., Martin, S., & Noordhuizen, J. (1998). Genetic parameters of health disorders, and relationships with 305-day milk yield and conformation traits of registered Holstein cows. Journal of Dairy Science, 81 (8), 2264-2270. Wasike, C., Ilatsia, E., Ojango, J., & Kahi, A. (2006). Genetic parameters for weaning weight of Kenyan Boran cattle accounting for direct-maternal genetic covariances. South African Journal of Animal Science, 36 (4), 275-281. Yacob, Y. (2008). Environmental and genetic parameters of growth, reproductive and survival performance of Afar and blackhead Somali sheep at Werer Agricultural Research Centre, Ethiopia. Yosef Tadesse. 2006. Genetic and Non-Genetic analysis of fertility and production traits in Holetta and Ada’a Berga Dairy herds. MSc Thesis, Alemaya University, Alemaya, Ethiopia. Zeleke, B. (2014). Estimation of genetic parameters for growth and reproductive traits of Fogera x Holstein Friesian crossbred cattle at Metekel ranch, Amhara region, Ethiopia. MSc Thesis, Haramaya University, Haramaya, Ethiopia, Zeleke, T. (2019). On-station and on-farm performance evaluation and genetic parameters estimation of Boer x Central Highland crossbred goat in North Wollo Zone, Ethiopia. Additional Declarations No competing interests reported. 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The cattle population is estimated to consist of about 44 million males and 56 million females. Indigenous breeds dominate the population, accounting for 97.4% of the total cattle population in the country, while hybrid and exotic breeds represent approximately 2.3% and 0.31%, respectively (CSA, 2020/2021). Despite its large herd size, Ethiopia's dairy industry remains underdeveloped compared to other East African nations such as Kenya, Tanzania, and Uganda (Dinka, 2013).\u003c/p\u003e\n\u003cp\u003eEstimating genetic parameters for various livestock traits has been a central theme in animal breeding for the past half-century. Accurate estimation of genetic parameters in tropical herds is crucial for designing and implementing effective breeding strategies (Choudhary et al., 2003). Estimates of these parameters are a fundamental strategic step in launching a breeding program, serving as an essential tool for implementing selective breed improvement within a population (Ayalew et al., 2017). Correct estimation of genetic parameters is, therefore, essential to guarantee the accurate prediction of an individual's genetic merit (Ayalew et al., 2017).\u003c/p\u003e\n\u003cp\u003eHowever, the effective implementation of selection programs and the overall breeding strategy are often hindered by the absence of comprehensive genetic parameter analyses for key traits such as milk production and reproduction. Genetic improvement of any trait is primarily dictated by the extent of genetic variation available within a population (Zeleke, 2019). The most frequently utilized genetic parameters in breeding programs include heritability, repeatability, and genetic as well as phenotypic correlations (Yacob, 2008). From a genetic perspective, previous studies worldwide have frequently reported an unfavorable negative relationship between increasing milk yield and declining reproductive performance in dairy cattle (Van Dorp et al., 1998).\u003c/p\u003e\n\u003cp\u003eIn the Ethiopian context, there is a lack of specific information on the estimates of genetic parameters for productive and reproductive traits of Holstein Friesian-derived synthetic dairy cattle, especially within tropical environments.\u003c/p\u003e"},{"header":"RESULTS AND DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003eHeritability and Variance Components of Productive Traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeritability plays a crucial role, alongside other factors, in determining the potential for genetic improvement in any given trait (Haile et al., 2007).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;In tropical and subtropical regions, environmental factors, diseases, and feed availability have a significant impact on animal performance, resulting in lower heritability estimates (Dechow et al., 2001; Wasike et al., 2006).\u003c/p\u003e\n\u003cp id=\"_Toc458174048\"\u003eVariance component heritability (h2), repeatability (r), and permanent environment effects (Vc) of productive traits are presented in Table 3. The current study shows that the heritability values for LMY (0.180 \u0026plusmn; 1.00), DMY (0.235 \u0026plusmn; 0.053), and LL (0.219 \u0026plusmn; 0.077) and repeatability values for LMY (0.589 \u0026plusmn; 1.00), DMY (0.491 \u0026plusmn; 0.227), and LL (0.735 \u0026plusmn; 0.151).\u003c/p\u003e\n\u003cp\u003eThe estimated heritability value for lactation milk yield (LMY) was 0.180 \u0026plusmn; 1.00. This value is consistent with the findings of Demeke et al. (2004a) for various crossbred breeds in Ethiopia and 0.18. Das et al. (2013) also reported a similar value for Holstein X Sahiwal crossbred cattle. However, the heritability estimate in the present study is lower than that reported by Getahun et al. (2018) for Holstein Friesian \u0026times; Boran crosses and Gebreyohannes et al. (2013), which were 0.25 \u0026plusmn; 1 and 0.30 \u0026plusmn; 0.04 for multi-breed cattle, respectively. In contrast, Birhanu et al. (2014) reported a higher value of 0.57 \u0026plusmn; 0.02 for Ethiopian Holstein Friesian \u0026times; Boran crosses. The wide variation among these studies may be due to the type of model used for the analysis and the number of records available.\u003c/p\u003e\n\u003cp\u003eThe heritability estimate for DMY in this study was 0.24 \u0026plusmn; 0.05, which is similar to the findings of Getahun et al. (2018) at 0.28 \u0026plusmn; 0.05 for Holstein Friesian \u0026times; Boran crosses in the central highlands of Ethiopia and Gebreyohannes et al. (2014) at 0.26 \u0026plusmn; 0.08 for various crossbreds. In contrast, lower estimates were reported by Demeke et al. (2004a) at 0.19 \u0026plusmn; 0.03 for various crossbreds and Beneberu et al. (2020) at 0.12 \u0026plusmn; 0.04 for pure Jersey breeds. Higher values were reported by Birhanu et al. (2014) at 0.52 \u0026plusmn; 0.02 for Ethiopian Holstein Friesian \u0026times; Borana crosses.\u003c/p\u003e\n\u003cp\u003eThe heritability estimate of lactation length (LL) was 0.22 \u0026plusmn; 0.1, which is consistent with previous findings by Haile et al. (2009a) for Ethiopian Boran \u0026times; Holstein Friesian (HF) crosses (0.26 \u0026plusmn; 0.03) and Birhanu et al. (2014) for the same cross (0.27 \u0026plusmn; 0.03). In contrast, a higher heritability value of 0.63 \u0026plusmn; 0.02 was reported by Haile et al. (2009a) for HF \u0026times; local breeds, while lower estimates were documented by Getahun et al. (2018) at 0.12 \u0026plusmn; 0.04 for HF \u0026times; Boran crosses and Beneberu et al. (2020) at 0.04 \u0026plusmn; 0.02 for pure Jersey breeds.\u003c/p\u003e\n\u003cp\u003e\u003cspan id=\"_Toc201824366\"\u003e\u003cstrong\u003eEstimate the variance components, heritability (h2 \u0026plusmn; se) and repeatability (r \u0026plusmn; se) for milk production traits from univariate analysis.\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"583\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026delta;\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003ee\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026delta;\u003csup\u003e2\u003c/sup\u003ea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026delta;\u003csup\u003e2\u003c/sup\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026delta;\u003csup\u003e2\u003c/sup\u003ec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eh\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eLMY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e315455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e138225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e769134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e315454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.180 \u0026plusmn; 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0.589 \u0026plusmn; 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eDMY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e5.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.235 \u0026plusmn; \u0026nbsp;0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0.491\u0026plusmn; 0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e164135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e135519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e619251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e319597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.219 \u0026nbsp;\u0026plusmn; 0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0.735\u0026plusmn; 0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026delta;\u003csup\u003e2\u003c/sup\u003ea\u003c/strong\u003e = additive variance, \u003cstrong\u003e\u0026delta;\u003csup\u003e2\u003c/sup\u003ec\u003c/strong\u003e = permanent environmental variance, \u003cstrong\u003e\u0026delta;\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ee\u003c/sub\u003e\u003c/strong\u003e = error variance,\u003cstrong\u003e\u0026nbsp;\u0026delta;\u003csup\u003e2\u003c/sup\u003ep\u003c/strong\u003e = phenotypic variance,\u003cstrong\u003e\u0026nbsp;h2\u003c/strong\u003e=heritability and \u003cstrong\u003er\u003c/strong\u003e= repeatability, LMY=lactation milk yield, DMY=daily milk yield, LL=lactation length.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstimation of heritability for reproductive traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe estimation of variance components, heritability (h2), and repeatability (r) for AFS, AFC, and CI are shown in the table. The current findings have shown that the heritability values of reproductive traits were low.\u003c/p\u003e\n\u003cp\u003eThe heritability estimate for AFS was 0.079 \u0026plusmn; 0.034, which aligns with the findings of Beneberu et al. (2020), who reported a value of 0.05 \u0026plusmn; 0.08 for pure Jersey breeds. This result is notably lower than the estimates provided by Getahun et al. (2018) at 0.22 \u0026plusmn; 0.08 for Holstein Friesian \u0026times; Boran crosses, and Zeleke et al. (2014) at 0.26 for Fogera \u0026times; Holstein Friesian crosses. In contrast, higher heritability values were documented by Haile et al. (2009b) at 0.61 \u0026plusmn; 0.15 for Boran \u0026times; Holstein Friesian crosses, and by Berhanu and Ashim (2014) at 0.51 \u0026plusmn; 0.10 for Ethiopian Boran \u0026times; Holstein Friesian crosses.\u003c/p\u003e\n\u003cp\u003eThe heritability estimate for AFC derived from the univariate analysis was 0.080 \u0026plusmn; 0.033. This finding is consistent with the results reported by Beneberu \u003cem\u003eet al\u003c/em\u003e. (2020), who documented a heritability estimate of 0.05 \u0026plusmn; 0.05 for pure Jersey breeds, indicating a relatively low genetic influence on this trait. However, this estimate is lower than that reported by Yosef \u003cem\u003eet al\u003c/em\u003e. (2006),\u0026nbsp;who found a heritability of 0.16 \u0026plusmn; 0.06 for Jersey breeds, suggesting a moderate genetic component in that population. In contrast, significantly higher heritability estimates were reported by Haile \u003cem\u003eet al\u003c/em\u003e. (2009b) for Ethiopian Boran \u0026times; Holstein Friesian crosses at 0.7 \u0026plusmn; 0.16 and by Gebeyehu \u003cem\u003eet al\u003c/em\u003e. (2014) for Holstein breeds at 0.53 \u0026plusmn; 0.116. These higher values imply a stronger genetic influence on AFC in these populations, which may be attributed to selective breeding practices and genetic variability within the respective breeds.\u003c/p\u003e\n\u003cp id=\"_Toc201824367\"\u003eThe heritability estimate for calving interval obtained in the present study was 0.180 \u0026plusmn; 0.042. This result is similar to that reported by Tadesse et al. (2014), who found a heritability of 0.16 \u0026plusmn; 0.031 for Ethiopian Boran \u0026times; Holstein Friesian crosses. Additionally, the current estimate is higher than the value reported by Getahun et al. (2018), which was 0.071 \u0026plusmn; 0.03 for Holstein Friesian \u0026times; Boran crosses. Notably, a significantly higher heritability estimate of 0.499 was reported by Ahmed et al. (2007) for Holstein and Jersey crosses with local breeds. It is important to note that the length of the calving interval is influenced by various factors, including the herd\u0026apos;s reproductive management practices, which can significantly affect the genetic expression of this trait. These different estimates of heritability may be due to sample size used, genetic group/breed, and analysis methods as suggested by Sendeku et al. (2015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstimate the variance components, heritability (h2 \u0026plusmn; se) and repeatability (r \u0026plusmn; se) for milk reproductive traits from univariate analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026delta;\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003ee\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026delta;\u003csup\u003e2\u003c/sup\u003ea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026delta;\u003csup\u003e2\u003c/sup\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026delta;\u003csup\u003e2\u003c/sup\u003ec\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eh\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eAFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e129.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e139.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.0798 \u0026plusmn;0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eAFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e129.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e139.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.080 \u0026plusmn;0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e19007.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e4443.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e24730.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e1279.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.180 \u0026plusmn; \u0026nbsp;0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.23\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026delta;\u003csup\u003e2\u003c/sup\u003ea\u003c/strong\u003e = additive genetic variance, \u003cstrong\u003e\u0026delta;\u003csup\u003e2\u003c/sup\u003ec\u003c/strong\u003e = permanent environmental variance, \u003cstrong\u003e\u0026delta;\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ee\u003c/sub\u003e\u003c/strong\u003e = residual variance, \u003cstrong\u003e\u0026delta;\u003csup\u003e2\u003c/sup\u003ep\u003c/strong\u003e= phenotypic variance, \u003cstrong\u003eh\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e= heritability\u0026nbsp;\u003cstrong\u003er\u003c/strong\u003e= repeatability AFS= age at first service, AFC= age at first calving, CI= calving interval.\u003c/p\u003e\n\u003cp id=\"_Toc201823917\"\u003e\u003cstrong\u003e\u0026nbsp;Estimation of Repeatability (r) for productive Traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp id=\"_Toc458112818\"\u003eThe repeatability estimates for productive traits, such as lactation milk yield (LMY), daily milk yield (DMY), and lactation length (LL) were 0.589 \u0026plusmn; 1.00, 0.491 \u0026plusmn; 0.227, and 0.735 \u0026plusmn; 0.151, respectively (Table 4). These high repeatability values indicate that cow performance is a reliable indicator across successive lactations, supporting culling decisions based on individual productivity. The results suggest the presence of substantial additive genetic and permanent environmental variance contributing to trait consistency. The repeatability estimate for LMY in this study is consistent with Getahun \u003cem\u003eet al.\u003c/em\u003e (2018), who reported 0.50 \u0026plusmn; -1 for Holstein Friesian \u0026times; Boran crosses, and Ghorbani \u003cem\u003eet al\u003c/em\u003e. (2011), who reported 0.505 for Iranian Holstein Friesian crosses. However, it exceeds the 0.33 reported by Beneberu \u003cem\u003eet al\u003c/em\u003e. (2020) for pure Jersey breeds and the lower estimate of 0.17 by Haile \u003cem\u003eet al\u003c/em\u003e. (2009a) for Holstein Friesian \u0026times; Boran crosses.\u003c/p\u003e\n\u003cp\u003e\u003cspan id=\"_Toc198392989\"\u003eThe repeatability estimate for daily milk yield (DMY) in this study was 0.46 \u0026plusmn; 0.02, consistent with Getahun et al. (2018) for Holstein Friesian \u0026times; Boran crosses, and higher than the 0.334 reported by Ghorbani et al. (2011) for Iranian Holstein Friesian crosses. However, Demeke et al. (2004b) documented lower repeatability values of 0.30 \u0026plusmn; 0.02 for Holstein Friesian \u0026times; Boran and Jersey \u0026times; Boran crosses.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan id=\"_Toc198392990\"\u003eThe repeatability for lactation length observed in this study was approximately 0.70, as reported by Haile et al. (2009a) for Holstein Friesian \u0026times; Boran crosses. This value was higher than the 0.23 \u0026plusmn; 0.02 reported by Getahun et al. (2018) for the same crossbreds. In contrast, Tadesse et al. (2019) reported a notably lower repeatability estimate of 0.050 \u0026plusmn; 0.07 for Holstein Friesian \u0026times; Boran crosses.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eRegarding reproductive performance, the repeatability estimate for calving interval (CI) in this study was 0.23 \u0026plusmn; 0.01. This value is lower than the 0.359 \u0026plusmn; 0.06 reported by Tadesse \u003cem\u003eet al\u003c/em\u003e. (2019) for Holstein Friesian \u0026times; Boran crosses but higher than values reported by Beneberu \u003cem\u003eet al\u003c/em\u003e. (2020) at 0.09 \u0026plusmn; 0.02 for pure Jersey breeds and by Getahun \u003cem\u003eet al\u003c/em\u003e. (2018) at 0.17 \u0026plusmn; 0.02 for the same crossbreeds. The comparatively low repeatability observed here likely reflects a pronounced impact of transient environmental factors on individual records, thereby increasing within-animal variability and reducing trait consistency across repeated measurements.\u003c/p\u003e\n\u003cp\u003e\u003cspan id=\"_Toc201823918\"\u003e\u003cstrong\u003e\u0026nbsp;Genetic and phenotypic correlations\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp id=\"_Toc458112819\"\u003eDirect genetic and phenotypic correlations for productive traits of lactation milk yield (LMY), daily milk yield (DMY), and lactation length (LL) and reproductive traits (age at first service (AFS), age at first calving (AFC), and calving interval (CI) were estimated using multivariate analysis, as shown in Table below. The results indicated that direct genetic correlations were generally higher than phenotypic correlations for most traits, with some exceptions among reproductive traits. Direct genetic correlations reflect the influence of shared genetic factors, while phenotypic correlations encompass both genetic and environmental effects, as noted by Zeleke \u003cem\u003eet al\u003c/em\u003e. (2019). The study found that traits with positive phenotypic correlations, such as CI and DMY, often aligned with genetic correlations, while other traits exhibited negative or antagonistic correlations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic correlations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genetic correlation between productive traits were positive and the coefficients ranged from weak (0.14 \u0026plusmn; 0.07) to very strong (0.93 \u0026plusmn; 0.03). High correlation observed between LMY and DMY (0.926 \u0026plusmn; 0.032). This signifies that the two traits are governed by the same gene. Similar to our finding Beneberu \u003cem\u003eet al\u003c/em\u003e (2020) a high correlation coefficient of (0.98\u0026plusmn;0.07) between LMY and DMY. On the other hand, Tadesse (2014) reported moderate to very strong genetic correlation (0.589, 0.956 and 0.998) between DMY and LL, LMY and DMY and LMY and LL, respectively. However, weak genetic correlation obtained in the work of Das \u003cem\u003eet al\u003c/em\u003e. (2013) i.e., 0.31 for LMY and LL and 0.30 for LMY and DM, respectively.\u003c/p\u003e\n\u003cp\u003eGenetic correlation coefficients between reproductive traits in the present were weak but positive. AFS-AFC (0.228 \u0026plusmn; 0.172), AFS-CI (0.181 \u0026plusmn; 0.194), AFC-CI (0.063\u0026nbsp;\u0026plusmn; 0.02).In agreement with this finding, Belay \u003cem\u003eet a\u003c/em\u003el.(2014)\u0026nbsp; found a perfect positive genetic correlation (1) between AFS and AFC \u0026nbsp;for Fogera cattle crosses. However, higher genetic correlation between reproductive traits was reported by Beneberu \u003cem\u003eet al\u003c/em\u003e. (2020) for AFC and CI (0.30\u0026plusmn;0.61) and AFS and AFC (0.89\u0026plusmn;0.11) for pure Jersey breed.\u003c/p\u003e\n\u003cp\u003eStrong genetic correlation looked between CI-LL (0.785 \u0026plusmn; 0.074), moderate genetic correlation between CI-LMY and AFC-LL (0.428 \u0026plusmn; 0.098, and 0.40 \u0026plusmn; 0.107), respectively, very weak genetic correlation values were CI-DMY, AFC-LMY and AFS-LMY (0.142 \u0026plusmn; 0.073, 0.024 \u0026plusmn; 0.001, 0.129 \u0026plusmn; \u0026nbsp; 0.056), respectively and finally negative genetic correlation were appeared between AFC-DMY (-0.206 \u0026plusmn;0.072), AFS-DMY (-0.196 \u0026plusmn; \u0026nbsp;0.148) and AFS-LL (-0.020 \u0026nbsp;\u0026plusmn; 0.078).The negative genetic correlation AFC-DMY (-0.206 \u0026plusmn;0.072 and \u0026nbsp;AFS-LL (-0.020 \u0026nbsp;\u0026plusmn; 0.078) similar with the report of Getahun \u003cem\u003eet al\u003c/em\u003e.(2018) AFC-DMY (-0.55) and AFS-LL ( -0.11).\u003c/p\u003e\n\u003cp\u003eIn general, a positive direct genetic correlation between traits in the current study showed that selection of one trait might be a vital for the improvement of other traits. Also, these high genetic correlation results are due to the phenomenon of a single gene affecting more than one trait and due to the occurrence of two or more loci that affect the same trait on the same chromosome Bourdon \u003cem\u003eet al\u003c/em\u003e. (2014). Nevertheless, traits which have shown negative direct genetic correlations in the present study indicates that as one trait increases, the other trait tends to decrease which might be favorable or unfavorable depending on the combination of traits considered.\u003c/p\u003e\n\u003cp id=\"_Toc458112820\"\u003e\u003cstrong\u003ePhenotypic correlations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe phenotypic correlations estimated for production traits were positive very weak (0.017 \u0026plusmn; 0.024) between DMY-LL strong (0.670 \u0026plusmn; 0.012) between DMY-LMY and very strong (0.890 \u0026plusmn; 0.078) between LMY-LL. The phenotypic correlation between LMY-LL in this study was similar with the report of Beneberu \u003cem\u003eet al\u003c/em\u003e. (2018) (0.82\u0026plusmn;0.01) for pure jersey breed and Tadesse \u003cem\u003eet al\u003c/em\u003e. (2014) (0.862) for Boran. The variation of the present study from others might be due to breed, number of observations and analysis methods.\u003c/p\u003e\n\u003cp\u003eThe phenotypic correlation among reproductive traits as indicated in the table were positive very weak (0.011 \u0026plusmn; 0.026) between AFS-AFC and (0.051 \u0026plusmn; 0.055) AFS-CI and negative (0.014 \u0026plusmn;0001) between AFC-CI. Similar results was reported by Getahun \u003cem\u003eet al\u003c/em\u003e.(2018) negative phenotypic correlation AFS- CI (-0.03) .The present study was positive and negative phenotypic correlation of these traits are strongly disagreed with the finding of Belay (2014) who found very strong phenotypic correlation between AFS and AFC (0.85463).The current study vary from others might be due to breed, number of observation studied and software procedure used for analysis.\u003c/p\u003e\n\u003cp\u003eThe phenotypic correlation between productive and reproductive traits was ranged from moderate positive to negative values. The present study lactation length was a negative phenotypic correlation with AFS and AFC. However, positive correlation was showed between AFS-LMY (0.017 \u0026plusmn; 0.031), AFS-DMY (0.041 \u0026plusmn; 0.032), AFC-LMY (0.002 \u0026nbsp; \u0026plusmn;0.037), LL-CI (0.447 \u0026plusmn; 0.019), LMY-CI (0.215 \u0026plusmn;0.023) and (0.017 \u0026plusmn; 0.024).The phenotypic correlation among LL-CI in the current study similar with the report of Beneberu \u003cem\u003eet al.\u0026nbsp;\u003c/em\u003e(2020) 0.41\u0026plusmn;0.02 for pure jersey and higher than the report of Getahun \u003cem\u003eet al\u003c/em\u003e. (2018) 0.18 for HF x Boran.The negative value of LL-AFS similar with the finding of Getahun \u003cem\u003eet al\u003c/em\u003e. (2018) and Das \u003cem\u003eet al\u003c/em\u003e. (2013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstimates of genetic correlations (below diagonal) and phenotypic correlations (above diagonal) between reproductive and production traits\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"731\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eParameters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDMY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eLL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.011 \u0026plusmn; 0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.051\u0026plusmn;0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.017\u0026plusmn; 0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.041\u0026plusmn;0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e-0.017\u0026plusmn;0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.228 \u0026plusmn;0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.014\u0026plusmn;0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.002 \u0026nbsp; \u0026plusmn;0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e-0.122\u0026plusmn;0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e-0.0062\u0026plusmn;0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.181 \u0026plusmn;0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.063 \u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.215 \u0026nbsp;\u0026plusmn;0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.017\u0026plusmn; 0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.447 \u0026plusmn; 0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eLMY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.129 \u0026plusmn;0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.024 \u0026plusmn; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.428\u0026plusmn; 0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.670\u0026plusmn; 0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.890 \u0026plusmn; 0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eDMY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-0.196\u0026plusmn;0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.206 \u0026plusmn;0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.142\u0026plusmn; 0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.926 \u0026plusmn; 0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.017 \u0026plusmn; 0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-0.020\u0026plusmn;0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.40 \u0026plusmn;0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.785\u0026plusmn; 0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.854 \u0026plusmn; 0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.141\u0026plusmn; 0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAFS=Age at First Service, AFC=Age at First Calving, CI=Calving Interval, LMY=Lactation Milk Yield, DMY=Daily Milk Yield, LL=Lactation Length.\u0026nbsp;\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe values of heritability and repeatability both productive and reproductive traits were none zero values and ranges from low to higher. Highest heritability estimate value was 0.235 \u0026plusmn; 0.053 for DMY and the lowest was 0.180 \u0026plusmn; 1.00 for LMY whereas highest LL (0.735\u0026plusmn; 0.151) and lowest DMY (0.491\u0026plusmn; 0.227) repeatability values were obtained. However, the current study shows that Low heritability and repeatability indicates that comparatively high environmental variance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe genetic correlations between the traits in the current study were higher than the corresponding phenotypic correlations among all traits. Knowing that all of the productive and reproductive traits in this study have only positive genetic correlations, it is likely that similar genes control them all. This indicates that selecting for one trait will improve other correlated traits in the desired direction, which will aid in the breeding process overall by improving all of the traits that are correlated with one another. The phenotypic correlation between productive traits was ranges from very weak to strong correlation. Strong phenotypic correlation was observed between LL and LMY. However, negative correlation was observed among AFC and LL. Therefore, it is recommended that future studies verify the lower estimates of certain traits by using larger datasets and applying multivariate models for both productive and reproductive traits.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eDescription of the study area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was conducted at the Holetta Agricultural Research Center (HARC), located in Ethiopia\u003csup\u003e\u0026rsquo;\u003c/sup\u003es central highlands, approximately 35 kilometers west of Addis Ababa. The area is situated between 3\u0026deg;24\u0026prime;N and 14\u0026deg;53\u0026prime;N latitude and 33\u0026deg;00\u0026prime;E to 48\u0026deg;00\u0026prime;E longitude, at an elevation of 2,400 meters above sea level. It receives an average annual rainfall of 1,100 mm and has an average temperature of 15\u0026deg;C, with daily minimum and maximum temperatures of 6\u0026deg;C and 24\u0026deg;C, respectively (Gojam et al., 2016). The region experiences an average monthly relative humidity of 60% (Gebreyohanes et al., 2013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverview of Dairy Cattle Research Farm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Holetta Research Center was established in 1966. Initially, the center focused on evaluating the preliminary characterization, milk production, and reproductive performances of selected indigenous cattle breeds at four experimental stations (Holetta, Horo, Melka-Werer, and Adamitulu). The indigenous breeds produced an average total lactation yield of 550 kg over a 6-month lactation period. However, due to the lower milk yield of indigenous cows and the high demand for milk and milk products driven by rapid human population growth, crossbreeding was proposed in 1972 by G. Winner, a FAO consultant.\u003c/p\u003e\n\u003cp\u003eThe first preliminary results of the long-term dairy cattle crossbreeding experiments in Ethiopia were reported in Sendros, (1987), 20 years after the start of the experiment. The results indicated that first generation (F\u003csub\u003e1\u003c/sub\u003e) crossbred dairy cows in general produce three to five times more milk than indigenous cows. Kebede, (1992) conducted a comprehensive study and identified milk production as one of the breeding program\u0026apos;s target goals, achieving significant success.\u003c/p\u003e\n\u003cp\u003eCurrently, due to fluctuations in the inheritance of exotic genes among crossbreds produced through breeding and the lack of an appropriate breeding program, efforts are underway to develop a 75% synthetic/composite dairy breed at Holeta Agricultural Research Center (HARC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimal Management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe herds were managed based on their breed group, pregnancy stage, lactation period, sex, and age. Consistent feeding and management protocols were applied to all animals within each specific category. During the day, animals were allowed to graze from early morning until evening. A concentrate mixture made up of wheat bran (54%), noug (Guizotia abyssinica) cake (45%), and salt (1%) was supplemented according to their body weight, productivity, and physiological status. Cows, heifers, and calves were supplemented with the concentrate mixture at rates of 4 kg, 1-1.5 kg, and 0.25-1 kg per day per animal, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCalves were weighed and ear tagged within 24 hours of birth. After four days, they were moved to a calf rearing pen where they were provided with a dry diet and 260 kg of whole milk over 98 days through bucket feeding, except for the F1 calves who suckled their dams until weaning. Weaned calves were then transferred to another pen and kept indoors until they reached 6 months of age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMating Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Boran cattle, sourced from Boran pastoralist communities in southern Ethiopia, were used as the foundation stock for crossbreeding. Initially, pure Boran cows were inseminated with pure Holstein Friesian (HF) semen to produce 50% F1 crossbreeds. These F1 animals were then backcrossed with pure Holstein Friesian semen to generate the first-generation 75% Holstein Friesian\u0026ndash;25% Boran offspring. The later generations (F2 and F3) were produced by mating 75% (HF X Boran) males with 75% (HF X Boran) females to create a synthetic breed with 75% HF and 25% Boran gene inheritance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Source and Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study utilized data collected over a 30-year period, from 1995 to 2024, at the Holetta Agricultural Research Center (HARC). In total, 13,116 crossbred dairy cattle performance records were used for this study (see Table 1).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003e\u0026nbsp;Milk production Traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 306px;\"\u003e\n \u003cp\u003eReproduction Traits \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eGenotypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eLMY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eDMY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eAFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eAFC \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e50% F1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e7980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e50% F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e50% F3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e75% F1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e2642\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e75% F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e2564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e2564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e13,116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLMY=Lactation Milk Yield, DMY=Daily Milk Yield, LL=Lactation Length, CI=Calving Interval, AFS =Age At First Service, AFC=Age At First Calving.\u003c/p\u003e\n\u003cp\u003eTable 2: Number of observations in pedigree records\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003ePedigree data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eNo. of animals with unknown sire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eNo. of animals with unknown dam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eNo. of animals with both parents unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eNo. of sires\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eNo. of animals with paternal grandsire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003eNo. of animals with paternal grand dam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTraits to be studied\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe traits analyzed in this study were classified into two groups: productive and reproductive traits. Productive traits included lactation milk yield (LMY), daily milk yield (DMY), and lactation length (LL). Reproductive traits included age at first service (AFS), age at first calving (AFC), and calving interval (CI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic Parameter Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariance and covariance components, heritability, repeatability, and genetic correlations were estimated using WOMBAT software. Univariate and multivariate analyses were applied for genetic parameter estimation.\u003c/p\u003e\n\u003cp\u003eThe following animal model was applied,\u003c/p\u003e\n\u003cp\u003eY = Xb + Za + Wd + e. where;\u003c/p\u003e\n\u003cp\u003eY, is a vector of observations for the traits of interest\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;b, is a vector of fixed effects (genetic group, calving year, calving season and parity).\u003c/p\u003e\n\u003cp\u003ea, is a vector of random individual additive effects\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ed, is a vector of permanent environmental effects\u003c/p\u003e\n\u003cp\u003eX, matrices relating records to fixed effects\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Z, incidence matrices relating records to individual animal effect\u003c/p\u003e\n\u003cp\u003eW, matrices of permanent environmental effects\u003c/p\u003e\n\u003cp\u003ee, vector of random residual effect\u003c/p\u003e\n\u003cp\u003eThe model assumed the expected mean of zero and variances \u0026sigma;a\u003csup\u003e2\u003c/sup\u003e, \u0026sigma;c\u003csup\u003e2\u003c/sup\u003e and \u0026sigma;e\u003csup\u003e2\u003c/sup\u003e, respectively. Pedigree data as the software already recognized the formula as follows;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003e\u0026sigma;p\u003csup\u003e2\u003c/sup\u003e = \u0026sigma;a\u003csup\u003e2\u003c/sup\u003e + \u0026sigma;c\u003csup\u003e2\u003c/sup\u003e +\u0026sigma;e\u003csup\u003e2 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003e\u0026sigma;p\u003csup\u003e2\u003c/sup\u003e; is phenotypic variance (total variance)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eh\u003csup\u003e2\u003c/sup\u003e = \u0026sigma;a\u003csup\u003e2\u003c/sup\u003e/\u0026sigma;p\u003csup\u003e2\u003c/sup\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003e\u0026sigma;a\u003csup\u003e2\u003c/sup\u003e; additive genetic variance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003er\u003csup\u003e2\u003c/sup\u003e = \u0026sigma;a\u003csup\u003e2\u003c/sup\u003e+\u0026sigma;c\u003csup\u003e2\u003c/sup\u003e/\u0026sigma;p\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003e\u0026sigma;c\u003csup\u003e2\u003c/sup\u003e; permanent environmental variance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003eAi = h\u003csup\u003e2\u003c/sup\u003e x P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 319px;\"\u003e\n \u003cp\u003e\u0026sigma;e\u003csup\u003e2\u003c/sup\u003e; residual variance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our heartfelt gratitude to the reviewers for their comprehensive, thoughtful, and constructive feedback. Their in-depth recommendations significantly enhanced the clarity, organization, and scientific integrity of our manuscript. We are truly thankful for the time and expertise they contributed to refining our work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u0026rsquo;\u003cstrong\u003e\u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAA contributed to design of the study, data analysis and interpretation, drafting and revising the manuscript. HW contributed to conception and design of the study, data collection, data analysis and interpretation and drafting the manuscript. ZW contributed to drafting and revising the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funding was received for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not require official or institutional ethical approval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrior publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData have not been published previously.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details:\u003c/strong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eEthiopian Institute of Agricultural Research, Holeta Agricultural Research Center, P O Box 2003 Addis Ababa or 31 Holeta, Ethiopia.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmed, M.-K., Teirab, A. 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Genetic parameters of health disorders, and relationships with 305-day milk yield and conformation traits of registered Holstein cows. \u003cem\u003eJournal of Dairy Science, 81\u003c/em\u003e(8), 2264-2270. \u003c/li\u003e\n\u003cli\u003eWasike, C., Ilatsia, E., Ojango, J., \u0026amp; Kahi, A. (2006). Genetic parameters for weaning weight of Kenyan Boran cattle accounting for direct-maternal genetic covariances. \u003cem\u003eSouth African Journal of Animal Science, 36\u003c/em\u003e(4), 275-281. \u003c/li\u003e\n\u003cli\u003eYacob, Y. (2008). Environmental and genetic parameters of growth, reproductive and survival performance of Afar and blackhead Somali sheep at Werer Agricultural Research Centre, Ethiopia. \u003c/li\u003e\n\u003cli\u003eYosef Tadesse. 2006. Genetic and Non-Genetic analysis of fertility and production traits in Holetta and Ada\u0026rsquo;a Berga Dairy herds. MSc Thesis, Alemaya University, Alemaya, Ethiopia.\u003c/li\u003e\n\u003cli\u003eZeleke, B. (2014). \u003cem\u003eEstimation of genetic parameters for growth and reproductive traits of Fogera x Holstein Friesian crossbred cattle at Metekel ranch, Amhara region, Ethiopia.\u003c/em\u003e MSc Thesis, Haramaya University, Haramaya, Ethiopia, \u003c/li\u003e\n\u003cli\u003eZeleke, T. (2019). \u003cem\u003eOn-station and on-farm performance evaluation and genetic parameters estimation of Boer x Central Highland crossbred goat in North Wollo Zone, Ethiopia.\u003c/em\u003e \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Crossbred dairy cattle, Ethiopia, Genetic correlation, Genetic parameters, Heritability, Milk yield, Reproductive traits","lastPublishedDoi":"10.21203/rs.3.rs-8865360/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8865360/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eThe objective of this study was to estimate genetic parameters for production (milk yield) and reproduction traits in a dairy cattle breed at the Holeta Agricultural Research Center. The analysis utilized extensive records covering 13,116 observations collected over a 30-year period (1995 to 2024). The genetic parameters for milk yield and reproductive traits were estimated using WOMBAT software via multivariate analysis. The heritability estimates for lactation yield traits (LMY, DMY, and LL) were 0.180 ± 1.00, 0.235 ± 0.053, and 0.219 ± 0.077, respectively, and for reproductive traits (AFS, AFC, and CI) 0.0798 ± 0.034, 0.080 ± 0.033, and 0.180 ± 0.042, respectively.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe results indicated that repeatability values of lactation yield traits were 0.589 ± 1.00 for LMY, 0.491 ± 0.227 for DMY, 0.735 ± 0.151 for LL, and 0.23 ± 0.01 for CI. The study also found positive direct genetic correlations between lactation yield traits, ranging from very weak (0.141 ± 0.073) to very strong (0.854 ± 0.304) genetic correlations. High correlation was observed between LMY and LL (0.854 ± 0.304).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePositive genetic correlations ranging from very weak to weak were found among reproductive traits. AFS-AFC (0.228 ± 0.172),AFS-CI (0.181 ± 0.194), AFC-CI (0.063 ± 0.02). The study indicated that the genetic correlation among lactation yield and reproductive traits was closely related in some traits. Strong genetic correlation was found between CI-LL (0.785 ± 0.074), moderate genetic correlation between CI-LMY and AFC-LL (0.428 ± 0.098, and 0.40 ± 0.107), respectively.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe low to moderate heritability estimates suggest that mass selection alone may be slow, and proper management plays a significant role in improving these traits. The favorable genetic correlations found indicate that selection for certain milk yield traits (like LMY or LL) could also lead to a positive correlated response in reproductive efficiency (e.g., shorter CI). Knowing these genetic parameters is crucial for designing effective breeding programs that prioritize traits showing high favorable correlations for overall animal performance improvement.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Estimates of genetic parameters for milk yield and reproductive traits in crossbreed dairy cattle at the Holeta Agricultural Research Center, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 08:18:15","doi":"10.21203/rs.3.rs-8865360/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":"298091d4-ccbc-4203-9fc3-4975538061b3","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62880858,"name":"Biological sciences/Genetics"},{"id":62880859,"name":"Biological sciences/Zoology"}],"tags":[],"updatedAt":"2026-05-18T18:39:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 08:18:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8865360","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8865360","identity":"rs-8865360","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}