Estimation of genetic and phenotypic parameters for milk production and reproduction traits in a developing synthetic dairy cattle breed at Holeta Agricultural Research Center, Ethiopia

Estimation of genetic and phenotypic parameters for milk production and reproduction traits in a developing synthetic dairy cattle breed at 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 Estimation of genetic and phenotypic parameters for milk production and reproduction traits in a developing synthetic dairy cattle breed at Holeta Agricultural Research Center, Ethiopia Asamenew Ayalew, Haile Welearegay, Zewdie Wondatir This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8744363/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract This study was carried out to estimate genetic and phenotypic parameters for milk production and reproduction traits of synthetic dairy cattle breed development program being implemented at Holeta research center dairy farm. Data collected from 1995 through 2024 on lactation milk yield, lactation length ,daily milk yield, age at first service, age at first calving, and calving intervals from experiments targeted at developing a synthetic breed at the Holeta Agricultural Research Center dairy herd were used for this study. The GLM procedures of SAS software were used to estimate the effect of fixed effects such as year, season and parity while regression analysis was performed to estimate crossbreeding parameters (additive, heterosis and recombination effects). Genetic components, including variance covariance estimates were analyzed using WOMBAT software. A univariate mixed model for genetic parameters and Multiple Regression Model for crossbreeding parameters was used for data analysis. The performance of dairy cattle affected by genetic and non-genetic factors. The result of fixed effects (year and genetic group) analysis showed that significant (p<0.0001) differences in all productive and reproductive traits. Correspondingly, productive traits (LMY and DMY) and reproductive (CI) traits were also significantly (p<0.0001) influenced by parity. The traits, lactation and milk yield, were sensitive to seasonal variation. The overall least square means for lactation milk yield (LMY), daily milk yield (DMY), lactation length (LL), age at first service (AFS), age at first calving (AFC), and calving interval(CI)were 2140.61 ± 32.92kg, 6.89 ± 0.07kg, 316.54 ± 3.31days, 33.56 ± 0.63months, 42.78 ± 0.63months and, 469.01 ± 7.03days, respectively. Additive genetic effects were much larger than the non-significant negative value of heterosis effect of lactation milk yield (3728 ± 139.39 kg of additive and -81.65 ± 97.98 kg of heterosis). The cross-breeds were -21.51± 29.19 days, -2.29 ± 3.12 months, and -2.23 ± 3.12 months, reduced for CI, AFS and, AFC due to the additive effect of the Friesian gene. Estimations of heritability for productive traits (LMY, DMY, and LL) were 0.180 ± 1.00, 0.235 ± 0.053 and 0.219 ± 0.077, respectively, and reproductive traits (AFS, AFC, and CI) 0.0798 ±0.034, 0.080 ±0.033and 0.180 ± 0.042, in respective order. The current result indicated that repeatability values of productive traits 0.589 ± 1.00 for LMY, 0.491± 0.227 for DMY, 0.735± 0.151 for LL, and0.23±0.01 for CI. The current study indicated that the direct genetic correlation between productive traits was positive and ranged from very weak (0.141 ± 0.073) to very strong (0.854 ± 0.304) genetic correlations. From the current study, a high correlation was observed between LMY and LL (0.854 ± 0.304). The current study indicated that positive genetic correlation ranged from very weak to weak genetic correlation among reproductive traits. AFS-AFC (0.228 ± 0.172), AFS-CI (0.181 ± 0.194), and AFC-CI (0.063 ± 0.02). The present study indicated that the genetic correlation between productive and reproductive traits was closely related with each other in some traits. 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. Thus, based on the study's findings, it was feasible to draw the conclusion that proper parental line selection and crossing should be used to create next-generation calves and improve the farm's overall management system. Biological sciences/Genetics Biological sciences/Zoology Additive genetic effect Borena crossbred dairy cattle Ethiopia genetic parameter genetic trend Holeta productive performance reproductive performance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. INTRODUCTION Globally, there are approximately 1.5 billion cattle and 1000 recognized breeds (FAO, 2020). Ethiopia alone is home to more than 30 cattle breeds, with a national cattle population of 66 million (CSA, 2022). However, the cattle population in Ethiopia is predominantly composed of indigenous breeds, which are mainly characterized by low milk yield (1.48 kg) and shorter lactation length (approximately 7 months) (CSA, 2022. Since the 1950s, crossbreeding has been practiced in Ethiopia to improve milk production. Among the exotic cattle breeds, germplasm of Holstein Friesian, Jersey and Semental have been imported for crossbreeding with the local breeds (Kebede, 1992 ). According to recent reports (CSA, 2022), the proportion of crossbred cattle in the country does not exceed 3%. Currently, the Holstein Friesian and Jersey breeds are mainly utilized for crossbreeding purposes, Tesema et al. ( 2020 ). In tropical regions, this kind of crossbreeding has been frequently employed to combine the high milk production of Bos Taurus breeds and resistance to disease, heat stress, adaptation to poor management, and survival to low-quality feeds of indigenous breeds. The first filial generation (F1) of these crosses has a significant advantage in total output due to heterosis impacts on milk yield and reproduction traits, in addition to the additive contribution of each breed to overall performance, Mackinnon et al. ( 1996 ). Nevertheless, additional improvement through backcrossing to the European breed produced inconsistent and frequently unsatisfactory results. According to Schuler et al. (2001), a number of factors influence crossbred offspring, namely direct effects, parental effects, heterosis effects like dominance and epistasis, recombination loss, and implications for combination appropriateness. These effects, which have been theoretically outlined by Hill et al. (1971), rely on the breeds involved and the characteristics of interest. When there are significant trait-related variations between the parent breeds, heterosis effects are likely to be significant. For example, among dairy cow breeds, Jersey has the greatest genetic distance from Holstein-Friesian (HF) (Basedow, 1998 ). A detailed review of crossbreeding schemes for dairy cattle and of heterosis estimates is reported in the literature (Sorensen et al. 2008). It is noted that heterosis effects are not heritable additions accompanying the combined additive effects as a bonus of a cross and decreasing in advanced generations of crosses between two breeds. On the other hand, rotation crosses result in a cyclical gene composition from generation to generation, Schüler et al. ( 2001 ). Two breed rotations maintain 67% of F1 heterosis at equilibrium and 3 breed rotational crosses maintain 86% of F1 heterosis. A decrease in milk production from F1 to F2 (and F3) was noted by Littlewood ( 1933 ). However, this was attributed to gene segregation or loss of heterosis. Ayrshire and Friesian F2 crossings with zebu breeds (Sahiwal, Red Sindhi, and Hariana) yielded 30–35% less milk than F1 crosses (Kartha's, 1934). In a review of dairy cattle cross-breeding experiments in the tropics, Syrstad ( 1989 ) concluded that most of the decline in the productivity from F1 to F2 generations was due to loss of heterozygosity, i.e. dominance effects were the most important contributor to heterosis, with perhaps a small negative effect of recombination on milk yield. Milk yield increased only slightly, or even declined, fertility deteriorated and mortality increased. The lack of adaptation to tropical conditions was obvious. Breeding strategies aiming at the economical use of genetic resources require information on breed and cross-breed performance, including estimates of within and between-population genetic factors (Dickerson, 1969). Nevertheless, these estimations differ significantly among breeds, production systems, estimation techniques, etc. (Kahi et al., 2000 ; Lobo et al., 2000 ). A crossbreeding program can be optimized by using the best breed combinations and breeding systems to maximize heterosis. Separating the additive and non-additive contributions and dividing the latter into within-locus (dominance) and between-locus (epistatic) components are the challenges in crossbreeding (Tadesse and Tadelle, 2003). Even though Ethiopia has been upgrading or crossbreeding native cattle with exotic breeds for the past 50 years or more, breed combinations are optimized for dominance and additive genetic contribution. Selecting the best crossbreeding method for the country's milk production is still hindered by the absence of reliable estimations of crossbreeding parameters. 1.2. Statement of the research problem Dairy cattle production in Ethiopia is a vital component of the country's agriculture, contributing significantly to the national economy. However, local breeds have limited genetic potential for milk production and reproductive performance, which hampers the growth of the dairy cattle industry. To address this, upgrading the genetic potential of local breeds through crossbreeding with exotic dairy cattle breeds, developing synthetic breeds, and promoting good management practices are essential strategies to enhance productivity and promote sustainable dairy cattle farming in Ethiopia. A dairy research at Holetta agricultural research center has been conducting crossbreeding experiments for five decades and evaluating different crossbreds of different exotic gene inheritance. As a result, researchers at Holetta research center initiated a synthetic breed development program a few years ago. Developing synthetic breeds of dairy cattle in Ethiopia hold greater long-term importance than traditional crossbreeding programs. Crossbreeding is mating local cows with exotic dairy breeds to improve milk yields, which often lacks consistency and sustainability. This is due to uncontrolled breeding practices and a lack of adaptability in successive generations. In contrast, synthetic breeds are developed through systematic breeding over several generations to combine the high milk production traits of exotic breeds with the hardiness, disease resistance, and environmental adaptability of indigenous Ethiopian cattle. This approach results in a more stable and uniform breed population that can thrive in local conditions while maintaining improved productivity. Moreover, synthetic breeds reduce reliance on the continuous importation of exotic genetics, lowering costs and preserving national biosecurity. Therefore, the current study estimated the genetic and phenotypic parameters for productive and reproductive traits of the ongoing synthetic dairy cattle breeding program at HARC. 1.3. Objectives 1.3.1. General objective To assess the genetic and non-genetic factors influencing productive and reproductive traits in an ongoing synthetic breed development program, with the aim of improving breeding strategies through the estimation of genetic and crossbreeding parameters. 1.3.2. Specific Objectives To estimate genetic parameters for productive and reproductive traits in an ongoing synthetic breed development program at Holetta Agricultural Research Center. Evaluate the effect of non genetic factors affecting productive and reproductive performance traits in an ongoing synthetic breed development program at Holetta Agricultural Research Center. To estimate crossbreeding parameters of productive and reproductive traits of an ongoing synthetic breed development program at Holetta Agricultural Research Center. 2. MATERIALS AND MHODS 2.1. Description of the Study Area The study was conducted at Holetta Agricultural Research Center (HARC). It is located at 9°30' N and 380 30' E and 35 km west of Addis Ababa on the way to Ambo. The topography where the center is located consists of a section of central Ethiopia, which represents a cool tropical highland area that covers about 30% of the land mass of Ethiopia and more than 70% of the population of the country. In the area where the center is located, the topography can be expressed by the existence of some scattered hills and mountains ranging between altitudes of 2250 m to 2500m above sea level. The area receives an average annual rainfall of around 1,200 mm. The region's average yearly temperature is 18°C, with relative humidity averaging 60% throughout the year. Figure 1: Map of study area 2.2. Data Source and Data Collection Data recorded from 1995 through 2024.at Holetta Agricultural Research Center (HARC) were used for this study. The Borana cow breed was used as a foundation stock to produce 50% F1 crosses. Whereas, 50%of F1, F2 and F3, 75%F1, and 75%F2 were different genotypes resulting from subsequent crossing. The following data were recorded in a database: Identification number of animals Date of birth Date of first service Date of First calving Daily milk yield, lactation length and lactation milk yield Parity genetic group Sire of a cow and dam of a cow. 2.3. Animal Management The animals were managed according to 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 daytime, animals were allowed to graze starting from early morning until evening. A concentrate mixture composed of wheat bran (64%), noug (Guizocia abyssinica) cake (35%), and salt (1%) was supplemented for heifers up to 2 years of age, 69% wheat bran 39% noug cake, and salt 1% for pregnant cows and calves supplemented with concentrate mixture at a rate of 0.25-1 kg per day/animal. The animals had free access to clean tap water. Calves were permitted to suckle their dam immediately after birth for approximately four days to ensure they received sufficient colostrum. Weighting and ear tagging are applied within 24 hours after birth. After four days, they were moved to a calf rearing pen and provided with a dry diet and whole milk. Over 98 days, 260 kg of whole milk was administered via bucket feeding for the F1 calves which suckled their dams until weaning. Weaned calves were transferred to another pen and kept indoors until 6 months of age. Cows have been milked with a milking machine twice daily (early morning and evening) since 2002. Since 2005, animal selection has been conducted through estimated breeding value and physical appearance. All herds on the research farm were vaccinated for major transmittable diseases (Anthrax, Blackleg, Foot and Mouth Disease (FMD), and Lamp Skin Disease (LSD) and were vaccinated on a regular schedule. Management system (feeding) of the herd might vary with seasons, depending on availability of feed and other inputs. 2.4. Overview of Dairy Cattle Research at Holeta Agricultural Research Center Holetta Research Center was established in 1966. The dairy cattle research started two years later after the establishment of the center. In the beginning, preliminary characterization and milk production and reproductive performances of selected indigenous cattle breeds were evaluated at four experimental stations (Holetta, Horo, Melka-Werer and Adamitulu). Indigenous breeds such as Begait, Borana and Horro were evaluated for milk production. As a result, these indigenous breeds produced an overall total lactation yield of 550 kg over a lactation period of 6 months. However, due to the lower milk yield of indigenous cows and high demand for milk and milk products associated with alarming human population growth, crossbreeding was proposed in 1972 by G. Winner FAO consultant. The first preliminary results of the long-term dairy cattle crossbreeding experiments in Ethiopia were reported by Sendros, (1987), 20 years after the start of the experiment. The results indicated that first generation (F1) 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. The F1 (50%) and ¾ crosses produced a 3 to 5-fold higher daily milk yield than their contemporary local cow groups. The F2 and F3 genotypes resulting from the inter se mating, however, produced somewhat lower milk yield due to loss of hybrid vigor compared to ½ F1 and ¾ F1 crosses. Moreover, it was also concluded that Jersey crosses produce higher milk yield per metabolic body weight than Friesian and Simmental crosses, reflecting the higher efficiency of Jersey crosses for milk production under low-input, low-output dairy production systems. Likewise, crossbred calves were found to have a higher birth weight and growth rate to reach puberty earlier than compared to local calves. The second approach was the extension of the conclusive results obtained from the first national dairy cattle crossbreeding program. The aim of this approach was to develop a 50% synthetic dairy breed around the milk sheds of Addis Ababa with nucleus herd planned to be established at Holetta Agricultural Research Center. A community-based open nucleus breeding scheme was suggested and the program was designed to span over a period of 10 years (1995–2005). However, this strategy was unsuccessful and finally failed due to absence of on-farm dairy cattle performance recording schemes. Due to fluctuations in exotic gene inheritance among crossbreds animals produced as a result of crossing and lack of an appropriate breeding program, efforts are underway to develop a 75% synthetic/composite dairy breed at Holeta Agricultural Research Center (HARC). The current ongoing synthetic breed development program has already attained fourth generation. Subsequent crossing will be continued until the 9th generation is achieved, where gene segregation will be fixed and stabilized at this stage. 2.5. Mating Design The Borana cattle used as a foundation stock for crossbreeding were brought from Borena pastoralists in southern Ethiopia. Pure Borena dams mated with pure Holstein Friesian (HF) semen to produce 50% F1 crosses while the 50% F1 is back crossed with pure Friesian semen to produce the 75% (HF X Borana) first generation. The later generations (F2 and F3) were produced by inter se mating of 75% (HF X Borana) males with 75% (HF X Borana) females to produce a synthetic breed of 75% HF and 25% Borena gene inheritance. The mating design used to produce synthetic breed in the farm is indicated in Fig. 1. (Source: Adopted by Philipsson (2011). Mating was undertaken throughout the year using artificial insemination. Semen of 75% (HF X Borana) was sourced from NAIC, Kality, Addis Ababa. In cases where cows/heifers became repeat breeders following AI, natural service was occasionally used. Bulls born on the farm were selected for breeding based on the milk performance of their dams and physical conformation. These selected bulls were used for on-station breeding activities after their semen was collected and evaluated in collaboration with NAIC. A careful attention was paid to avoiding genetic-relatedness during bull selection. Heat detection in cows was carried out daily by herd attendants and a teaser bull kept together with the female herd. Cows exhibiting standing heat were artificially inseminated by qualified technicians. Cows that did not exhibit signs of heat after service were diagnosed for pregnancy at 45 days post-insemination. 2.6. Data management Data cleaning was done to avoid too many missed observations, outliers and incorrectly recorded observations. Lactation lengths less than 100 days were eliminated from the data set. Age at first service (AFS) below 10 months and above 80 months, as well as age at first calving (AFC) below 20 months and above 90 months, were removed from the data set. Parities above 7 were few and grouped as parity 7. The average gestation time for cows is 285 days, and they have a 45-day voluntary waiting period (330 days CI) following calving. Cows with a calving interval (CI) of less than 330 days were eliminated from the analysis. Table 1 Records used for analysis of genetic and crossbreeding parameters for different trait Milk production Traits Reproduction Traits Genotypes LMY DMY LL CI AFS AFC Total Pure Boran 240 240 132 205 44 51 912 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 2804 2804 2696 2113 1802 1809 14,028 LMY=Lactation Milk Yield, DMY=Daily Milk Yield, LL=Lactation Length, CI=Calving Interval, AFS = Age At First Service, AFC = Age At First Service 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 N=Number of observations 2.7. Method of Data Analysis Data analysis on non-genetic factors was performed using SAS software (SAS, 2004). A general linear model (GLM) of this software was employed to the effect of genetic group, parity, season and year-on-age at first service (AFS), age at first calving (AFC), calving interval (CI), lactation milk yield (LMY), daily milk yield (DMY), and lactation length (LL). The genetic groups considered in the model were 50% HFXB and 75% HFXB). No selection and improvement has been undertaken on the Borena breed since it has only been used as a dam line for F1 generations. Therefore, performance evaluation of the pure Borena breed was not the objective of this study and was excluded from fixed effect analysis. However, their genetic contribution for further generations was immense (25% and 50%) in the present study and the breed was fitted in to the genetic and genotypic models to calculate their contribution. For season of birth and calving, months in a year were classified into 3 seasons based on rainfall distribution. October to February is the dry season; March to May is a short rainy season; and June to September is the main rainy season. Due to the limited number of records available per calving year, the years were categorized into 5 groups: 1995–1999 (year 1), 2000–2005 (year 2), 2006–2011(year 3), 2012–2017 (year 4), 2018–2024 (year 5). Lactation milk yield (LMY) is the mammary gland secretes and yields milk throughout a single lactation period. Daily milk yield (DMY) is the amount of milk produced by the cow each day in the morning (AM) and evening (PM). Lactation length (LL) refers to the period from when a cow starts to secrete milk after parturition to the time of drying off. The calving interval (CI) is the period between two consecutive parturitions and one of the major components of reproductive performance that influences livestock production systems. Age at first service (AFS) is defined as the age at which heifers reach sexual maturity and attain body condition and sexual maturity after accepting service for the first time. Synthetic breeds are developed by combining two or more different breeds, aiming to take advantage of hybrid vigor while maintaining a stable population without the need for further crossbreeding (Bourdon, 2000 ). 2.7.1. Model was used for fixed effect analysis: Model 1 For production traits (LMY, DMY, and LL) and for reproductive trait (CI) Y ijkln = µ + Y i + S j + G k + P l + e ijkln, Where; Y ijkln =n th record of, i th year, j th season, k th genetic group and l th parity µ = overall mean Yi = effect of i th Year of Calving S j = effect of j th Season of Calving dry (October to February), short rain season (March to May), and long rain season (June to September). G k = effect of k th Genetic group (50% F1, F2, F3 and 75% F1, F2) Pl = effect of l th Parity of Dam (1, 2, 3, 4, 5, 6, 7) e ijkln = random error associated with each observation Model 2 Reproductive traits (AFS and AFC) were analyzed the main model without the effect of parity. Y ijkn = µ + Y i + S j + G k + e ijkn Where, Y ijkn = n th record of, it h year, j th season, k th genetic group µ = overall mean Y i = effect of i th year of birth S j = effect of j th season of birth G k = effect of k th genetic group (i = 75%HF x BoF1, 75% HF x BoF2, 75% HF x BoF3) e ijkn = random error associated with each observation 2.7.2 Genetic Parameter Analysis Variance and covariance components, heritability, repeatability, and genetic correlations were estimated by using WOMBAT software version 01-11-2011. Univariate and multivariate analysis 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 2.7.3. Crossbreeding Parameter Analysis The regression analysis was performed using the Generalized Linear Model (GLM) procedure in SAS version 9.0 (2004) was used to analyze the productive (LMY, DMY, and LL) and reproductive (AFS, AFC, and CI) performance traits on crossbreeding parameters. Crossbreeding effects were decomposed into breed additive effects, heterosis, and recombination loss coefficients. These components were fitted as covariates in the model to estimate the breed additive (gi), heterosis (hij), and recombination loss (rij) coefficients, following the methodologies outlined by Dickerson et al. (1969) and Akbaş et al. (1993). The genetic model used for estimation of crossbreeding parameters is indicted as follows; Y = X 1 b 1 + X 2 b 2 + βα. Where; Y, is a vector of observations for the traits of interest. b 1 , is a vector of fixed effects other than genotype. b 2 , genetic effect (breed additive difference, heterosis and recombination coefficients). Β is the matrix of expected genetic contribution (breed additive, heterosis and recombination loss) α is a vector of the estimated corresponding parameters including overall mean X 1 , matrices relating records to fixed effects. X 2 is a matrix of coefficients relating fixed breed additive, heterosis and recombination effects to the individual trait record. The equation uses to calculate breed additive (g i ), heterosis (h ij ) and recombination loss (r ij ) effects will as follows: Breed additive (g i ) = ½ (𝛼i s + 𝛼i d ), Heterosis (h ij ) = 𝛼i s 𝛼j d + 𝛼j s 𝛼i d and Recombination loss (rij) = 4 gi gj - h ij (Wolf et al., 1995 cited by Demeke et al., 2004 a, b) where 𝛼i s and 𝛼i d denote the gene proportion of breed i in the sire and dam of the cow, respectively. Table 3 The proportions of Holstein Friesian genes, individual and maternal heterosis and individual recombination coefficients used in prediction of performance of different genetic group Breed and Genetic group (sire x dam) Genetic coefficient gI h I rI Pure Borena 0 0 0 50% F1 (HF*Bo) 0.5 1 0.5 F2 (HF*Bo) *(HF*Bo) 0.5 0.5 0.5 F3 (((HF*Bo)*(HF*BO))*((HF*Bo)*(HF*BO))) 0.5 0.5 0.5 75% F1 HF * (HF*Bo) 0.75 0.5 0.25 F2 ((HF * (HF*Bo) * (HF * (HF*Bo)) 0.75 0.375 0.375 g I ; individual additive genetic, h I ; individual heterosis, r I individual recombination effect Bo; Borena, HF; Holstein Friesian. 3. RESULTS AND DISCUSSION 3.1. Productive Performance 3.1.1. Lactation Milk Yield Results of the least square mean and standard errors for fixed effects of genetic group, calving year, calving season and parity, are summarized in Table 8 . The overall lactation milk yield, daily milk yield and lactation length in the present study were 2140.61 ± 32.92 kg, 6.89 ± 0.07kg and 316.54 ± 3.31days, respectively. Our present results are slightly lower than those reported by Zenebe et al. (2024), which showed values of 2676.5 ± 86.33 for Local x HF crosses in Smallholder Farmers, and Kassa et al. (2018), with values of 2305.2 ± 32.15 for Holstein Friesian Dairy Herd at ELFORA Cheffa Dairy Farm. Comparable with the current result was reported by Getahun et al. (2018) with values of 2204.05 ± 21.12 for Borena x HF crosses at Holeta Research Center dairy farm. Lower values were reported (Effa et al., 2011 , and Ashutosh et al., 2013 ) with values of 1798 ± 25 for Borena x HF crosses, 2088.7 ± 29.4 for Borena x HF crossbred in the central highlands of Ethiopia, and 1506.75 ± 71.37 for HF x local in Bangladesh, respectively. By contrast, higher LMY results for Holstein–Friesian cows, with amounts of 3349.1, 3084.0, and 3604.0, liters, were reported by Yosef et al. (2006) in Holleta dairy farms, and by Gebeyehu et al. ( 2014 ), and Wondwossen et al. (2015) at the Holleta Bull Dam, respectively. The difference might be attributed to breed/genetic makeup, management, feeding practice and climate factors in which animals were managed. Lactation milk yield was significantly (p < 0.0001) affected by genetic group. The least square mean of lactation milk yield was increased when exotic gene inheritance increased from 50% to 75% HF crosses since the management level of high-grade cows increased. The 75% F1 crossbred cows produced significantly (P < 0.05) the highest lactation milk yield. Mean lactation milk yield was significantly decreased by 25% in the F2 generation of inter-se genotype (75% F2). The findings in the present study agree with the previous reports by Million et al. (2010), which suggested upgraded crossbred cows produce higher lactation milk yield than50% crosses. However, cows need to be managed well as the level of exotic gene inheritance is continuously upgraded. Likewise, Hirooka and Bhutyan (1995) reported that a high milk yield is recorded by exotic breeds in the tropics when they are well-fed and managed, signifying that the genetic potential of an animal is partly the reflection of management. Milk yield decreased in the interim individuals in the subsequent generations, as a result of loss of heterosis due to gene segregation. Calving year markedly (P < 0.0001) affected lactation milk yield. The lactation milk yield decreased over years. The highest lactation milk yield was obtained in the year 1995–1999 (2397.85kg), while the lowest milk yield was attained in the year group 2018–2024 (Fig. 2 ). The difference could be explained by the availability of feed across years. The lactation milk yield was significantly (P < 0.0003) affected by calving season. Higher lactation milk yield was obtained in both the dry and main rainy seasons. The reason might be in the dry season, harvesting hay by bell forms and supplementing green forage in the rainy season, but milk yield was lowest in the short rainy season. Quality of feed mainly varies over seasons, which perhaps affects the performance of animals, where strategic supplementation might enhance the productivity of animals in feed shortage periods (in the rainy season). Parity had a significant (p < 0.05) effect on lactation milk yield. In this study, lactation milk yield sharply increased from first to fourth parity and became plateau towards 5th parity. Most literature supports our present finding. For instance, Goshu and Mekonnen ( 1997 ) reported similar higher milk yields for the first four lactations of the Fogera-Friesian cross around Gonder city. However, some reports reveal that lactation milk yield decreases after the third parity for crossbred cows: Mackinnon et al. ( 1996 ), Getahun et al. (2018). The gradual increase in milk yield from first to four lactations might be attributed to development of secretory tissues of the udder due to recurring pregnancies. 3.1.2. Daily Milk Yield (DMY) Daily milk yield was markedly (P < 0.05) affected by genetic group, calving year, season of calving and parity (Table 8 ). The daily milk yield increased as the exotic blood level of cows upgraded from 50% F1 to 75% F1. In line with our finding, the level of heterosis certainly enhanced in advanced generations. Though the significant difference observed in this study is not in agreement with studies by Gebregziabher et al. ( 2013 ), who reported that upgrading from 50% to higher Friesian fractions for HF X indigenous crosses has shown no significant differences in milk yields. The variation between the present and the previous study might be associated with management differences and the number of records considered in a data set. The 75% F1 produced 24 and 23.7% more daily milk yield than 50% F1 and 75% F2 generations, respectively. 50% F1 and 75% F2 genetic groups gave the same amount of milk yield/day (P > 0.05). The mean daily milk yield decreased from the 1st generation to 50%in the F2 and F3 inter mated generations. It is clear and frequently mentioned in much literature that heterosis declines in the inter progeny, which favors recombination loss during further crossing, process 3.1.3. Lactation Length The overall least square means and standard errors for lactation length in HF × Boran genetic groups are presented in Table 6 . The 75% F1 cows exhibited the longest lactation length (335 days), followed by the 50% F1 crossbred cows (329 days). The lactation lengths observed in this study are relatively close to the standard lactation length of 305 days. Previous studies, such as those by Haile et al. ( 2009 a), Kumar et al. ( 2014 ), and Dash et al. ( 2015 ), have reported similar values of 325 ± 3 days, 325.12 ± 61.28 days, 326.69 ± 2.03 days, and 326.57 ± 2.60 days, respectively. On the other hand, longer lactation lengths were reported by Suhban et al . (2000) and Effa et al. ( 2006 ), with corresponding estimates of 503.0 ± 6.36 days and 360.76 ± 6.11 days, respectively. In contrast, a shorter lactation length of 204 ± 27.8 days was reported by Djoko et al. ( 2003 ). Despite the substantial milk production associated with longer lactation lengths, the number of calves produced per cow would inevitably decrease. Several factors may contribute to variations in lactation length, including differences in breed type, nutrition, health status, and overall management practices. A balanced compromise in lactation length is essential in dairy cattle breeding practices to ensure that neither milk yield nor calf crop production is adversely affected. Lactation length exhibited a decreasing trend over time, as shown in Table 8 . Over the past 30 years, lactation length declined by approximately 28 days. Similar findings were reported in the same farm in earlier periods by Haile et al. ( 2009 a) and Effa et al. ( 2006 b). This downward trend in lactation length may be attributed to earlier drying-off practices, shifts in breeding schedules, or nutritional adjustments aimed at optimizing other performance traits. Understanding these factors is crucial for developing informed breeding and management decisions that balance milk production with overall herd reproductive efficiency. Table 4 Least square means and standard errors (LSM ± SE) of lactation milk yield, daily milk yield and lactation length Effects N LMY (lit) DMY (lit) LL (days) Overall 2564 2140.61 ± 32.92 6.89 ± 0.07 316.54 ± 3.31 CV (%) 37.92 25.89 27.23 Genetic group **** **** **** 50% F1 1665 2280.06 b ± 32.74 7.03 b ± 0.07 329.07a ± 3.28 50% F2 236 1719.34 c ± 61.99 5.85 c ± 0.13 307.85b ± 6.21 50% F3 142 1546.64 c ± 77.79 5.30 d ± 0.17 300.06b ± 7.79 75% F1 436 3030.305 a ± 52.44 9.26 a ± 0.11 335.20a ± 5.25 75% F2 85 2126.71 b ± 96.86 7.01 b ± 0.21 310.52ab ± 9.75 Year group of calving **** **** **** 1995–1999 95 2299.19 ab ± 103.54 6.19 c ± 0.22 386.34 a ± 10.37 2000–2005 569 2301.18 a ± 44.23 7.096 b ± 0.09 326.19 b ± 4.43 2006–2011 712 2162.22 b ± 43.85 7.19 ab ± 0.09 299.50 c ± 4.39 2012–2017 817 2104.517 b ± 46.16 7.37 a ± 0.10 287.75 cd ± 4.63 2018–2024 371 1835.95 c ± 53.44 6.59 c ± 0.11 282.91 d ± 5.37 Calving season group *** **** ns Dry season 1179 2214.76 a ± 37.43 7.02 a ± 0.08 321.31 ± 3.75 short rain season 703 2047.08 b ± 42.69 6.49 b ± 0.09 313.96 ± 4.28 Main rain season 682 2159.99 a ± 43.35 7.01 a ± 0.09 314.34 ± 4.35 Parity **** **** ns 1 718 1894.13 b ± 41.43 5.92 c ± 0.09 320.38 ± 4.15 2 566 2048.24 a ± 44.26 6.35 b ± 0.10 324.90 ± 4.44 3 437 2162.03 a ± 48.00 7.11 a ± 0.10 309.56 ± 4.81 4 320 2225.43 a ± 54.53 7.09 a ± 0.12 319.62 ± 5.46 5 236 2202.23 a ± 61.57 7.12 a ± 0.13 316.40 ± 6.17 6 170 2206.44 a ± 71.20 7.18 a ± 0.15 314.71 ± 7.16 7 117 2245.77 a ± 84.44 7.49 a ± 0.18 310.19 ± 8.45 LMY=Lactation Milk Yield, DMY=Daily Milk Yield, LL=Lactation Length LSM=Least Square Mean, SE=standard error, N= Number of observations*Different superscripts (a, b, c, d) in the same fixed effect indicate differences among sample means. ns = non-significant, **** highly significant. 3.2. Reproductive Traits Reproductive traits are vital for milk production, herd replacement, and the overall profitability of dairy farming. Among the various factors influencing livestock production, the reproductive performance of female animals is the most critical consideration. 3.2.1. Age at First Service (AFS) The least square means and standard errors for age at first service (AFS) are summarized in Table 9 . The overall mean AFS was 33.56 ± 0.63 months. In this study, the highest and lowest AFS values recorded were 37.01 and 27.23 months, respectively. The mean AFS observed in the current study is lower than the 36.8 ± 0.8 months reported by Gebeyehu et al. ( 2005 ) for Fogera × Holstein Friesian (HF) crosses. However, it is higher than the values reported by Haile et al. ( 2009 b) and Birhanu et al . (2014), which were 29 ± 0.7 months for Borena × HF crosses and 29.30 ± 0.21 months for Borena × HF × Jersey crosses, respectively. These variations in AFS among different studies may be attributed to differences in breed composition, environmental conditions, and management practices, all of which significantly influence reproductive performance in dairy cattle. A highly significant effect of genetic group on age at first service (AFS) was observed (P < 0.0001); (Appendix Table IV), consistent with the findings of Wssie et al . (2015). The 50% F1 group had the shortest AFS (30.33 ± 0.46 months), while the 50% F2 group exhibited the longest (37.92 ± 1.00 months), indicating a 6.5-month increase from 50% F1 to 75% F1. This suggests that traits influenced by hybrid vigour may show reduced performance in backcrosses, depending on the additive genetic merit of the parental breeds (Cunningham & Syrstad et al . (1987), Arthur et al. ( 1999 ). No significant differences (p > 0.05) were observed between 50% F2, 50% F3, and 75% F1, nor between 50% F1 and 75% F2. Additionally, calving season had no significant (p > 0, 05) effect on AFS, implying that the season of birth did not influence the onset of puberty in heifers. Nonetheless, timely breeding—aligned with periods of optimal forage quality may help achieve earlier first service through better nutritional management. 3.2.2. Age at First Calving (AFC) Age at first calving (AFC),a critical factor influencing the cost of raising dairy replacements. AFC showed a highly significant (P < 0.0001) variation across genetic groups and birth years (Table 9 ). The overall mean AFC in this study was 42.78 ± 0.63 months, aligning with findings by Damissu et al . (2013) and Wassie et al. ( 2015 ), but higher than values reported by Getahun et al . (2018), Suhban et al . (2000), and Hafez et al . (2013). Among the genetic groups, 50% F1 crosses had the lowest AFC (38.44 ± 0.46 months), while 50% F2 exhibited the highest (45.94 ± 1.05 months). No significant differences (p > 0.05) were found between 50% F2, 50% F3, and 75% F1 or between 50% F1 and 75% F2. The differences in AFC across studies may be attributed to breed composition, environmental conditions, feeding and management practices, heat detection efficiency, and health care. Additionally, animals born between 2015 and 2020 had significantly lower AFC compared to those born in 2005–2009, while birth season had no significant (p > 0.05) effect on AFC. Table 5 The least square means and standard error (LSM ± SE) of AFS and AFC Effects N AFS (month) LSM ± SE AFC (month) LSM ± SE Overall 1758 33.56 ± 0.63 42.78 ± 0.63 CV (%) 34.22 26.89 Genetic group *** *** 50% F1 828 29.17 b ± 0.59 38.44 b ± 0.59 50% F2 158 36.64 a ± 1.05 45.94 a ±1.05 50% F3 131 35.54 a ± 1.15 44.64 a ± 1.15 75% F1 515 35.67 a ± 0.82 44.93 a ± 0.82 75% F2 126 30.763 b ± 1.26 39.95 b ± 1.26 Year group of calving **** **** 1995–1999 232 34.49 b ± 0.86 43.78 b ± 0.86 2000–2004 674 33.86 b ± 0.58 43.00 bc ±0.58 2005–2009 522 37.01 a ± 0.55 46.43 a ± 0.55 2010–2014 306 35.01 ab ± 0.83 44.25 ab ±0.83 2015–2020 24 27.23 c ± 2.42 36.46 c ± 2.42 Calving season group ns ns Dry season 808 33.69 ± 0.68 42.93 ± 0.68 short rain season 582 33.33 ± 0.73 42.53 ± 0.73 Main rain season 368 33.65 ± 0.84 42.8 ± 0.84 AFS = Age at First Service, AFC = Age at First Calving, LSM=Least Square Mean, SE =Standard error 3.2.3. Calving Interval (CI) The calving interval (CI), defined as the period between successive calving, averaged 469.01 ± 7.03 days in this study, aligning closely with findings from Getahun et al . (2011), Wassie et al. ( 2015 ), Belay et al . (2014), and Getahun et al . (2018), who reported CI values ranging from 468 to 476 days for different crossbred dairy cattle in Ethiopia. However, this result was notably lower than the 612 ± 4.6 days reported by Suhban et al . (2000) for Pakistani crossbreds. Year of calving significantly influenced CI (p < 0.0001), consistent with studies by Yosef et al .(2006), Hunde et al .(2012), and Getahun et al . (2018), suggesting the impact of management practices, location, and genetic background. Interestingly, calving season did not significantly affect CI, echoing earlier reports by Million and Tadelle (2003a), Haile et al. ( 2009 b), and Belay et al . (2014) Genetic and non-genetic factors influenced the calving interval, with the except season. Among genetic groups, 50% F2 crosses had the longest CI at 507.17 ± 15.10 days, while 50% F1 crosses had the shortest at 449.12 ± 6.15 days. This variation is likely due to heterosis, recombination effects, and differences in lactation length. Parity also had a significant impact, with the longest CI observed in first parity and the shortest in fourth parity, aligning with reports by Getahun et al . (2018) and Gojam et al. ( 2016 ). The trend suggests a decrease in CI with increasing parity, potentially due to improved uterine recovery and adaptation to parturition and lactation stress as cows mature. Table 6 Least square means and standard error of (LSM ± SE) Calving Interval (CI) Effects N CI Overall 1977 469.01 ± 7.03 CV (%) 29.14 Genetic group **** 50% F1 1329 449.12 b ± 6.15 50% F2 156 507.17 a ± 15.10 50% F3 84 455.65 b ± 12.99 75% F1 304 463.23 b ± 9.94 75% F2 35 469.89 ab ± 23.49 Year group of calving **** 1995–1999 83 469.014 ab ± 18.85 2000–2005 401 471.66 ab ± 8.77 2006–2011 556 453.76 b ± 9.18 2012–2017 653 452.47 b ± 9.49 2018–2024 215 498.14 a ±11.57 Calving season group ns Dry season 900 474.62 ± 7.64 Short rain season 504 472.03 ± 8.66 Main rain season 504 460.37 ± 8.82 Parity **** 1 539 519.315 a ± 8.29 2 441 493.69 ab ± 8.62 3 325 462.54 bc ± 9.39 4 244 451.11 c ± 10.40 5 173 451.91 c ± 11.95 6 119 464.51 c ± 13.75 7 67 439.98c ± 17.94 N=number of observation CI=Calving Interval, LSM= Least Square Mean, SE=Standard error 3.3. Estimation of Crossbreeding Parameters 3.3.1. Crossbreeding parameter estimates for production traits Estimates of individual breed additive, individual heterosis and individual recombination for lactation milk yield, daily milk yield and lactation length are shown in Table 11. Individual breed additive effects were large and highly significant ( p < 0.001) for lactation milk yield and daily milk yield, but not significant for lactation length. Relative to the mean value of the Borena cows, the individual additive contribution of Holstein Friesian cows was 3728 ± 139.39 for lactation milk yield, 10.89 ± 0.30 daily milk yields and 48.95 ± 14.77 days for lactation length. Individual heterosis effect was not significant for lactation milk yield, daily milk yield and lactation length. The breed additive difference for lactation milk yield in the present study (Table 11) was higher than the breed additive difference of 2674.05 kg reported for crosses between Boran x HF in the central highlands of Ethiopia reported by Getahun et al . (2018) and breed additive difference of 2220 kg lactation milk yield for crosses between Holstein Friesian and Barca breed at Debre Zeit Agricultural Research Center in Ethiopia Tadesse et al. ( 2010 ). The estimate of individual heterosis with respect to Holstein Friesian and Borena breed genes was negative − 81.65 ± 97.98, positive 0.44 ± 0.21and negative (-18.72 ± 10.38) for lactation milk yield, daily milk yield and lactation length, respectively (Table 11). Non-significant and negative heterosis estimates by Tadese et al., (2019) reported estimates of average lactation milk yield in crossbreeding between Boran and HF. The current study on the estimate of individual heterosis slightly differs from the value reported by Million et al. (2019), which was − 54.08 ± 195 kg for milk yield in the crossbreeding of Boran cattle with Holstein Friesians in the central highlands of Ethiopia. The current study aligns with the findings of Getahun et al. (2018), who reported a significant negative effect of recombination on lactation milk yield (LMY), daily milk yield (DMY), and lactation length (LL). A small negative and highly significant individual heterosis for lactation milk yield (LMY) was reported by Abolfazl et al. (2012) in Iran for Holstein Friesian crosses with local breeds. In contrast, Hunde et al. ( 2022 ) reported a significant and higher negative individual heterosis value of 150.6 ± 76. Differences in heterosis estimates between crossbred cattle can arise from various factors, including the genetic diversity of the parent breeds, the specific traits being measured, and environmental influences. Additionally, non-additive gene effects and the degree of genetic compatibility between the breeds can significantly impact heterosis outcomes. The Friesian breed has undergone selection for many generations primarily to enhance milk production per cow. Consequently, beneficial epistatic relationships among genes across various loci may have evolved. Therefore, when the Friesian is crossed with the unselected Borena, these interactions could be disrupted due to recombination. Table 7 Crossbreeding parameters estimates and their associated standard errors for productive traits. Crossbreeding parameters LMY DMY LL Breed additive genetic 3728 ± 139.39*** 10.89 ± 0.3*** 48.95 ± 14.77 ns Individual heterosis -81.65 ± 97.98 ns 0.44 ± 0.21 ns -18.72 ± 10.38 ns Individual recombination -1440.92 ± 152.25*** -2.55 ± 0.33*** − 75.15 ± 16.13*** LMY=lactation milk yield, DMY=daily milk yield, LL=lactation length ns: non-significant; ** significant ; *** highly significant 3.3.2. Crossbreeding parameter estimates for reproduction traits Genetic parameter estimates of the individual additive, heterosis and recombination effect on reproductive traits in the present study are summarized in Table 12. The additive effect of individuals had no significant negative impact on age at first service (AFS), age at first calving (AFC), and calving interval (CI) traits. Getahun et al. (2018) reported a significant negative estimate of -373.9 ± 114.8, -337.1 ± 112.6 days for AFS and AFC traits, respectively. The genetic estimate for individual heterosis in the current study was a small significant negative and desirable for AFC and a non-significant negative effect for AFS. This result was similar to the findings of Hunde et al., ( 2022 ) and Getahun et al., (2018). However, CI was positive and non-significant. A significant estimate was reported by Demeke et al. ( 2004 b) (-3.5months, -50 days for AFC and CI, respectively. The recombination effect in the current study was non-significant and positive for AFS, AFC and CI traits. Friesian crosses with Borena about 2.95 ± 3.15 months and 2.91 ± 3.15 months delay in AFS and AFC, which could be attributed to the recombination loss. Table 8 Crossbreeding parameters estimates and their standard errors for reproductive traits. Crossbreeding parameters CI AFS AFC Breed additive genetic -21.51 ± 29.19 ns -2.29 ± 3.12 ns -2.23 ± 3.12 ns Individual heterosis 5.33 ± 19.79 ns -8. 79 ± 2.69 ns -8. 84 ± 2.71* Individual recombination 81.01 ± 32.71 ns 2.95 ± 3.15 ns 2.91 ± 3.15 ns CI=calving interval AFS = age at first service AFC = age at first calvingns: non-significant; ** significant ; *** highly significant. 3.4. Estimates of Genetic and Phenotypic Parameters 3.4.1. Estimates of variance components and heritability 3.4.1.1. Heritability and variance component of productive traits Heritability is important among several factors determining how much genetic improvement can be made in any trait, Haile, et al. (2006). In tropical and subtropical regions, disease and feed have greater effects on the performance of the animal. As a result, heritability might be low, Dechow et al., ( 2001 ) and Wasike et al., ( 2006 ). Variance component heritability (h2), repeatability (r) and permanent environmental effects (Vc) of productive traits are presented in Table 13. The current study was presented that the productive traits of heritability LMY (0.180 ± 1.00), DMY (0.235 ± 0.053), and LL (0.219 ± 0.077) and repeatability 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. The current study is similar, with value corroborated by Demeke et al. ( 2004 a) for various crossbred breeding in Ethiopia and 0.18. Ashutosh et al. ( 2013 ) reported comparable value for Holstein X Sahiwal crossbred cattle. However, the present heritability estimate is lower than that reported by Getahun et al. (2018) for Holstein Friesian × Boran crosses and Gebregziabher et al. ( 2013 ), which were 0.25 ± -1 and 0.30 ± 0.04 for multi-breed cattle, respectively. In contrast, Tadesse et al. (2014) reported a higher value of 0.57 ± 0.02 for Ethiopian Holstein Friesian × Boran crosses. The wide variation among these studies might result from the type of model used for the analysis and number of records. Daily milk yield (DMY): The heritability estimate from this study was 0.24 ± 0.05. This result is comparable 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 Gebregziabher et al. ( 2014 ) at 0.26 ± 0.08 for various crossbreds. In contrast, lower estimates were reported by Demeke et al. ( 2004 a) 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 Tadesse et al. (2014) at 0.52 ± 0.02 for Ethiopian Holstein Friesian × Borena crosses. Lactation length (LL): The heritability estimate was 0.22 ± 0.1, which is comparable to the previous findings of Haile et al. ( 2009 a) for Ethiopian Boran × Holstein Friesian (HF) crosses (0.26 ± 0.03) and Tadesse 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. ( 2009 a) 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. Table 9 Estimate of 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. 3.4.1.2. Estimation of heritability for reproductive traits Estimation of for variance component, heritability (h 2 ) and repeatability (r) for AFS, AFC and CI are showed in Table 10 . The current finding has shown that heritability values of reproductive traits were low. Age at first service (AFS): The heritability estimate for AFS was 0.079 ± 0.034, which is consistent with the findings of Beneberu et al. ( 2020 ), who reported a value of 0.05 ± 0.08 for pure Jersey breed. This result is significantly lower than the estimates reported by Getahun et al. (2018) at 0.22 ± 0.08 for Holstein Friesian × Boran crosses and Belay et al. (2014) at 0.26 for Fogera × Holstein Friesian crosses. Conversely, higher heritability values were reported by Haile et al. ( 2009 b) at 0.61 ± 0.15 for Boran × Holstein Friesian crosses and Berhanu and Ashim (2014) at 0.51 ± 0.10 for Ethiopian Boran × Holstein Friesian crosses. Age at first calving (AFC): 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. ( 2009 b) 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. Calving interval (CI): The heritability estimate obtained in the present study was 0.180 ± 0.042. This result is comparable 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 Mohamed 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 the sample size used, genetic group/breed and analysis methods of Assemu et al. (2015). Table 10 Estimate of 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. 3.4.2. Estimation of repeatability (r) for productive traits The repeatability estimates for productive traits of 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 10 ). 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. ( 2009 a) for Holstein Friesian × Boran crosses. The repeatability estimate for daily milk yield (DMY) in the present study was 0.46 ± 0.02, aligning with Getahun et al. (2018) for Holstein Friesian × Boran crosses, and exceeding the 0.334 reported by Ghorbani et al. ( 2011 ) for Iranian Holstein Friesian crosses. However, lower repeatability values of 0.30 ± 0.02 were documented by Demeke et al. ( 2004 b) for Holstein Friesian × Boran and Jersey × Boran crosses. Repeatability for lactation length observed in this study approximated 0.70 as reported by Haile et al. ( 2009 a) for Holstein Friesian × Boran crosses and was higher than the 0.23 ± 0.02 value 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 Million 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. 3.4.3. 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 15. 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. 3.4.3.1. Genetic correlations The genetic correlation between productive traits was positive, and the coefficients ranged from weak (0.14 ± 0.07) to very strong (0.93 ± 0.03). A high correlation was 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 ) showed 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 was obtained in the work of Ashutosh 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), and AFC-CI (0.063 ± 0.02). In agreement with this finding, Belay et al.(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 was seen between CI-LL (0.785 ± 0.074), and 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 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 between AFC-DMY (-0.206 ± 0.072) and AFS-LL (-0.020 ± 0.078) is 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 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 indicate that as one trait increases, the other trait tends to decrease, which might be favorable or unfavorable depending on the combination of traits considered. 3.4.3.2. 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 Table.15, was positive and 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 had positive and negative phenotypic correlation of these traits strongly disagree with the findings of Belay ( 2014 ) who found very strong phenotypic correlation between AFS and AFC (0.85463). The current studies vary from others. It might be due to breed, number of observations studied and software procedure used for analysis. The phenotypic correlation between productive and reproductive traits ranged from moderate positive to negative values. The present study lactation length saw a negative phenotypic correlation between AFS and AFC. However, a positive correlation was shown 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 is similar to 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 is similar to the findings of Getahun et al. (2018) and Ashutosh et al. ( 2013 ). AFS = Age at First Service, AFC = Age at First Calving, CI=Calving Interval, LMY=Lactation Milk Yield, DMY=Daily Milk Yield, LL=Lactation Length. 3.4.4. Estimates of Breeding Values The total genetic ability of an animal for a given trait is called breeding value (BV). Estimates of breeding value mention the value of an animal's additive genetic effects in the breeding programm for a specific trait of interest. Breeding values were estimated for reproductive and production traits of HF crosses with different genotypes at Holeta Research Center, since the trait of interest at the center is to improve the reproductive and milk production performance of the breed through improving their genetic performance. 3.4.4.1. Breeding value estimates of cows for productive traits Average breeding values for the estimated lactation milk yield (21.89 liters), daily milk yield (0.060 liters), and lactation length (0.06 days) were summarized (Fig. 2 , 3 , 4 ) respectively. Average estimated breeding values across the year of birth group for milk production traits vary across birth year groups. The highest estimated average breeding value (182.97 liters) for lactation milk yield was observed in the 2000–2005 year whereas the lowest estimated average breeding value (-159.45) was observed in 1995–1999. The genetic trend for lactation milk yield from the year 2000–2005 in this study showed an increasing trend. The average estimated breeding value for daily milk yield ranged from − 0.105 to 0.1851 Liters, whereas the average estimated breeding value for lactation length was between − 0.018 and 0.093 days. The declining trend indicates inefficiency in selection and proper culling of unproductive cows for milk production traits and also implies that the inefficiency in selection methods based on phenotypic performance. The positive improvements in genetic trends for lactation milk yield, daily milk yield and lactation length traits in some year groups might be due to better management, feed availability, number of observations and proper selection of cows based on their phenotypic performance. Upward and downward trends of breeding values for milk production traits across year groups might be due to the presence of high and low producing cows with the absence of culling, environmental stresses (heat stress, low quality and quantity of feed), inefficiency in selection methods based on phenotypic performance. 3.4.4.2. Breeding value estimates of cows for reproductive traits The current study showed that the average estimated breeding values for AFS (-0.012 months with AFC (-0.017 months) Figure (5), and CI (-1.565 days) Figure (7) were summarized. The current study showed a negative trend for reproductive traits (AFS, AFC and CI). It might be efficient breeding programs, efficiency in selection methods based on phenotypic performance and proper culling of unproductive cows. 4. CONCLUSIONS The current study was specifically designed to estimate the performance of Friesian and Borena crossbreds with various genotypes in a synthetic breed developing at Holeta Agricultural Research Center. With this regards during the period 1995-2024 were used for this study. Genetic and non-genetic factors influenced the productive and reproductive performance of Friesian and Borena crossbreeds. Season of birth group, and season of calving group, had no significant effect on reproductive traits (AFS, AFC, and CI) and productive traits (DMY and LL). However, the season of calving group had a significant effect on LMY. The other significant variations were found in the measured reproductive and productive traits between genetic groups, birth year groups, calving year groups, and parity. This suggests that strict selection and improved management choices can result in a notable improvement. First-generation crosses produced more milk than second-generation crosses. Performance among 50% F1, F2, and F3, and 75%F1, and F2 significantly declined, which indicated the importance of retaining heterosis. 75%of F1 had produced superior milk per lactation and was the breed of choice for milk production trait compared with other genetic groups. Among 75% of crosses, mean lactation milk yield was significantly decreased by 25% for the inter-se mating breed group (75% females mated with 75% males) compared to 75% F1 or first generation. The higher milk yield of first generation (50% F1 and 75% F1) crosses from its contemporary group was also associated with longer lactation length. The 50% F1 genetic group was also characterized by better reproductive performance and the 75% F1 cross was poor in reproductive and high productive traits. As well as, the 50%of the second and third generation were belonged with their poor reproductive performances. It might be the breakdown of hybrid vigor occurs when gene interactions (epistasis) that benefited the F1 generation are disrupted in the F2 or F3 generations, leading to poorer performance and recombination loss. The year and parity significantly influenced reproductive and productive traits due to the unpredictable changes in the environment, management practices, herd structure, and lactation stages that occurred annually. Trend analysis has shown that the highest milk yield was recorded at the station in the year from 2000-2005. The non-significant heterosis effect of crossbred for lactation, milk yield, and daily milk yield in the current study showed that the higher milk yield of these crosses was only due to the breed additive of the Friesian sire gene, and the recombination loss was significant in all productive traits except lactation length. This might be due to unfavorable epistatic allele interaction. While the individual recombination effect was significant, it had an unfavorable, large, and negative impact on lactation milk yield, daily milk yield, and lactation length. In contrast, the individual breed additive effect of these three traits was more important than the individual heterosis effect. Except AFC on heterosis, all reproductive traits in the present study indicated that the negative value and non-significant on the additive, heterosis, and recombination due to the additive effect of the Friesian gene. The values of heritability and repeatability for both productive and reproductive traits, were none zero and ranged from low to higher. The 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 a 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 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 the traits that are correlated with one another. The phenotypic correlation between productive traits ranges from very weak to strong correlation. A strong phenotypic correlation was observed between LL and LMY. However, a negative correlation was observed among AFC and LL. Upward and downward trends of breeding values for milk production traits across year groups might be due to the presence of high and low producing cows with the absence of culling, environmental stresses (heat stress, low quality and quantity of feed), inefficiency in selection methods based on phenotypic performance. Based on our study, improved management is required to minimize the genetic recombination effect on lactation milk yield and daily milk yield. Continuous selection is important among crossbred animals to exploit the advantage of heterosis in inter crossbreds. More data are required to evaluate subsequent generations in the upcoming periods to produce a composite breed. • It is vital to consider a huge segregated population with sufficient genetic variability, avoiding any possibility of increasing the rate of inbreeding. Abbreviations AFC Age at First Calving AFS Age at First Service AI Artificial Insemination AMY Annual Milk Yield BV Breeding Value CI Calving Interval CSA Central Statistical Authority CV Coefficient of Variation DMY Daily Milk Yield EARO Ethiopian Agricultural Research Organization EBV Estimated Breeding Value F1 First Generation Crossbred F2 Second Generation Crossbred F3 Third Generation Crossbred FAO Food and Agriculture Organization of the United Nation GLM Generalized Linear Model HARC Holeta Agricultural Research Center HF Holstein Friesian ID Individual Identity LL Lactation Length LDI Livestock Development Institute LMY Lactation Milk Yield LSM Least Square Means MASL Meter Above Sea Level SAS Statistical Analysis System Declarations Acknowledgment The Almighty God's love, kindness, and faithfulness in providing health, patience, strength, and safety during the study period are very greatly appreciated. Throughout the study period, from the writing of the proposal to the completion of this thesis work, I would like to sincerely thank Dr.Haile Welearegay (Major Advisor) Dr. Zewdie Wondatir (co-advisor) for their helpful criticism, suggestions, encouragement and data analysis. Author 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 This study was funded by the Ethiopian Institute of Agricultural Research (EIAR). Data availability The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate This study did not require official or institutional ethical approval. Consent for publication Not applicable. 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Universite de Liege (Belgium), Vanholder, T., Leroy, J., Dewulf, J., Duchateau, L., Coryn, M., de Kruif, A., & Opsomer, G. (2005). Hormonal and metabolic profiles of high‐yielding dairy cows prior to ovarian cyst formation or first ovulation post partum. Reproduction in Domestic Animals, 40 (5), 460-467. 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. Wassie, T., Mekuriaw, G., & Mekuriaw, Z. (2015). Reproductive performance for Holstein Friesian× Arsi and Holstein Friesian× Boran crossbred cattle. Wondifraw, Z., Thombre, B., & Bainwad, D. (2013). Effect of non-genetic factors on milk production of Holstein Friesian× Deoni crossbred cows. International Journal of Livestock Production, 4 (7), 106-112. Yohannes, G., 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. 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. Yusuf, M. (2020). Reproductive performance of dairy cows in a smallholder farm. Paper presented at the IOP Conference Series: Earth and Environmental Science. 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. Zenebe, T. (2024). Evaluating Synergies of Genetic and Non-Genetic Intervention on reproductive and Productive Performance of Crossbred Dairy Cows under Smallholder Farmers Condition in Selected Milk Shed areas of Ethiopia. Zewdu Wondifraw, Z. W., Thombre, B., & Bainwad, D. (2013). Effect of non-genetic factors on milk production of Holstein Friesian× Deoni crossbred cows. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8744363","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":588316301,"identity":"5b0e2a4b-8cba-4f5f-a366-b047374c5f8a","order_by":0,"name":"Asamenew 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University","correspondingAuthor":false,"prefix":"","firstName":"Haile","middleName":"","lastName":"Welearegay","suffix":""},{"id":588316303,"identity":"3138a06a-9af7-40f6-976f-7de1ad5157a2","order_by":2,"name":"Zewdie Wondatir","email":"","orcid":"","institution":"Ethiopian Institute of Agricultural Research","correspondingAuthor":false,"prefix":"","firstName":"Zewdie","middleName":"","lastName":"Wondatir","suffix":""}],"badges":[],"createdAt":"2026-01-30 19:08:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8744363/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8744363/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102730666,"identity":"4eb60e00-b19b-410b-b8c5-e244e6c04941","added_by":"auto","created_at":"2026-02-16 04:49:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113666,"visible":true,"origin":"","legend":"\u003cp\u003eMap of study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8744363/v1/c6fccecd126c86ad5e47fc59.png"},{"id":102748796,"identity":"ba84c2c0-9bac-496e-9e77-872ba18544df","added_by":"auto","created_at":"2026-02-16 09:11:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137799,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic presentation of breeding program at Holeta agricultural research center\u003c/p\u003e\n\u003cp\u003e(Source: Adopted by Philipsson (2011).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8744363/v1/be5358f76fd44c1610190ac1.png"},{"id":102748866,"identity":"a02c2dc3-96aa-4844-aaba-82550887d785","added_by":"auto","created_at":"2026-02-16 09:11:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":142310,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenetic Correlations\u003c/strong\u003e (r \u003csub\u003eg\u003c/sub\u003e) and one for the \u003cstrong\u003ePhenotypic Correlations\u003c/strong\u003e (r \u003csub\u003ep\u003c/sub\u003e).\u003c/p\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.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8744363/v1/d77d91e73baa30fd569804f5.png"},{"id":102730669,"identity":"69a47dba-8021-4fa4-a16f-82e1cb6d25ed","added_by":"auto","created_at":"2026-02-16 04:49:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51057,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated genetic trend of lactation milk yield (LMY) based on birth year group\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8744363/v1/adf843c1296ad8f1badc9a93.png"},{"id":102730673,"identity":"0337f810-77c0-4713-939c-9963cb60c155","added_by":"auto","created_at":"2026-02-16 04:49:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":51115,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated genetic trend of daily milk yield (DMY) based on birth year group\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8744363/v1/1499f3fed90d8fc579e108ee.png"},{"id":102730672,"identity":"db0d9790-fe88-46c2-a631-7c4619c8a74f","added_by":"auto","created_at":"2026-02-16 04:49:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":40788,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated genetic trend of lactation length (LL) based on birth year group\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8744363/v1/5ff4106c67864c5a74bf1355.png"},{"id":102749350,"identity":"c1d2a5d7-abd2-4cf8-9ef9-3f4c4bf0bee6","added_by":"auto","created_at":"2026-02-16 09:12:27","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":72314,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated genetic trends of age at first service (AFS) and age at first calving (AFC) based birth year group\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8744363/v1/9d81808235bd87ac6266f344.png"},{"id":102730671,"identity":"5f10c26b-85f9-42df-910f-de9ec1074c3b","added_by":"auto","created_at":"2026-02-16 04:49:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":42767,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated genetic trend of calving interval (CI) based on birth year group\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8744363/v1/92de4a77a7676580ed43be10.png"},{"id":103056455,"identity":"cf557da4-66ab-4ac1-ae68-e4c98d3a8e3c","added_by":"auto","created_at":"2026-02-20 09:10:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2544908,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8744363/v1/5a64fd2d-f819-472e-95b4-bbfdc8a9bd07.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimation of genetic and phenotypic parameters for milk production and reproduction traits in a developing synthetic dairy cattle breed at Holeta Agricultural Research Center, Ethiopia","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eGlobally, there are approximately 1.5\u0026nbsp;billion cattle and 1000 recognized breeds (FAO, 2020). Ethiopia alone is home to more than 30 cattle breeds, with a national cattle population of 66\u0026nbsp;million (CSA, 2022). However, the cattle population in Ethiopia is predominantly composed of indigenous breeds, which are mainly characterized by low milk yield (1.48 kg) and shorter lactation length (approximately 7 months) (CSA, 2022. Since the 1950s, crossbreeding has been practiced in Ethiopia to improve milk production. Among the exotic cattle breeds, germplasm of Holstein Friesian, Jersey and Semental have been imported for crossbreeding with the local breeds (Kebede, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). According to recent reports (CSA, 2022), the proportion of crossbred cattle in the country does not exceed 3%. Currently, the Holstein Friesian and Jersey breeds are mainly utilized for crossbreeding purposes, Tesema et al. (\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In tropical regions, this kind of crossbreeding has been frequently employed to combine the high milk production of Bos Taurus breeds and resistance to disease, heat stress, adaptation to poor management, and survival to low-quality feeds of indigenous breeds. The first filial generation (F1) of these crosses has a significant advantage in total output due to heterosis impacts on milk yield and reproduction traits, in addition to the additive contribution of each breed to overall performance, Mackinnon et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Nevertheless, additional improvement through backcrossing to the European breed produced inconsistent and frequently unsatisfactory results. According to Schuler et al. (2001), a number of factors influence crossbred offspring, namely direct effects, parental effects, heterosis effects like dominance and epistasis, recombination loss, and implications for combination appropriateness.\u003c/p\u003e \u003cp\u003eThese effects, which have been theoretically outlined by Hill et al. (1971), rely on the breeds involved and the characteristics of interest. When there are significant trait-related variations between the parent breeds, heterosis effects are likely to be significant. For example, among dairy cow breeds, Jersey has the greatest genetic distance from Holstein-Friesian (HF) (Basedow, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). A detailed review of crossbreeding schemes for dairy cattle and of heterosis estimates is reported in the literature (Sorensen et al. 2008). It is noted that heterosis effects are not heritable additions accompanying the combined additive effects as a bonus of a cross and decreasing in advanced generations of crosses between two breeds. On the other hand, rotation crosses result in a cyclical gene composition from generation to generation, Sch\u0026uuml;ler et al. (\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Two breed rotations maintain 67% of F1 heterosis at equilibrium and 3 breed rotational crosses maintain 86% of F1 heterosis. A decrease in milk production from F1 to F2 (and F3) was noted by Littlewood (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e1933\u003c/span\u003e). However, this was attributed to gene segregation or loss of heterosis. Ayrshire and Friesian F2 crossings with zebu breeds (Sahiwal, Red Sindhi, and Hariana) yielded 30\u0026ndash;35% less milk than F1 crosses (Kartha's, 1934). In a review of dairy cattle cross-breeding experiments in the tropics, Syrstad (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) concluded that most of the decline in the productivity from F1 to F2 generations was due to loss of heterozygosity, i.e. dominance effects were the most important contributor to heterosis, with perhaps a small negative effect of recombination on milk yield. Milk yield increased only slightly, or even declined, fertility deteriorated and mortality increased. The lack of adaptation to tropical conditions was obvious. Breeding strategies aiming at the economical use of genetic resources require information on breed and cross-breed performance, including estimates of within and between-population genetic factors (Dickerson, 1969). Nevertheless, these estimations differ significantly among breeds, production systems, estimation techniques, etc. (Kahi et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Lobo et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). A crossbreeding program can be optimized by using the best breed combinations and breeding systems to maximize heterosis. Separating the additive and non-additive contributions and dividing the latter into within-locus (dominance) and between-locus (epistatic) components are the challenges in crossbreeding (Tadesse and Tadelle, 2003). Even though Ethiopia has been upgrading or crossbreeding native cattle with exotic breeds for the past 50 years or more, breed combinations are optimized for dominance and additive genetic contribution. Selecting the best crossbreeding method for the country's milk production is still hindered by the absence of reliable estimations of crossbreeding parameters.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Statement of the research problem\u003c/h2\u003e \u003cp\u003eDairy cattle production in Ethiopia is a vital component of the country's agriculture, contributing significantly to the national economy. However, local breeds have limited genetic potential for milk production and reproductive performance, which hampers the growth of the dairy cattle industry. To address this, upgrading the genetic potential of local breeds through crossbreeding with exotic dairy cattle breeds, developing synthetic breeds, and promoting good management practices are essential strategies to enhance productivity and promote sustainable dairy cattle farming in Ethiopia.\u003c/p\u003e \u003cp\u003eA dairy research at Holetta agricultural research center has been conducting crossbreeding experiments for five decades and evaluating different crossbreds of different exotic gene inheritance. As a result, researchers at Holetta research center initiated a synthetic breed development program a few years ago. Developing synthetic breeds of dairy cattle in Ethiopia hold greater long-term importance than traditional crossbreeding programs. Crossbreeding is mating local cows with exotic dairy breeds to improve milk yields, which often lacks consistency and sustainability. This is due to uncontrolled breeding practices and a lack of adaptability in successive generations. In contrast, synthetic breeds are developed through systematic breeding over several generations to combine the high milk production traits of exotic breeds with the hardiness, disease resistance, and environmental adaptability of indigenous Ethiopian cattle. This approach results in a more stable and uniform breed population that can thrive in local conditions while maintaining improved productivity. Moreover, synthetic breeds reduce reliance on the continuous importation of exotic genetics, lowering costs and preserving national biosecurity. Therefore, the current study estimated the genetic and phenotypic parameters for productive and reproductive traits of the ongoing synthetic dairy cattle breeding program at HARC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.3. Objectives\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e1.3.1. General objective\u003c/h2\u003e \u003cp\u003eTo assess the genetic and non-genetic factors influencing productive and reproductive traits in an ongoing synthetic breed development program, with the aim of improving breeding strategies through the estimation of genetic and crossbreeding parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e1.3.2. Specific Objectives\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo estimate genetic parameters for productive and reproductive traits in an ongoing synthetic breed development program at Holetta Agricultural Research Center.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEvaluate the effect of non genetic factors affecting productive and reproductive performance traits in an ongoing synthetic breed development program at Holetta Agricultural Research Center.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo estimate crossbreeding parameters of productive and reproductive traits of an ongoing synthetic breed development program at Holetta Agricultural Research Center.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"2. MATERIALS AND MHODS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Description of the Study Area\u003c/h2\u003e \u003cp\u003eThe study was conducted at Holetta Agricultural Research Center (HARC). It is located at 9\u0026deg;30' N and 380 30' E and 35 km west of Addis Ababa on the way to Ambo. The topography where the center is located consists of a section of central Ethiopia, which represents a cool tropical highland area that covers about 30% of the land mass of Ethiopia and more than 70% of the population of the country. In the area where the center is located, the topography can be expressed by the existence of some scattered hills and mountains ranging between altitudes of 2250 m to 2500m above sea level. The area receives an average annual rainfall of around 1,200 mm. The region's average yearly temperature is 18\u0026deg;C, with relative humidity averaging 60% throughout the year.\u003c/p\u003e \u003cp\u003eFigure 1: Map of study area\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Source and Data Collection\u003c/h2\u003e \u003cp\u003eData recorded from 1995 through 2024.at Holetta Agricultural Research Center (HARC) were used for this study. The Borana cow breed was used as a foundation stock to produce 50% F1 crosses. Whereas, 50%of F1, F2 and F3, 75%F1, and 75%F2 were different genotypes resulting from subsequent crossing. The following data were recorded in a database:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIdentification number of animals\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDate of birth\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDate of first service\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDate of First calving\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDaily milk yield,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003elactation length and\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003elactation milk yield\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003egenetic group\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSire of a cow and dam of a cow.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Animal Management\u003c/h2\u003e \u003cp\u003e The animals were managed according to 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 daytime, animals were allowed to graze starting from early morning until evening. A concentrate mixture composed of wheat bran (64%), noug (Guizocia abyssinica) cake (35%), and salt (1%) was supplemented for heifers up to 2 years of age, 69% wheat bran 39% noug cake, and salt 1% for pregnant cows and calves supplemented with concentrate mixture at a rate of 0.25-1 kg per day/animal. The animals had free access to clean tap water. Calves were permitted to suckle their dam immediately after birth for approximately four days to ensure they received sufficient colostrum. Weighting and ear tagging are applied within 24 hours after birth. After four days, they were moved to a calf rearing pen and provided with a dry diet and whole milk. Over 98 days, 260 kg of whole milk was administered via bucket feeding for the F1 calves which suckled their dams until weaning. Weaned calves were transferred to another pen and kept indoors until 6 months of age.\u003c/p\u003e \u003cp\u003eCows have been milked with a milking machine twice daily (early morning and evening) since 2002. Since 2005, animal selection has been conducted through estimated breeding value and physical appearance. All herds on the research farm were vaccinated for major transmittable diseases (Anthrax, Blackleg, Foot and Mouth Disease (FMD), and Lamp Skin Disease (LSD) and were vaccinated on a regular schedule. Management system (feeding) of the herd might vary with seasons, depending on availability of feed and other inputs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Overview of Dairy Cattle Research at Holeta Agricultural Research Center\u003c/h2\u003e \u003cp\u003eHoletta Research Center was established in 1966. The dairy cattle research started two years later after the establishment of the center. In the beginning, preliminary characterization and milk production and reproductive performances of selected indigenous cattle breeds were evaluated at four experimental stations (Holetta, Horo, Melka-Werer and Adamitulu). Indigenous breeds such as Begait, Borana and Horro were evaluated for milk production. As a result, these indigenous breeds produced an overall total lactation yield of 550 kg over a lactation period of 6 months. However, due to the lower milk yield of indigenous cows and high demand for milk and milk products associated with alarming human population growth, crossbreeding was proposed in 1972 by G. Winner FAO consultant.\u003c/p\u003e \u003cp\u003eThe first preliminary results of the long-term dairy cattle crossbreeding experiments in Ethiopia were reported by Sendros, (1987), 20 years after the start of the experiment. The results indicated that first generation (F1) crossbred dairy cows in general produce three to five times more milk than indigenous cows. Kebede, (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) conducted a comprehensive study and identified milk production as one of the breeding program's target goals, achieving significant success.\u003c/p\u003e \u003cp\u003eThe F1 (50%) and \u0026frac34; crosses produced a 3 to 5-fold higher daily milk yield than their contemporary local cow groups. The F2 and F3 genotypes resulting from the inter se mating, however, produced somewhat lower milk yield due to loss of hybrid vigor compared to \u0026frac12; F1 and \u0026frac34; F1 crosses. Moreover, it was also concluded that Jersey crosses produce higher milk yield per metabolic body weight than Friesian and Simmental crosses, reflecting the higher efficiency of Jersey crosses for milk production under low-input, low-output dairy production systems. Likewise, crossbred calves were found to have a higher birth weight and growth rate to reach puberty earlier than compared to local calves.\u003c/p\u003e \u003cp\u003eThe second approach was the extension of the conclusive results obtained from the first national dairy cattle crossbreeding program. The aim of this approach was to develop a 50% synthetic dairy breed around the milk sheds of Addis Ababa with nucleus herd planned to be established at Holetta Agricultural Research Center. A community-based open nucleus breeding scheme was suggested and the program was designed to span over a period of 10 years (1995\u0026ndash;2005). However, this strategy was unsuccessful and finally failed due to absence of on-farm dairy cattle performance recording schemes.\u003c/p\u003e \u003cp\u003eDue to fluctuations in exotic gene inheritance among crossbreds animals produced as a result of crossing and lack of an appropriate breeding program, efforts are underway to develop a 75% synthetic/composite dairy breed at Holeta Agricultural Research Center (HARC). The current ongoing synthetic breed development program has already attained fourth generation. Subsequent crossing will be continued until the 9th generation is achieved, where gene segregation will be fixed and stabilized at this stage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Mating Design\u003c/h2\u003e \u003cp\u003eThe Borana cattle used as a foundation stock for crossbreeding were brought from Borena pastoralists in southern Ethiopia. Pure Borena dams mated with pure Holstein Friesian (HF) semen to produce 50% F1 crosses while the 50% F1 is back crossed with pure Friesian semen to produce the 75% (HF X Borana) first generation. The later generations (F2 and F3) were produced by \u003cem\u003einter se\u003c/em\u003e mating of 75% (HF X Borana) males with 75% (HF X Borana) females to produce a synthetic breed of 75% HF and 25% Borena gene inheritance. The mating design used to produce synthetic breed in the farm is indicated in Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(Source: Adopted by Philipsson (2011).\u003c/p\u003e \u003cp\u003eMating was undertaken throughout the year using artificial insemination. Semen of 75% (HF X Borana) was sourced from NAIC, Kality, Addis Ababa. In cases where cows/heifers became repeat breeders following AI, natural service was occasionally used. Bulls born on the farm were selected for breeding based on the milk performance of their dams and physical conformation. These selected bulls were used for on-station breeding activities after their semen was collected and evaluated in collaboration with NAIC. A careful attention was paid to avoiding genetic-relatedness during bull selection. Heat detection in cows was carried out daily by herd attendants and a teaser bull kept together with the female herd. Cows exhibiting standing heat were artificially inseminated by qualified technicians. Cows that did not exhibit signs of heat after service were diagnosed for pregnancy at 45 days post-insemination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Data management\u003c/h2\u003e \u003cp\u003eData cleaning was done to avoid too many missed observations, outliers and incorrectly recorded observations. Lactation lengths less than 100 days were eliminated from the data set. Age at first service (AFS) below 10 months and above 80 months, as well as age at first calving (AFC) below 20 months and above 90 months, were removed from the data set. Parities above 7 were few and grouped as parity 7.\u003c/p\u003e \u003cp\u003eThe average gestation time for cows is 285 days, and they have a 45-day voluntary waiting period (330 days CI) following calving. Cows with a calving interval (CI) of less than 330 days were eliminated from the analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRecords used for analysis of genetic and crossbreeding parameters for different trait\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMilk production Traits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eReproduction Traits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLMY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDMY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure Boran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% F1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% F2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% F3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75% F1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75% F2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14,028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eLMY=Lactation Milk Yield, DMY=Daily Milk Yield, LL=Lactation Length, CI=Calving Interval, AFS\u0026thinsp;=\u0026thinsp;Age At First Service, AFC\u0026thinsp;=\u0026thinsp;Age At First Service\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of observations in pedigree records\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePedigree data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of animals with unknown sire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of animals with unknown dam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of animals with both parents unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of sires\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of animals with paternal grandsire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of animals with paternal grand dam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eN=Number of observations\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Method of Data Analysis\u003c/h2\u003e \u003cp\u003eData analysis on non-genetic factors was performed using SAS software (SAS, 2004). A general linear model (GLM) of this software was employed to the effect of genetic group, parity, season and year-on-age at first service (AFS), age at first calving (AFC), calving interval (CI), lactation milk yield (LMY), daily milk yield (DMY), and lactation length (LL).\u003c/p\u003e \u003cp\u003eThe genetic groups considered in the model were 50% HFXB and 75% HFXB). No selection and improvement has been undertaken on the Borena breed since it has only been used as a dam line for F1 generations. Therefore, performance evaluation of the pure Borena breed was not the objective of this study and was excluded from fixed effect analysis. However, their genetic contribution for further generations was immense (25% and 50%) in the present study and the breed was fitted in to the genetic and genotypic models to calculate their contribution.\u003c/p\u003e \u003cp\u003eFor season of birth and calving, months in a year were classified into 3 seasons based on rainfall distribution. October to February is the dry season; March to May is a short rainy season; and June to September is the main rainy season.\u003c/p\u003e \u003cp\u003eDue to the limited number of records available per calving year, the years were categorized into 5 groups: 1995\u0026ndash;1999 (year 1), 2000\u0026ndash;2005 (year 2), 2006\u0026ndash;2011(year 3), 2012\u0026ndash;2017 (year 4), 2018\u0026ndash;2024 (year 5).\u003c/p\u003e \u003cp\u003eLactation milk yield (LMY) is the mammary gland secretes and yields milk throughout a single lactation period.\u003c/p\u003e \u003cp\u003eDaily milk yield (DMY) is the amount of milk produced by the cow each day in the morning (AM) and evening (PM).\u003c/p\u003e \u003cp\u003eLactation length (LL) refers to the period from when a cow starts to secrete milk after parturition to the time of drying off.\u003c/p\u003e \u003cp\u003eThe calving interval (CI) is the period between two consecutive parturitions and one of the major components of reproductive performance that influences livestock production systems.\u003c/p\u003e \u003cp\u003eAge at first service (AFS) is defined as the age at which heifers reach sexual maturity and attain body condition and sexual maturity after accepting service for the first time.\u003c/p\u003e \u003cp\u003eSynthetic breeds are developed by combining two or more different breeds, aiming to take advantage of hybrid vigor while maintaining a stable population without the need for further crossbreeding (Bourdon, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.7.1. Model was used for fixed effect analysis:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eModel 1\u003c/strong\u003e \u003cp\u003eFor production traits (LMY, DMY, and LL) and for reproductive trait (CI)\u003c/p\u003e \u003c/p\u003e \u003cp\u003eY\u003csub\u003eijkln\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;Y\u003csub\u003ei\u003c/sub\u003e + S\u003csub\u003ej\u003c/sub\u003e + G\u003csub\u003ek\u003c/sub\u003e + P\u003csub\u003el\u003c/sub\u003e + e\u003csub\u003eijkln,\u003c/sub\u003e Where;\u003c/p\u003e \u003cp\u003eY\u003csub\u003eijkln\u003c/sub\u003e =n\u003csup\u003eth\u003c/sup\u003e record of, i\u003csup\u003eth\u003c/sup\u003e year, j\u003csup\u003eth\u003c/sup\u003e season, k\u003csup\u003eth\u003c/sup\u003e genetic group and l\u003csup\u003eth\u003c/sup\u003e parity\u003c/p\u003e \u003cp\u003e\u0026micro;\u0026thinsp;=\u0026thinsp;overall mean\u003c/p\u003e \u003cp\u003eYi\u0026thinsp;=\u0026thinsp;effect of i\u003csup\u003eth\u003c/sup\u003e Year of Calving S\u003csup\u003ej\u003c/sup\u003e = effect of j\u003csup\u003eth\u003c/sup\u003e Season of Calving dry (October to February), short rain season (March to May), and long rain season (June to September).\u003c/p\u003e \u003cp\u003eG\u003csub\u003ek\u003c/sub\u003e = effect of k\u003csup\u003eth\u003c/sup\u003e Genetic group (50% F1, F2, F3 and 75% F1, F2)\u003c/p\u003e \u003cp\u003ePl\u0026thinsp;=\u0026thinsp;effect of l\u003csup\u003eth\u003c/sup\u003e Parity of Dam (1, 2, 3, 4, 5, 6, 7)\u003c/p\u003e \u003cp\u003e \u003cb\u003ee\u003c/b\u003e \u003csub\u003eijkln\u003c/sub\u003e = random error associated with each observation\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eModel 2\u003c/strong\u003e \u003cp\u003eReproductive traits (AFS and AFC) were analyzed the main model without the effect of parity.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eY\u003csub\u003eijkn\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;Y\u003csub\u003ei\u003c/sub\u003e + S\u003csub\u003ej\u003c/sub\u003e + G\u003csub\u003ek\u003c/sub\u003e + e\u003csub\u003eijkn\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eWhere, Y\u003csub\u003eijkn\u003c/sub\u003e = n\u003csup\u003eth\u003c/sup\u003e record of, it\u003csup\u003eh\u003c/sup\u003e year, j\u003csup\u003eth\u003c/sup\u003e season, k\u003csup\u003eth\u003c/sup\u003e genetic group\u003c/p\u003e \u003cp\u003e\u0026micro;\u0026thinsp;=\u0026thinsp;overall mean\u003c/p\u003e \u003cp\u003eY\u003csub\u003ei\u003c/sub\u003e = effect of i\u003csup\u003eth\u003c/sup\u003e year of birth\u003c/p\u003e \u003cp\u003eS\u003csub\u003ej\u003c/sub\u003e = effect of j\u003csup\u003eth\u003c/sup\u003e season of birth\u003c/p\u003e \u003cp\u003eG\u003csub\u003ek\u003c/sub\u003e = effect of k\u003csup\u003eth\u003c/sup\u003e genetic group (i\u0026thinsp;=\u0026thinsp;75%HF x BoF1, 75% HF x BoF2, 75% HF x BoF3)\u003c/p\u003e \u003cp\u003e \u003cb\u003ee\u003c/b\u003e \u003csub\u003eijkn\u003c/sub\u003e = random error associated with each observation\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.7.2 Genetic Parameter Analysis\u003c/h2\u003e \u003cp\u003eVariance and covariance components, heritability, repeatability, and genetic correlations were estimated by using WOMBAT software version 01-11-2011. Univariate and multivariate analysis were applied for genetic parameter estimation.\u003c/p\u003e \u003cp\u003eThe following animal model was applied,\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;Xb\u0026thinsp;+\u0026thinsp;Za\u0026thinsp;+\u0026thinsp;Wd\u0026thinsp;+\u0026thinsp;e. where;\u003c/p\u003e \u003cp\u003eY, is a vector of observations for the traits of interest\u003c/p\u003e \u003cp\u003eb, is a vector of fixed effects (genetic group, calving year, calving season and parity).\u003c/p\u003e \u003cp\u003ea, is a vector of random individual additive effects\u003c/p\u003e \u003cp\u003ed, is a vector of permanent environmental effects\u003c/p\u003e \u003cp\u003eX, matrices relating records to fixed effects\u003c/p\u003e \u003cp\u003eZ, incidence matrices relating records to individual animal effect\u003c/p\u003e \u003cp\u003eW, matrices of permanent environmental effects\u003c/p\u003e \u003cp\u003ee, vector of random residual effect\u003c/p\u003e \u003cp\u003eThe model assumed the expected mean of zero and variances σa\u003csup\u003e2\u003c/sup\u003e, σc\u003csup\u003e2\u003c/sup\u003e and σe\u003csup\u003e2\u003c/sup\u003e, respectively. Pedigree data as the software already recognized the formula as follows;\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;σa\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;σc\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;σe\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eσp\u003csup\u003e2\u003c/sup\u003e; is phenotypic variance (total variance)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eh\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;σa\u003csup\u003e2\u003c/sup\u003e/σp\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eσa\u003csup\u003e2\u003c/sup\u003e; additive genetic variance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003er\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;σa\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;σc\u003csup\u003e2\u003c/sup\u003e/σp\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eσc\u003csup\u003e2\u003c/sup\u003e; permanent environmental variance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAi\u0026thinsp;=\u0026thinsp;h\u003csup\u003e2\u003c/sup\u003e x P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eσe\u003csup\u003e2\u003c/sup\u003e; residual variance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.7.3. Crossbreeding Parameter Analysis\u003c/h2\u003e \u003cp\u003eThe regression analysis was performed using the Generalized Linear Model (GLM) procedure in SAS version 9.0 (2004) was used to analyze the productive (LMY, DMY, and LL) and reproductive (AFS, AFC, and CI) performance traits on crossbreeding parameters. Crossbreeding effects were decomposed into breed additive effects, heterosis, and recombination loss coefficients. These components were fitted as covariates in the model to estimate the breed additive (gi), heterosis (hij), and recombination loss (rij) coefficients, following the methodologies outlined by Dickerson et al. (1969) and Akbaş et al. (1993). The genetic model used for estimation of crossbreeding parameters is indicted as follows;\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;X\u003csub\u003e1\u003c/sub\u003eb\u003csub\u003e1\u003c/sub\u003e + X\u003csub\u003e2\u003c/sub\u003eb\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;βα. Where;\u003c/p\u003e \u003cp\u003eY, is a vector of observations for the traits of interest.\u003c/p\u003e \u003cp\u003eb\u003csub\u003e1\u003c/sub\u003e, is a vector of fixed effects other than genotype.\u003c/p\u003e \u003cp\u003eb\u003csub\u003e2\u003c/sub\u003e, genetic effect (breed additive difference, heterosis and recombination coefficients).\u003c/p\u003e \u003cp\u003eΒ is the matrix of expected genetic contribution (breed additive, heterosis and recombination loss)\u003c/p\u003e \u003cp\u003eα is a vector of the estimated corresponding parameters including overall mean\u003c/p\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003e, matrices relating records to fixed effects.\u003c/p\u003e \u003cp\u003eX\u003csub\u003e2\u003c/sub\u003e is a matrix of coefficients relating fixed breed additive, heterosis and recombination effects to the individual trait record.\u003c/p\u003e \u003cp\u003eThe equation uses to calculate breed additive (g\u003csup\u003ei\u003c/sup\u003e), heterosis (h\u003csub\u003eij\u003c/sub\u003e) and recombination loss (r\u003csub\u003eij\u003c/sub\u003e) effects will as follows:\u003c/p\u003e \u003cp\u003eBreed additive (g\u003csup\u003ei\u003c/sup\u003e) = \u0026frac12; (\u0026#120572;i\u003csup\u003es\u003c/sup\u003e + \u0026#120572;i\u003csup\u003ed\u003c/sup\u003e),\u003c/p\u003e \u003cp\u003eHeterosis (h\u003csub\u003eij\u003c/sub\u003e) = \u0026#120572;i\u003csup\u003es\u003c/sup\u003e \u0026#120572;j\u003csup\u003ed\u003c/sup\u003e + \u0026#120572;j\u003csup\u003es\u003c/sup\u003e \u0026#120572;i\u003csup\u003ed\u003c/sup\u003e and\u003c/p\u003e \u003cp\u003eRecombination loss (rij) = 4\u003csub\u003egi gj\u003c/sub\u003e - h\u003csub\u003eij\u003c/sub\u003e (Wolf et al., 1995 cited by Demeke et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2004\u003c/span\u003ea, b) where \u0026#120572;i\u003csup\u003es\u003c/sup\u003eand \u0026#120572;i\u003csup\u003ed\u003c/sup\u003e denote the gene proportion of breed i in the sire and dam of the cow, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe proportions of Holstein Friesian genes, individual and maternal heterosis and individual recombination coefficients used in prediction of performance of different genetic group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBreed and Genetic group (sire x dam)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGenetic coefficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003egI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eh\u003csup\u003eI\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003erI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure Borena\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% F1 (HF*Bo)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF2 (HF*Bo) *(HF*Bo)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF3 (((HF*Bo)*(HF*BO))*((HF*Bo)*(HF*BO)))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75% F1 HF * (HF*Bo)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF2 ((HF * (HF*Bo) * (HF * (HF*Bo))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eg\u003csup\u003eI\u003c/sup\u003e; individual additive genetic, h\u003csup\u003eI\u003c/sup\u003e ; individual heterosis, r\u003csup\u003eI\u003c/sup\u003e individual recombination effect Bo; Borena, HF; Holstein Friesian.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Productive Performance\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Lactation Milk Yield\u003c/h2\u003e \u003cp\u003eResults of the least square mean and standard errors for fixed effects of genetic group, calving year, calving season and parity, are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The overall lactation milk yield, daily milk yield and lactation length in the present study were 2140.61\u0026thinsp;\u0026plusmn;\u0026thinsp;32.92 kg, 6.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07kg and 316.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.31days, respectively. Our present results are slightly lower than those reported by Zenebe et al. (2024), which showed values of 2676.5\u0026thinsp;\u0026plusmn;\u0026thinsp;86.33 for Local x HF crosses in Smallholder Farmers, and Kassa et al. (2018), with values of 2305.2\u0026thinsp;\u0026plusmn;\u0026thinsp;32.15 for Holstein Friesian Dairy Herd at ELFORA Cheffa Dairy Farm. Comparable with the current result was reported by Getahun et al. (2018) with values of 2204.05\u0026thinsp;\u0026plusmn;\u0026thinsp;21.12 for Borena x HF crosses at Holeta Research Center dairy farm. Lower values were reported (Effa et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, and Ashutosh et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) with values of 1798\u0026thinsp;\u0026plusmn;\u0026thinsp;25 for Borena x HF crosses, 2088.7\u0026thinsp;\u0026plusmn;\u0026thinsp;29.4 for Borena x HF crossbred in the central highlands of Ethiopia, and 1506.75\u0026thinsp;\u0026plusmn;\u0026thinsp;71.37 for HF x local in Bangladesh, respectively. By contrast, higher LMY results for Holstein\u0026ndash;Friesian cows, with amounts of 3349.1, 3084.0, and 3604.0, liters, were reported by Yosef et al. (2006) in Holleta dairy farms, and by Gebeyehu et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and Wondwossen et al. (2015) at the Holleta Bull Dam, respectively. The difference might be attributed to breed/genetic makeup, management, feeding practice and climate factors in which animals were managed.\u003c/p\u003e \u003cp\u003eLactation milk yield was significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) affected by genetic group. The least square mean of lactation milk yield was increased when exotic gene inheritance increased from 50% to 75% HF crosses since the management level of high-grade cows increased. The 75% F1 crossbred cows produced significantly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) the highest lactation milk yield. Mean lactation milk yield was significantly decreased by 25% in the F2 generation of inter-se genotype (75% F2). The findings in the present study agree with the previous reports by Million et al. (2010), which suggested upgraded crossbred cows produce higher lactation milk yield than50% crosses. However, cows need to be managed well as the level of exotic gene inheritance is continuously upgraded. Likewise, Hirooka and Bhutyan (1995) reported that a high milk yield is recorded by exotic breeds in the tropics when they are well-fed and managed, signifying that the genetic potential of an animal is partly the reflection of management. Milk yield decreased in the interim individuals in the subsequent generations, as a result of loss of heterosis due to gene segregation.\u003c/p\u003e \u003cp\u003eCalving year markedly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) affected lactation milk yield. The lactation milk yield decreased over years. The highest lactation milk yield was obtained in the year 1995\u0026ndash;1999 (2397.85kg), while the lowest milk yield was attained in the year group 2018\u0026ndash;2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The difference could be explained by the availability of feed across years.\u003c/p\u003e \u003cp\u003eThe lactation milk yield was significantly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0003) affected by calving season. Higher lactation milk yield was obtained in both the dry and main rainy seasons. The reason might be in the dry season, harvesting hay by bell forms and supplementing green forage in the rainy season, but milk yield was lowest in the short rainy season. Quality of feed mainly varies over seasons, which perhaps affects the performance of animals, where strategic supplementation might enhance the productivity of animals in feed shortage periods (in the rainy season).\u003c/p\u003e \u003cp\u003eParity had a significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) effect on lactation milk yield. In this study, lactation milk yield sharply increased from first to fourth parity and became plateau towards 5th parity. Most literature supports our present finding. For instance, Goshu and Mekonnen (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) reported similar higher milk yields for the first four lactations of the Fogera-Friesian cross around Gonder city. However, some reports reveal that lactation milk yield decreases after the third parity for crossbred cows: Mackinnon et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), Getahun et al. (2018). The gradual increase in milk yield from first to four lactations might be attributed to development of secretory tissues of the udder due to recurring pregnancies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Daily Milk Yield (DMY)\u003c/h2\u003e \u003cp\u003eDaily milk yield was markedly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) affected by genetic group, calving year, season of calving and parity (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The daily milk yield increased as the exotic blood level of cows upgraded from 50% F1 to 75% F1. In line with our finding, the level of heterosis certainly enhanced in advanced generations. Though the significant difference observed in this study is not in agreement with studies by Gebregziabher et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), who reported that upgrading from 50% to higher Friesian fractions for HF X indigenous crosses has shown no significant differences in milk yields. The variation between the present and the previous study might be associated with management differences and the number of records considered in a data set. The 75% F1 produced 24 and 23.7% more daily milk yield than 50% F1 and 75% F2 generations, respectively. 50% F1 and 75% F2 genetic groups gave the same amount of milk yield/day (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The mean daily milk yield decreased from the 1st generation to 50%in the F2 and F3 inter mated generations. It is clear and frequently mentioned in much literature that heterosis declines in the inter progeny, which favors recombination loss during further crossing, process\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3. Lactation Length\u003c/h2\u003e \u003cp\u003eThe overall least square means and standard errors for lactation length in HF \u0026times; Boran genetic groups are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The 75% F1 cows exhibited the longest lactation length (335 days), followed by the 50% F1 crossbred cows (329 days). The lactation lengths observed in this study are relatively close to the standard lactation length of 305 days. Previous studies, such as those by Haile et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003ea), Kumar et al. (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and Dash et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), have reported similar values of 325\u0026thinsp;\u0026plusmn;\u0026thinsp;3 days, 325.12\u0026thinsp;\u0026plusmn;\u0026thinsp;61.28 days, 326.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03 days, and 326.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.60 days, respectively. On the other hand, longer lactation lengths were reported by Suhban \u003cem\u003eet al\u003c/em\u003e. (2000) and Effa et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), with corresponding estimates of 503.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.36 days and 360.76\u0026thinsp;\u0026plusmn;\u0026thinsp;6.11 days, respectively. In contrast, a shorter lactation length of 204\u0026thinsp;\u0026plusmn;\u0026thinsp;27.8 days was reported by Djoko et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Despite the substantial milk production associated with longer lactation lengths, the number of calves produced per cow would inevitably decrease. Several factors may contribute to variations in lactation length, including differences in breed type, nutrition, health status, and overall management practices. A balanced compromise in lactation length is essential in dairy cattle breeding practices to ensure that neither milk yield nor calf crop production is adversely affected.\u003c/p\u003e \u003cp\u003eLactation length exhibited a decreasing trend over time, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Over the past 30 years, lactation length declined by approximately 28 days. Similar findings were reported in the same farm in earlier periods by Haile et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003ea) and Effa et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003eb). This downward trend in lactation length may be attributed to earlier drying-off practices, shifts in breeding schedules, or nutritional adjustments aimed at optimizing other performance traits. Understanding these factors is crucial for developing informed breeding and management decisions that balance milk production with overall herd reproductive efficiency.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLeast square means and standard errors (LSM\u0026thinsp;\u0026plusmn;\u0026thinsp;SE) of lactation milk yield, daily milk yield and lactation length\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLMY (lit)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDMY (lit)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLL (days)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2140.61\u0026thinsp;\u0026plusmn;\u0026thinsp;32.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e316.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenetic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% F1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2280.06\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;32.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.03\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e329.07a\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% F2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1719.34\u003csup\u003ec\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;61.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.85\u003csup\u003ec\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e307.85b\u0026thinsp;\u0026plusmn;\u0026thinsp;6.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% F3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1546.64\u003csup\u003ec\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;77.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.30\u003csup\u003ed\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e300.06b\u0026thinsp;\u0026plusmn;\u0026thinsp;7.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75% F1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3030.305\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;52.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.26\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e335.20a\u0026thinsp;\u0026plusmn;\u0026thinsp;5.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75% F2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2126.71\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;96.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.01\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e310.52ab\u0026thinsp;\u0026plusmn;\u0026thinsp;9.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear group of calving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1995\u0026ndash;1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2299.19\u003csup\u003eab\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;103.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.19\u003csup\u003ec\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e386.34\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;10.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u0026ndash;2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2301.18\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;44.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.096\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e326.19\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;4.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2162.22\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;43.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.19\u003csup\u003eab\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e299.50\u003csup\u003ec\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;4.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2104.517\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;46.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.37\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e287.75\u003csup\u003ecd\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;4.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u0026ndash;2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1835.95\u003csup\u003ec\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;53.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.59\u003csup\u003ec\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e282.91\u003csup\u003ed\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;5.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalving season group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e****\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ens\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2214.76\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;37.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.02\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e321.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eshort rain season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2047.08\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;42.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.49\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e313.96\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain rain season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2159.99\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;43.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.01\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e314.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ens\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1894.13\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;41.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.92\u003csup\u003ec\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e320.38\u0026thinsp;\u0026plusmn;\u0026thinsp;4.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2048.24\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;44.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.35\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e324.90\u0026thinsp;\u0026plusmn;\u0026thinsp;4.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2162.03\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;48.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.11\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e309.56\u0026thinsp;\u0026plusmn;\u0026thinsp;4.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2225.43\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;54.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.09\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e319.62\u0026thinsp;\u0026plusmn;\u0026thinsp;5.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2202.23\u003csup\u003ea\u003c/sup\u003e\u0026plusmn; 61.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.12\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e316.40\u0026thinsp;\u0026plusmn;\u0026thinsp;6.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2206.44\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;71.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.18\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e314.71\u0026thinsp;\u0026plusmn;\u0026thinsp;7.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2245.77\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;84.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.49\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e310.19\u0026thinsp;\u0026plusmn;\u0026thinsp;8.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLMY=Lactation Milk Yield, DMY=Daily Milk Yield, LL=Lactation Length LSM=Least Square Mean, SE=standard error, N= Number of observations*Different superscripts (a, b, c, d) in the same fixed effect indicate differences among sample means. ns\u0026thinsp;=\u0026thinsp;non-significant, **** highly significant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Reproductive Traits\u003c/h2\u003e \u003cp\u003eReproductive traits are vital for milk production, herd replacement, and the overall profitability of dairy farming. Among the various factors influencing livestock production, the reproductive performance of female animals is the most critical consideration.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Age at First Service (AFS)\u003c/h2\u003e \u003cp\u003eThe least square means and standard errors for age at first service (AFS) are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The overall mean AFS was 33.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63 months. In this study, the highest and lowest AFS values recorded were 37.01 and 27.23 months, respectively. The mean AFS observed in the current study is lower than the 36.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 months reported by Gebeyehu et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) for Fogera \u0026times; Holstein Friesian (HF) crosses. However, it is higher than the values reported by Haile et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003eb) and Birhanu \u003cem\u003eet al\u003c/em\u003e. (2014), which were 29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 months for Borena \u0026times; HF crosses and 29.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 months for Borena \u0026times; HF \u0026times; Jersey crosses, respectively. These variations in AFS among different studies may be attributed to differences in breed composition, environmental conditions, and management practices, all of which significantly influence reproductive performance in dairy cattle.\u003c/p\u003e \u003cp\u003eA highly significant effect of genetic group on age at first service (AFS) was observed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001); (Appendix Table IV), consistent with the findings of Wssie \u003cem\u003eet al\u003c/em\u003e. (2015). The 50% F1 group had the shortest AFS (30.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46 months), while the 50% F2 group exhibited the longest (37.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00 months), indicating a 6.5-month increase from 50% F1 to 75% F1. This suggests that traits influenced by hybrid vigour may show reduced performance in backcrosses, depending on the additive genetic merit of the parental breeds (Cunningham \u0026amp; Syrstad \u003cem\u003eet al\u003c/em\u003e. (1987), Arthur et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). No significant differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were observed between 50% F2, 50% F3, and 75% F1, nor between 50% F1 and 75% F2. Additionally, calving season had no significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0, 05) effect on AFS, implying that the season of birth did not influence the onset of puberty in heifers. Nonetheless, timely breeding\u0026mdash;aligned with periods of optimal forage quality may help achieve earlier first service through better nutritional management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Age at First Calving (AFC)\u003c/h2\u003e \u003cp\u003eAge at first calving (AFC),a critical factor influencing the cost of raising dairy replacements. AFC showed a highly significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) variation across genetic groups and birth years (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The overall mean AFC in this study was 42.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63 months, aligning with findings by Damissu \u003cem\u003eet al\u003c/em\u003e. (2013) and Wassie et al. (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), but higher than values reported by Getahun \u003cem\u003eet al\u003c/em\u003e. (2018), Suhban \u003cem\u003eet al\u003c/em\u003e. (2000), and Hafez \u003cem\u003eet al\u003c/em\u003e. (2013). Among the genetic groups, 50% F1 crosses had the lowest AFC (38.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46 months), while 50% F2 exhibited the highest (45.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05 months). No significant differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were found between 50% F2, 50% F3, and 75% F1 or between 50% F1 and 75% F2. The differences in AFC across studies may be attributed to breed composition, environmental conditions, feeding and management practices, heat detection efficiency, and health care. Additionally, animals born between 2015 and 2020 had significantly lower AFC compared to those born in 2005\u0026ndash;2009, while birth season had no significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) effect on AFC.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe least square means and standard error (LSM\u0026thinsp;\u0026plusmn;\u0026thinsp;SE) of AFS and AFC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAFS (month)\u003c/p\u003e \u003cp\u003eLSM\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAFC (month)\u003c/p\u003e \u003cp\u003eLSM\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenetic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% F1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.17\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.44\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% F2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.64\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.94\u003csup\u003ea\u003c/sup\u003e \u0026plusmn;1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% F3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.54\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.64\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75% F1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.67\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.93\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75% F2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.763\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.95\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear group of calving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1995\u0026ndash;1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.49\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.78\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u0026ndash;2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.86\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.00\u003csup\u003ebc\u003c/sup\u003e \u0026plusmn;0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u0026ndash;2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.01\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.43\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.01\u003csup\u003eab\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.25\u003csup\u003eab\u003c/sup\u003e \u0026plusmn;0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.23\u003csup\u003ec\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.46\u003csup\u003ec\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalving season group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eshort rain season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain rain season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAFS\u0026thinsp;=\u0026thinsp;Age at First Service, AFC\u0026thinsp;=\u0026thinsp;Age at First Calving, LSM=Least Square Mean, SE =Standard error\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Calving Interval (CI)\u003c/h2\u003e \u003cp\u003eThe calving interval (CI), defined as the period between successive calving, averaged 469.01\u0026thinsp;\u0026plusmn;\u0026thinsp;7.03 days in this study, aligning closely with findings from Getahun \u003cem\u003eet al\u003c/em\u003e. (2011), Wassie et al. (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Belay \u003cem\u003eet al\u003c/em\u003e. (2014), and Getahun \u003cem\u003eet al\u003c/em\u003e. (2018), who reported CI values ranging from 468 to 476 days for different crossbred dairy cattle in Ethiopia. However, this result was notably lower than the 612\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6 days reported by Suhban \u003cem\u003eet al\u003c/em\u003e. (2000) for Pakistani crossbreds. Year of calving significantly influenced CI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), consistent with studies by Yosef \u003cem\u003eet al\u003c/em\u003e.(2006), Hunde \u003cem\u003eet al\u003c/em\u003e.(2012), and Getahun \u003cem\u003eet al\u003c/em\u003e. (2018), suggesting the impact of management practices, location, and genetic background. Interestingly, calving season did not significantly affect CI, echoing earlier reports by Million and Tadelle (2003a), Haile et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003eb), and Belay \u003cem\u003eet al\u003c/em\u003e. (2014)\u003c/p\u003e \u003cp\u003eGenetic and non-genetic factors influenced the calving interval, with the except season. Among genetic groups, 50% F2 crosses had the longest CI at 507.17\u0026thinsp;\u0026plusmn;\u0026thinsp;15.10 days, while 50% F1 crosses had the shortest at 449.12\u0026thinsp;\u0026plusmn;\u0026thinsp;6.15 days. This variation is likely due to heterosis, recombination effects, and differences in lactation length. Parity also had a significant impact, with the longest CI observed in first parity and the shortest in fourth parity, aligning with reports by Getahun \u003cem\u003eet al\u003c/em\u003e. (2018) and Gojam et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The trend suggests a decrease in CI with increasing parity, potentially due to improved uterine recovery and adaptation to parturition and lactation stress as cows mature.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLeast square means and standard error of (LSM\u0026thinsp;\u0026plusmn;\u0026thinsp;SE) Calving Interval (CI)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e469.01\u0026thinsp;\u0026plusmn;\u0026thinsp;7.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenetic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% F1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e449.12\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;6.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% F2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e507.17\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;15.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50% F3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e455.65\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;12.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75% F1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e463.23\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;9.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75% F2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e469.89\u003csup\u003eab\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;23.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear group of calving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1995\u0026ndash;1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e469.014\u003csup\u003eab\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;18.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u0026ndash;2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e471.66\u003csup\u003eab\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;8.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e453.76\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;9.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e452.47\u003csup\u003eb\u003c/sup\u003e\u0026plusmn; 9.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u0026ndash;2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e498.14\u003csup\u003ea\u003c/sup\u003e \u0026plusmn;11.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalving season group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ens\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e474.62\u0026thinsp;\u0026plusmn;\u0026thinsp;7.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort rain season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e472.03\u0026thinsp;\u0026plusmn;\u0026thinsp;8.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain rain season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e460.37\u0026thinsp;\u0026plusmn;\u0026thinsp;8.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e519.315\u003csup\u003ea\u003c/sup\u003e\u0026plusmn; 8.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e493.69\u003csup\u003eab\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;8.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e462.54\u003csup\u003ebc\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;9.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e451.11\u003csup\u003ec\u003c/sup\u003e\u0026plusmn; 10.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e451.91\u003csup\u003ec\u003c/sup\u003e\u0026plusmn; 11.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e464.51\u003csup\u003ec\u003c/sup\u003e\u0026plusmn; 13.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e439.98c\u0026thinsp;\u0026plusmn;\u0026thinsp;17.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eN=number of observation CI=Calving Interval, LSM= Least Square Mean, SE=Standard error\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Estimation of Crossbreeding Parameters\u003c/h2\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Crossbreeding parameter estimates for production traits\u003c/h2\u003e \u003cp\u003eEstimates of individual breed additive, individual heterosis and individual recombination for lactation milk yield, daily milk yield and lactation length are shown in Table\u0026nbsp;11. Individual breed additive effects were large and highly significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for lactation milk yield and daily milk yield, but not significant for lactation length. Relative to the mean value of the Borena cows, the individual additive contribution of Holstein Friesian cows was 3728\u0026thinsp;\u0026plusmn;\u0026thinsp;139.39 for lactation milk yield, 10.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30 daily milk yields and 48.95\u0026thinsp;\u0026plusmn;\u0026thinsp;14.77 days for lactation length. Individual heterosis effect was not significant for lactation milk yield, daily milk yield and lactation length.\u003c/p\u003e \u003cp\u003eThe breed additive difference for lactation milk yield in the present study (Table\u0026nbsp;11) was higher than the breed additive difference of 2674.05 kg reported for crosses between Boran x HF in the central highlands of Ethiopia reported by Getahun \u003cem\u003eet al\u003c/em\u003e. (2018) and breed additive difference of 2220 kg lactation milk yield for crosses between Holstein Friesian and Barca breed at Debre Zeit Agricultural Research Center in Ethiopia Tadesse et al. (\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe estimate of individual heterosis with respect to Holstein Friesian and Borena breed genes was negative\u0026thinsp;\u0026minus;\u0026thinsp;81.65\u0026thinsp;\u0026plusmn;\u0026thinsp;97.98, positive 0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21and negative (-18.72\u0026thinsp;\u0026plusmn;\u0026thinsp;10.38) for lactation milk yield, daily milk yield and lactation length, respectively (Table\u0026nbsp;11). Non-significant and negative heterosis estimates by Tadese et al., (2019) reported estimates of average lactation milk yield in crossbreeding between Boran and HF. The current study on the estimate of individual heterosis slightly differs from the value reported by Million et al. (2019), which was \u0026minus;\u0026thinsp;54.08\u0026thinsp;\u0026plusmn;\u0026thinsp;195 kg for milk yield in the crossbreeding of Boran cattle with Holstein Friesians in the central highlands of Ethiopia. The current study aligns with the findings of Getahun et al. (2018), who reported a significant negative effect of recombination on lactation milk yield (LMY), daily milk yield (DMY), and lactation length (LL). A small negative and highly significant individual heterosis for lactation milk yield (LMY) was reported by Abolfazl et al. (2012) in Iran for Holstein Friesian crosses with local breeds. In contrast, Hunde et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported a significant and higher negative individual heterosis value of 150.6\u0026thinsp;\u0026plusmn;\u0026thinsp;76. Differences in heterosis estimates between crossbred cattle can arise from various factors, including the genetic diversity of the parent breeds, the specific traits being measured, and environmental influences. Additionally, non-additive gene effects and the degree of genetic compatibility between the breeds can significantly impact heterosis outcomes. The Friesian breed has undergone selection for many generations primarily to enhance milk production per cow. Consequently, beneficial epistatic relationships among genes across various loci may have evolved. Therefore, when the Friesian is crossed with the unselected Borena, these interactions could be disrupted due to recombination.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCrossbreeding parameters estimates and their associated standard errors for productive traits.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrossbreeding parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLMY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDMY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreed additive genetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3728\u0026thinsp;\u0026plusmn;\u0026thinsp;139.39***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.95\u0026thinsp;\u0026plusmn;\u0026thinsp;14.77\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual heterosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-81.65\u0026thinsp;\u0026plusmn;\u0026thinsp;97.98\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-18.72\u0026thinsp;\u0026plusmn;\u0026thinsp;10.38\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual recombination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1440.92\u0026thinsp;\u0026plusmn;\u0026thinsp;152.25***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;75.15\u0026thinsp;\u0026plusmn;\u0026thinsp;16.13***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eLMY=lactation milk yield, DMY=daily milk yield, LL=lactation length\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ens: non-significant; ** significant ; *** highly significant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Crossbreeding parameter estimates for reproduction traits\u003c/h2\u003e \u003cp\u003eGenetic parameter estimates of the individual additive, heterosis and recombination effect on reproductive traits in the present study are summarized in Table\u0026nbsp;12. The additive effect of individuals had no significant negative impact on age at first service (AFS), age at first calving (AFC), and calving interval (CI) traits. Getahun et al. (2018) reported a significant negative estimate of -373.9\u0026thinsp;\u0026plusmn;\u0026thinsp;114.8, -337.1\u0026thinsp;\u0026plusmn;\u0026thinsp;112.6 days for AFS and AFC traits, respectively.\u003c/p\u003e \u003cp\u003eThe genetic estimate for individual heterosis in the current study was a small significant negative and desirable for AFC and a non-significant negative effect for AFS. This result was similar to the findings of Hunde et al., (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Getahun et al., (2018). However, CI was positive and non-significant. A significant estimate was reported by Demeke et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2004\u003c/span\u003eb) (-3.5months, -50 days for AFC and CI, respectively.\u003c/p\u003e \u003cp\u003eThe recombination effect in the current study was non-significant and positive for AFS, AFC and CI traits. Friesian crosses with Borena about 2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15 months and 2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15 months delay in AFS and AFC, which could be attributed to the recombination loss.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCrossbreeding parameters estimates and their standard errors for reproductive traits.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrossbreeding parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAFS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAFC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreed additive genetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-21.51\u0026thinsp;\u0026plusmn;\u0026thinsp;29.19\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;3.12\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.12 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual heterosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.33\u0026thinsp;\u0026plusmn;\u0026thinsp;19.79\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8. 79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8. 84\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual recombination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.01\u0026thinsp;\u0026plusmn;\u0026thinsp;32.71\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCI=calving interval AFS\u0026thinsp;=\u0026thinsp;age at first service AFC\u0026thinsp;=\u0026thinsp;age at first calvingns: non-significant; ** significant ; *** highly significant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Estimates of Genetic and Phenotypic Parameters\u003c/h2\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1. Estimates of variance components and heritability\u003c/h2\u003e \u003cdiv id=\"Sec31\" class=\"Section4\"\u003e \u003ch2\u003e3.4.1.1. Heritability and variance component of productive traits\u003c/h2\u003e \u003cp\u003eHeritability is important among several factors determining how much genetic improvement can be made in any trait, Haile, et al. (2006).\u003c/p\u003e \u003cp\u003eIn tropical and subtropical regions, disease and feed have greater effects on the performance of the animal. As a result, heritability might be low, Dechow et al., (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and Wasike et al., (\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVariance component heritability (h2), repeatability (r) and permanent environmental effects (Vc) of productive traits are presented in Table\u0026nbsp;13. The current study was presented that the productive traits of heritability LMY (0.180\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00), DMY (0.235\u0026thinsp;\u0026plusmn;\u0026thinsp;0.053), and LL (0.219\u0026thinsp;\u0026plusmn;\u0026thinsp;0.077) and repeatability LMY (0.589\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00), DMY (0.491\u0026thinsp;\u0026plusmn;\u0026thinsp;0.227), and LL (0.735\u0026thinsp;\u0026plusmn;\u0026thinsp;0.151).\u003c/p\u003e \u003cp\u003eThe estimated heritability value for lactation milk yield (LMY) was 0.180\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00. The current study is similar, with value corroborated by Demeke et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2004\u003c/span\u003ea) for various crossbred breeding in Ethiopia and 0.18. Ashutosh et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) reported comparable value for Holstein X Sahiwal crossbred cattle. However, the present heritability estimate is lower than that reported by Getahun et al. (2018) for Holstein Friesian \u0026times; Boran crosses and Gebregziabher et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which were 0.25 \u0026plusmn; -1 and 0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 for multi-breed cattle, respectively. In contrast, Tadesse et al. (2014) reported a higher value of 0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 for Ethiopian Holstein Friesian \u0026times; Boran crosses. The wide variation among these studies might result from the type of model used for the analysis and number of records.\u003c/p\u003e \u003cp\u003eDaily milk yield (DMY): The heritability estimate from this study was 0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05. This result is comparable to the findings of Getahun et al. (2018) at 0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 for Holstein Friesian \u0026times; Boran crosses in the central highlands of Ethiopia and Gebregziabher et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) at 0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 for various crossbreds. In contrast, lower estimates were reported by Demeke et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2004\u003c/span\u003ea) at 0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 for various crossbreds and Beneberu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) at 0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 for pure Jersey breeds. Higher values were reported by Tadesse et al. (2014) at 0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 for Ethiopian Holstein Friesian \u0026times; Borena crosses.\u003c/p\u003e \u003cp\u003eLactation length (LL): The heritability estimate was 0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1, which is comparable to the previous findings of Haile et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003ea) for Ethiopian Boran \u0026times; Holstein Friesian (HF) crosses (0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03) and Tadesse \u003cem\u003eet al\u003c/em\u003e. (2014) for the same cross (0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03). In contrast, a higher heritability value of 0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 was reported by Haile et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003ea) for HF \u0026times; local breeds, while lower estimates were documented by Getahun \u003cem\u003eet al\u003c/em\u003e. (2018) at 0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 for HF \u0026times; Boran crosses and Beneberu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) at 0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 for pure Jersey breeds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimate of variance components, heritability (h2\u0026thinsp;\u0026plusmn;\u0026thinsp;se) and repeatability (r\u0026thinsp;\u0026plusmn;\u0026thinsp;se) for milk production traits from univariate analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eδ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eδ\u003csup\u003e2\u003c/sup\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eδ\u003csup\u003e2\u003c/sup\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eδ\u003csup\u003e2\u003c/sup\u003ec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eh\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e315455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e769134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e315454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.180\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.589\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.235 \u0026plusmn; 0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.491\u0026thinsp;\u0026plusmn;\u0026thinsp;0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e619251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e319597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.219 \u0026plusmn; 0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.735\u0026thinsp;\u0026plusmn;\u0026thinsp;0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eδ\u003c/b\u003e \u003csup\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sup\u003e \u003cb\u003ea\u003c/b\u003e = additive variance, \u003cb\u003eδ\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003ec\u003c/b\u003e = permanent environmental variance, \u003cb\u003eδ\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003csub\u003e\u003cb\u003ee\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;error variance, \u003cb\u003eδ\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003ep\u003c/b\u003e = phenotypic variance, \u003cb\u003eh2\u003c/b\u003e\u0026thinsp;=\u0026thinsp;heritability and \u003cb\u003er\u003c/b\u003e= repeatability, LMY=lactation milk yield, DMY=daily milk yield, LL=lactation length.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section4\"\u003e \u003ch2\u003e3.4.1.2. Estimation of heritability for reproductive traits\u003c/h2\u003e \u003cp\u003eEstimation of for variance component, heritability (h\u003csup\u003e2\u003c/sup\u003e) and repeatability (r) for AFS, AFC and CI are showed in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. The current finding has shown that heritability values of reproductive traits were low.\u003c/p\u003e \u003cp\u003eAge at first service (AFS): The heritability estimate for AFS was 0.079\u0026thinsp;\u0026plusmn;\u0026thinsp;0.034, which is consistent with the findings of Beneberu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who reported a value of 0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 for pure Jersey breed. This result is significantly lower than the estimates reported by Getahun et al. (2018) at 0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 for Holstein Friesian \u0026times; Boran crosses and Belay et al. (2014) at 0.26 for Fogera \u0026times; Holstein Friesian crosses. Conversely, higher heritability values were reported by Haile et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003eb) at 0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 for Boran \u0026times; Holstein Friesian crosses and Berhanu and Ashim (2014) at 0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 for Ethiopian Boran \u0026times; Holstein Friesian crosses.\u003c/p\u003e \u003cp\u003eAge at first calving (AFC): The heritability estimate for AFC derived from the univariate analysis was 0.080\u0026thinsp;\u0026plusmn;\u0026thinsp;0.033. This finding is consistent with the results reported by Beneberu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who documented a heritability estimate of 0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;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\u0026thinsp;\u0026plusmn;\u0026thinsp;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. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003eb) for Ethiopian Boran \u0026times; Holstein Friesian crosses at 0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16 and by Gebeyehu et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) for Holstein breeds at 0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;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 \u003cp\u003eCalving interval (CI): The heritability estimate obtained in the present study was 0.180\u0026thinsp;\u0026plusmn;\u0026thinsp;0.042. This result is comparable to that reported by Tadesse et al. (2014), who found a heritability of 0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;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\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 for Holstein Friesian \u0026times; Boran crosses. Notably, a significantly higher heritability estimate of 0.499 was reported by Mohamed 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 the sample size used, genetic group/breed and analysis methods of Assemu et al. (2015).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimate of variance components, heritability (h2\u0026thinsp;\u0026plusmn;\u0026thinsp;se) and repeatability (r\u0026thinsp;\u0026plusmn;\u0026thinsp;se) for milk reproductive traits from univariate analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eδ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eδ\u003csup\u003e2\u003c/sup\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eδ\u003csup\u003e2\u003c/sup\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eδ\u003csup\u003e2\u003c/sup\u003ec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eh\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e139.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.0798\u0026thinsp;\u0026plusmn;\u0026thinsp;0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e139.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.080\u0026thinsp;\u0026plusmn;\u0026thinsp;0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19007.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4443.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24730.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1279.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.180 \u0026plusmn; 0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eδ\u003c/b\u003e \u003csup\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sup\u003e \u003cb\u003ea\u003c/b\u003e = additive genetic variance, \u003cb\u003eδ\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003ec\u003c/b\u003e = permanent environmental variance, \u003cb\u003eδ\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003csub\u003e\u003cb\u003ee\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;residual variance, \u003cb\u003eδ\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003ep\u003c/b\u003e= phenotypic variance, \u003cb\u003eh\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e= heritability \u003cb\u003er\u003c/b\u003e= repeatability AFS\u0026thinsp;=\u0026thinsp;age at first service, AFC\u0026thinsp;=\u0026thinsp;age at first calving, CI= calving interval.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2. Estimation of repeatability (r) for productive traits\u003c/h2\u003e \u003cp\u003eThe repeatability estimates for productive traits of lactation milk yield (LMY), daily milk yield (DMY), and lactation length (LL) were 0.589\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00, 0.491\u0026thinsp;\u0026plusmn;\u0026thinsp;0.227, and 0.735\u0026thinsp;\u0026plusmn;\u0026thinsp;0.151, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). 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 et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), who reported 0.505 for Iranian Holstein Friesian crosses. However, it exceeds the 0.33 reported by Beneberu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for pure Jersey breeds and the lower estimate of 0.17 by Haile et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003ea) for Holstein Friesian \u0026times; Boran crosses.\u003c/p\u003e \u003cp\u003eThe repeatability estimate for daily milk yield (DMY) in the present study was 0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02, aligning with Getahun et al. (2018) for Holstein Friesian \u0026times; Boran crosses, and exceeding the 0.334 reported by Ghorbani et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) for Iranian Holstein Friesian crosses. However, lower repeatability values of 0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 were documented by Demeke et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2004\u003c/span\u003eb) for Holstein Friesian \u0026times; Boran and Jersey \u0026times; Boran crosses.\u003c/p\u003e \u003cp\u003eRepeatability for lactation length observed in this study approximated 0.70 as reported by Haile et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003ea) for Holstein Friesian \u0026times; Boran crosses and was higher than the 0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 value reported by Getahun et al. (2018) for the same crossbreds. In contrast, Tadesse et al. (\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported a notably lower repeatability estimate of 0.050\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 for Holstein Friesian \u0026times; Boran crosses.\u003c/p\u003e \u003cp\u003eRegarding reproductive performance, the repeatability estimate for calving interval (CI) in this study was 0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01. This value is lower than the 0.359\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 reported by Million et al. (2019) for Holstein Friesian \u0026times; Boran crosses but higher than values reported by Beneberu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) at 0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 for pure Jersey breeds and by Getahun et al. (2018) at 0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;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 \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3. Genetic and phenotypic correlations\u003c/h2\u003e \u003cp\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\u0026nbsp;15. 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.\u003c/p\u003e \u003cdiv id=\"Sec35\" class=\"Section4\"\u003e \u003ch2\u003e3.4.3.1. Genetic correlations\u003c/h2\u003e \u003cp\u003eThe genetic correlation between productive traits was positive, and the coefficients ranged from weak (0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07) to very strong (0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03). A high correlation was observed between LMY and DMY (0.926\u0026thinsp;\u0026plusmn;\u0026thinsp;0.032). This signifies that the two traits are governed by the same gene. Similar to our finding, Beneberu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) showed a high correlation coefficient of (0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;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 was obtained in the work of Ashutosh et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) i.e., 0.31 for LMY and LL and 0.30 for LMY and DM, respectively.\u003c/p\u003e \u003cp\u003eGenetic correlation coefficients between reproductive traits in the present were weak but positive. AFS-AFC (0.228\u0026thinsp;\u0026plusmn;\u0026thinsp;0.172), AFS-CI (0.181\u0026thinsp;\u0026plusmn;\u0026thinsp;0.194), and AFC-CI (0.063\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02). In agreement with this finding, Belay et al.(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. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for AFC and CI (0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61) and AFS and AFC (0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11) for pure Jersey breed.\u003c/p\u003e \u003cp\u003eStrong genetic correlation was seen between CI-LL (0.785\u0026thinsp;\u0026plusmn;\u0026thinsp;0.074), and moderate genetic correlation between CI-LMY and AFC-LL (0.428\u0026thinsp;\u0026plusmn;\u0026thinsp;0.098, and 0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.107), respectively. Very weak genetic correlation values were CI-DMY, AFC-LMY, and AFS-LMY (0.142\u0026thinsp;\u0026plusmn;\u0026thinsp;0.073, 0.024\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001, 0.129\u0026thinsp;\u0026plusmn;\u0026thinsp;0.056), respectively, and finally negative genetic correlation appeared between AFC-DMY (-0.206\u0026thinsp;\u0026plusmn;\u0026thinsp;0.072), AFS-DMY (-0.196\u0026thinsp;\u0026plusmn;\u0026thinsp;0.148) and AFS-LL (-0.020\u0026thinsp;\u0026plusmn;\u0026thinsp;0.078). The negative genetic correlation between AFC-DMY (-0.206\u0026thinsp;\u0026plusmn;\u0026thinsp;0.072) and AFS-LL (-0.020\u0026thinsp;\u0026plusmn;\u0026thinsp;0.078) is similar with the report of Getahun et al.(2018) AFC-DMY (-0.55) and AFS-LL (-0.11).\u003c/p\u003e \u003cp\u003eIn general, a positive direct genetic correlation between traits in the current study showed that selection of one trait might be 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 indicate 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 \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section4\"\u003e \u003ch2\u003e3.4.3.2. Phenotypic correlations\u003c/h2\u003e \u003cp\u003eThe phenotypic correlations estimated for production traits were positive very weak (0.017\u0026thinsp;\u0026plusmn;\u0026thinsp;0.024) between DMY-LL strong (0.670\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012) between DMY-LMY and very strong (0.890\u0026thinsp;\u0026plusmn;\u0026thinsp;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\u0026thinsp;\u0026plusmn;\u0026thinsp;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.\u003c/p\u003e \u003cp\u003eThe phenotypic correlation among reproductive traits, as indicated in Table.15, was positive and very weak (0.011\u0026thinsp;\u0026plusmn;\u0026thinsp;0.026) between AFS-AFC and (0.051\u0026thinsp;\u0026plusmn;\u0026thinsp;0.055) AFS-CI and negative (0.014\u0026thinsp;\u0026plusmn;\u0026thinsp;0001) between AFC-CI. Similar results was reported by Getahun et al.(2018) negative phenotypic correlation AFS-CI (-0.03). The present study had positive and negative phenotypic correlation of these traits strongly disagree with the findings of Belay (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) who found very strong phenotypic correlation between AFS and AFC (0.85463).\u003c/p\u003e \u003cp\u003eThe current studies vary from others. It might be due to breed, number of observations studied and software procedure used for analysis.\u003c/p\u003e \u003cp\u003eThe phenotypic correlation between productive and reproductive traits ranged from moderate positive to negative values. The present study lactation length saw a negative phenotypic correlation between AFS and AFC. However, a positive correlation was shown between AFS-LMY (0.017\u0026thinsp;\u0026plusmn;\u0026thinsp;0.031), AFS-DMY (0.041\u0026thinsp;\u0026plusmn;\u0026thinsp;0.032), AFC-LMY (0.002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.037), LL-CI (0.447\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019), LMY-CI (0.215\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023) and (0.017\u0026thinsp;\u0026plusmn;\u0026thinsp;0.024). The phenotypic correlation among LL-CI in the current study is similar to the report of Beneberu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) 0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;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 is similar to the findings of Getahun et al. (2018) and Ashutosh et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAFS\u0026thinsp;=\u0026thinsp;Age at First Service, AFC\u0026thinsp;=\u0026thinsp;Age at First Calving, CI=Calving Interval, LMY=Lactation Milk Yield, DMY=Daily Milk Yield, LL=Lactation Length.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e \u003ch2\u003e3.4.4. Estimates of Breeding Values\u003c/h2\u003e \u003cp\u003eThe total genetic ability of an animal for a given trait is called breeding value (BV). Estimates of breeding value mention the value of an animal's additive genetic effects in the breeding programm for a specific trait of interest. Breeding values were estimated for reproductive and production traits of HF crosses with different genotypes at Holeta Research Center, since the trait of interest at the center is to improve the reproductive and milk production performance of the breed through improving their genetic performance.\u003c/p\u003e \u003cdiv id=\"Sec38\" class=\"Section4\"\u003e \u003ch2\u003e3.4.4.1. Breeding value estimates of cows for productive traits\u003c/h2\u003e \u003cp\u003eAverage breeding values for the estimated lactation milk yield (21.89 liters), daily milk yield (0.060 liters), and lactation length (0.06 days) were summarized (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) respectively.\u003c/p\u003e \u003cp\u003eAverage estimated breeding values across the year of birth group for milk production traits vary across birth year groups. The highest estimated average breeding value (182.97 liters) for lactation milk yield was observed in the 2000\u0026ndash;2005 year whereas the lowest estimated average breeding value (-159.45) was observed in 1995\u0026ndash;1999. The genetic trend for lactation milk yield from the year 2000\u0026ndash;2005 in this study showed an increasing trend. The average estimated breeding value for daily milk yield ranged from \u0026minus;\u0026thinsp;0.105 to 0.1851 Liters, whereas the average estimated breeding value for lactation length was between \u0026minus;\u0026thinsp;0.018 and 0.093 days.\u003c/p\u003e \u003cp\u003eThe declining trend indicates inefficiency in selection and proper culling of unproductive cows for milk production traits and also implies that the inefficiency in selection methods based on phenotypic performance.\u003c/p\u003e \u003cp\u003eThe positive improvements in genetic trends for lactation milk yield, daily milk yield and lactation length traits in some year groups might be due to better management, feed availability, number of observations and proper selection of cows based on their phenotypic performance.\u003c/p\u003e \u003cp\u003eUpward and downward trends of breeding values for milk production traits across year groups might be due to the presence of high and low producing cows with the absence of culling, environmental stresses (heat stress, low quality and quantity of feed), inefficiency in selection methods based on phenotypic performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section4\"\u003e \u003ch2\u003e3.4.4.2. Breeding value estimates of cows for reproductive traits\u003c/h2\u003e \u003cp\u003eThe current study showed that the average estimated breeding values for AFS (-0.012 months with AFC (-0.017 months) Figure (5), and CI (-1.565 days) Figure (7) were summarized. The current study showed a negative trend for reproductive traits (AFS, AFC and CI). It might be efficient breeding programs, efficiency in selection methods based on phenotypic performance and proper culling of unproductive cows.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. CONCLUSIONS","content":"\u003cp\u003eThe current study was specifically designed to estimate the performance of Friesian and Borena crossbreds with various genotypes in a synthetic breed developing at Holeta Agricultural Research Center. With this regards during the period 1995-2024 were used for this study.\u003cbr\u003e\u0026nbsp;Genetic and non-genetic factors influenced the productive and reproductive performance of Friesian and Borena crossbreeds.\u003c/p\u003e\n\u003cp\u003eSeason of birth group, and season of calving group, had no significant effect on reproductive traits (AFS, AFC, and CI) and productive traits (DMY and LL). However, the season of calving group had a significant effect on LMY. The other significant variations were found in the measured reproductive and productive traits between genetic groups, birth year groups, calving year groups, and parity. This suggests that strict selection and improved management choices can result in a notable improvement.\u003c/p\u003e\n\u003cp\u003eFirst-generation crosses produced more milk than second-generation crosses. Performance among 50% F1, F2, and F3, and 75%F1, and F2 significantly declined, which indicated the importance of retaining heterosis. 75%of F1 had produced superior milk per lactation and was the breed of choice for milk production trait compared with other genetic groups.\u003c/p\u003e\n\u003cp\u003eAmong 75% of crosses, mean lactation milk yield was significantly decreased by 25% for the inter-se mating breed group (75% females mated with 75% males) compared to 75% F1 or first generation.\u003c/p\u003e\n\u003cp\u003eThe higher milk yield of first generation (50% F1 and 75% F1) crosses from its contemporary group was also associated with longer lactation length.\u003c/p\u003e\n\u003cp\u003eThe 50% F1 genetic group was also characterized by better reproductive performance and the 75% F1 cross was poor in reproductive and high productive traits. As well as, the 50%of the second and third generation were belonged with their poor reproductive performances. It might be the breakdown of hybrid vigor occurs when gene interactions (epistasis) that benefited the F1 generation are disrupted in the F2 or F3 generations, leading to poorer performance and recombination loss.\u003c/p\u003e\n\u003cp\u003eThe year and parity significantly influenced reproductive and productive traits due to the unpredictable changes in the environment, management practices, herd structure, and lactation stages that occurred annually.\u003c/p\u003e\n\u003cp\u003eTrend analysis has shown that the highest milk yield was recorded at the station in the year from 2000-2005.\u003c/p\u003e\n\u003cp\u003eThe non-significant heterosis effect of crossbred for lactation, milk yield, and daily milk yield in the current study showed that the higher milk yield of these crosses was only due to the breed additive of the Friesian sire gene, and the recombination loss was significant in all productive traits except lactation length. This might be due to unfavorable epistatic allele interaction.\u003cbr\u003e\u0026nbsp;While the individual recombination effect was significant, it had an unfavorable, large, and negative impact on lactation milk yield, daily milk yield, and lactation length. In contrast, the individual breed additive effect of these three traits was more important than the individual heterosis effect.\u003c/p\u003e\n\u003cp\u003eExcept AFC on heterosis, all reproductive traits in the present study indicated that the negative value and non-significant on the additive, heterosis, and recombination due to the additive effect of the Friesian gene.\u003c/p\u003e\n\u003cp\u003eThe values of heritability and repeatability for both productive and reproductive traits, were none zero and ranged from low to higher.\u003c/p\u003e\n\u003cp\u003eThe 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 a 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.\u003c/p\u003e\n\u003cp\u003eKnowing that all 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 the traits that are correlated with one another.\u003c/p\u003e\n\u003cp\u003eThe phenotypic correlation between productive traits ranges from very weak to strong correlation. A strong phenotypic correlation was observed between LL and LMY. However, a negative correlation was observed among AFC and LL.\u0026nbsp;\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eUpward and downward trends of breeding values for milk production traits across year groups might be due to the presence of high and low producing cows with the absence of culling, environmental stresses (heat stress, low quality and quantity of feed), inefficiency in selection methods based on phenotypic performance.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBased on our study, improved management is required to minimize the genetic recombination effect on lactation milk yield and daily milk yield.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eContinuous selection is important among crossbred animals to exploit the advantage of heterosis in inter crossbreds.\u003c/li\u003e\n \u003cli\u003eMore data are required to evaluate subsequent generations in the upcoming periods to produce a composite breed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026bull; It is vital to consider a huge segregated population with sufficient genetic variability, \u0026nbsp; \u0026nbsp;avoiding any possibility of increasing the rate of inbreeding.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAFC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAge at First Calving\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAge at First Service\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArtificial Insemination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMY\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnnual Milk Yield\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBreeding Value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCalving Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentral Statistical Authority\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoefficient of Variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDMY\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDaily Milk Yield\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEARO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEthiopian Agricultural Research Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEBV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEstimated Breeding Value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eF1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFirst Generation Crossbred\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eF2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSecond Generation Crossbred\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eF3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThird Generation Crossbred\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFAO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFood and Agriculture Organization of the United Nation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGLM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized Linear Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHARC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHoleta Agricultural Research Center\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHolstein Friesian\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIndividual Identity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLactation Length\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLivestock Development Institute\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMY\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLactation Milk Yield\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeast Square Means\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMASL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMeter Above Sea Level\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Analysis System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Almighty God's love, kindness, and faithfulness in providing health, patience, strength, and safety during the study period are very greatly appreciated.\u003c/p\u003e\n\u003cp\u003eThroughout the study period, from the writing of the proposal to the completion of this thesis work, I would like to sincerely thank Dr.Haile Welearegay (Major Advisor) Dr. Zewdie Wondatir (co-advisor) for their helpful criticism, suggestions, encouragement and data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor 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\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Ethiopian Institute of Agricultural Research (EIAR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\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\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\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\u003eAbolfazl, G., \u0026amp; Salamatdoustnobar, R. 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(2017). \u003cem\u003eReproductive Performances and Management Practices of Small Holder Dairy Farms in Layarmachio Wereda, North Gondar Zone.\u003c/em\u003e Mekelle University,\u003c/li\u003e\n \u003cli\u003eS\u0026oslash;rensen, M., Norberg, E., Pedersen, J., \u0026amp; Christensen, L. (2008). Invited review: Crossbreeding in dairy cattle: A Danish perspective. \u003cem\u003eJournal of Dairy Science, 91\u003c/em\u003e(11), 4116-4128.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSuhban.M, Q., Khan, A., Mirbahar, K. B., \u0026amp; Samo, M. U. (2000). Productive and reproductive performance and their interaction in crossbred cattle under field conditions in district Bannu. \u003cem\u003ePakistan Veterinary Journal, 20\u003c/em\u003e(1), 31-34.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSyrstad, O. (1989). Dairy cattle cross-breeding in the tropics: performance of secondary cross-bred populations. \u003cem\u003eLivestock Production Science, 23\u003c/em\u003e(1-2), 97-106.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSyrstad, O. (1993). Milk yield and lactation length in tropical cattle. \u003cem\u003eWorld Anim. Rev, 74\u003c/em\u003e(75), 68-78.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTadesse Birhanu. 2014. Estimation of Crossbreeding Parameters in Holstein Friesian and Ethiopian Borena Crosses for Milk Production and Reproduction Traits at Holetta Agricultural Research Center, Ethiopia. MSc Thesis, Haramaya University, Haramaya, Ethiopia.\u003c/li\u003e\n \u003cli\u003eTadese, B., Mohammed, T., Kedebe, K., \u0026amp; Tadesse, M. (2015). Estimation of crossbreeding parameters for milk production and reproduction traits in Holstein Friesian and Ethiopian Boran crosses. \u003cem\u003eJournal of Reproduction and Infertility, 6\u003c/em\u003e(3), 63-69.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTadesse, M., \u0026amp; Dessie, T. (2003). Estimation of crossbreeding parameters for milk production traits of crosses between Holstein Friesian and local Arsi breed in the highland of Ethiopia. \u003cem\u003eEthiopian journal of animal production, 25\u003c/em\u003e.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTadesse, M., Thiengtham, J., Pinyopummin, A., \u0026amp; Prasanpanich, S. (2010). Productive and reproductive performance of Holstein Friesian dairy cows in Ethiopia. \u003cem\u003eLivestock research for rural development, 22\u003c/em\u003e(2), 2010.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTadesse, M., Hunde, D., \u0026amp; 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. \u003cem\u003eLivestock Research Results\u003c/em\u003e, 282-292.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTadesse, M., Getahun, K., Hunde, D., \u0026amp; Gelmessa, U. (2022). Analysis of the non-genetic factors influencing the performance of high-grade and inter se mated crossbred dairy cows at holetta dairy research farm, Ethiopia. \u003cem\u003eAsian Journal of Dairy and Food Research, 41\u003c/em\u003e(1), 38-42.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTesema, Z., Taye, M., \u0026amp; Kebede, D. (2020). Current status of livestock crossbreeding in Ethiopia: Implications for research and extension. \u003cem\u003eJournal of Applied Animal Science, 13\u003c/em\u003e(2).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTour\u0026eacute;, A. (2020). Analyse des typologies d\u0026rsquo;\u0026eacute;levage et des performances des bovins en vue d\u0026rsquo;\u0026eacute;valuer des strat\u0026eacute;gies de d\u0026eacute;veloppement des ressources g\u0026eacute;n\u0026eacute;tiques bovines au Mali. Universite de Liege (Belgium),\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eVanholder, T., Leroy, J., Dewulf, J., Duchateau, L., Coryn, M., de Kruif, A., \u0026amp; Opsomer, G. (2005). Hormonal and metabolic profiles of high‐yielding dairy cows prior to ovarian cyst formation or first ovulation post partum. \u003cem\u003eReproduction in Domestic Animals, 40\u003c/em\u003e(5), 460-467.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWasike, C., Ilatsia, E., Ojango, J., \u0026amp; Kahi, A. (2006). 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Performance of crossbred dairy cows suitable for smallholder production systems at Holetta Agricultural Research Centre. \u003cem\u003eEthiopian Journal of Agricultural Sciences, 27\u003c/em\u003e(1), 121-131.\u0026nbsp;\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\u003eYusuf, M. (2020). \u003cem\u003eReproductive performance of dairy cows in a smallholder farm.\u003c/em\u003e Paper presented at the IOP Conference Series: Earth and Environmental Science.\u003c/li\u003e\n \u003cli\u003eZeleke, 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,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZeleke, 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.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZenebe, T. (2024). Evaluating Synergies of Genetic and Non-Genetic Intervention on reproductive and Productive Performance of Crossbred Dairy Cows under Smallholder Farmers Condition in Selected Milk Shed areas of Ethiopia.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZewdu Wondifraw, Z. W., Thombre, B., \u0026amp; Bainwad, D. (2013). Effect of non-genetic factors on milk production of Holstein Friesian\u0026times; Deoni crossbred cows.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Additive genetic effect, Borena, crossbred dairy cattle, Ethiopia, genetic parameter, genetic trend, Holeta, productive performance, reproductive performance","lastPublishedDoi":"10.21203/rs.3.rs-8744363/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8744363/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study was carried out to estimate genetic and phenotypic parameters for milk production and reproduction traits of synthetic dairy cattle breed development program being implemented at Holeta research center dairy farm. Data collected from 1995 through 2024 on lactation milk yield, lactation length ,daily milk yield, age at first service, age at first calving, and calving intervals from experiments targeted at developing a synthetic breed at the Holeta Agricultural Research Center dairy herd were used for this study. The GLM procedures of SAS software were used to estimate the effect of fixed effects such as year, season and parity while regression analysis was performed to estimate crossbreeding parameters (additive, heterosis and recombination effects). Genetic components, including variance covariance estimates were analyzed using WOMBAT software. A univariate mixed model for genetic parameters and Multiple Regression Model for crossbreeding parameters was used for data analysis. The performance of dairy cattle affected by genetic and non-genetic factors. The result of fixed effects (year and genetic group) analysis showed that significant (p\u0026lt;0.0001) differences in all productive and reproductive traits. Correspondingly, productive traits (LMY and DMY) and reproductive (CI) traits were also significantly (p\u0026lt;0.0001) influenced by parity. The traits, lactation and milk yield, were sensitive to seasonal variation. The overall least square means for lactation milk yield (LMY), daily milk yield (DMY), lactation length (LL), age at first service (AFS), age at first calving (AFC), and calving interval(CI)were 2140.61 ± 32.92kg, 6.89 ± 0.07kg, 316.54 ± 3.31days, 33.56 ± 0.63months, 42.78 ± 0.63months and, 469.01 ± 7.03days, respectively. Additive genetic effects were much larger than the non-significant negative value of heterosis effect of lactation milk yield (3728 ± 139.39 kg of additive and -81.65 ± 97.98 kg of heterosis). The cross-breeds were -21.51± 29.19 days, -2.29 ± 3.12 months, and -2.23 ± 3.12 months, reduced for CI, AFS and, AFC due to the additive effect of the Friesian gene. Estimations of heritability for productive traits (LMY, DMY, and LL) were 0.180 ± 1.00, 0.235 ± 0.053 and 0.219 ± 0.077, respectively, and reproductive traits (AFS, AFC, and CI) 0.0798 ±0.034, 0.080 ±0.033and 0.180 ± 0.042, in respective order. The current result indicated that repeatability values of productive traits 0.589 ± 1.00 for LMY, 0.491± 0.227 for DMY, 0.735± 0.151 for LL, and0.23±0.01 for CI. The current study indicated that the direct genetic correlation between productive traits was positive and ranged from very weak (0.141 ± 0.073) to very strong (0.854 ± 0.304) genetic correlations. From the current study, a high correlation was observed between LMY and LL (0.854 ± 0.304). The current study indicated that positive genetic correlation ranged from very weak to weak genetic correlation among reproductive traits. AFS-AFC (0.228 ± 0.172), AFS-CI (0.181 ± 0.194), and AFC-CI (0.063 ± 0.02). The present study indicated that the genetic correlation between productive and reproductive traits was closely related with each other in some traits. 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. Thus, based on the study's findings, it was feasible to draw the conclusion that proper parental line selection and crossing should be used to create next-generation calves and improve the farm's overall management system.\u003c/p\u003e","manuscriptTitle":"Estimation of genetic and phenotypic parameters for milk production and reproduction traits in a developing synthetic dairy cattle breed at Holeta Agricultural Research Center, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 04:49:31","doi":"10.21203/rs.3.rs-8744363/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-10T08:39:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-28T20:03:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42312309404694593614104776418012913249","date":"2026-02-28T19:58:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-23T13:40:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163050677851877727668973796469203859919","date":"2026-02-22T20:11:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-22T17:38:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-05T14:13:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T00:50:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-03T00:49:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-30T18:52:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"298091d4-ccbc-4203-9fc3-4975538061b3","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62588605,"name":"Biological sciences/Genetics"},{"id":62588606,"name":"Biological sciences/Zoology"}],"tags":[],"updatedAt":"2026-04-24T18:53:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 04:49:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8744363","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8744363","identity":"rs-8744363","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}