ABCG2 (ATP binding cassette subfamily G member 2) gene have located in chromosome 6 which is encoded ABCG2 protein that transports various xenobiotics, cytostatic drugs across the plasma membrane and cholesterol into milk. A single nucleotide change (A/C) in base 86 of exon 14 is capable of encoding a substitution of tyrosine to serine in the ABCG2 gene and increases milk yield and decreases fat and protein concentration. The major aim of this research is to identify ABCG2 gene polymorphism in Iranian population of Holstein bulls. Genomic DNA of 105 bulls was extracted from semen samples using high Pure PCR template preparation kit. Primers were designed with Oligo software and utilized in PCR. Then the PCR fragments were sequenced. Results of sequences were compared with NCBI sequence databases. A/C mutation in base 86 of exon 14 was observed with 2% frequency.ABCG2 gene, Iranian Holstein bulls, Polymorphism.

This study was performed to investigate the incidence of abortion based on different definitions and, the effect of climate and herd size on abortion as well as its phenotypic trend. Using calving records collected from 16 dairy herds from years 2004 through 2014. Incidence rate and analysis of affecting factors on abortion were done using the FREQ and GENMOD procedures of SAS software, respectively. According to the definition of abortion as death and expulsion of fetus between 60-260 d, 100-260 d or 150-260 d of pregnancy, overall abortion incidence was 15. 5%, 11. 25% and 6. 6%, respectively. Herd size, climate, parity, year and season of calving as well as the interaction of calving year × season and climate × calving year were associated with abortion (P < 0. 001). Greatest abortion incidence was observed for second parity cows (28. 7%). The highest and lowest rates of abortion (16. 0 vs. 14. 5%) happened in the mild and cold climate, respectively. Also, the larger herds, with more than 3000 cows, had almost 1% higher abortion incidence than smaller herds, with less than 1000 cows. The average rate of abortion incidence was highest in the spring and lowest in the autumn (17. 8 vs. 13. 5 %). According to the regression of least squares means on calving year, abortion incidence showed a positive and significant trend (P < 0. 003) by 0. 4 per year. The results of this study can be used in the analysis of management practices to control abortion.

Abortion is an important trait with noticeable impacts on economic profit of dairy herds. This study was conducted to estimate variance components and genetic, environmental and phenotypic trends for abortions in Iranian Holstein Friesian cows. The data used were 247230 calving and abortion records of 84421 Hol-stein cows, collected during years 1991-2016, from 51 herds in Iran. Variance components and genetic pa-rameters were estimated using a logit link function, fitting an animal mixed model by ASReml software. In this model, parity and milk yield were fixed, while direct additive genetic, herd-year-season and permanent environment effects were considered as random factors. Genetic, phenotypic and environmental trends were estimated as weighted regression coefficients of breeding values, phenotypic values and environmental deviations averages on birth year, respectively and the number of observations was considered as the weighting factor. Generally, abortion risk in the first parity was lower than the later parities and in autumn was lower than the other seasons. Abortion risk also increased by increase in milk yield level. Heritability and repeatability estimates for abortion were both 0. 165 ± 0. 010. A significant positive genetic trend (0. 038 rate/year) and a significant negative environmental trend (-0. 033 rate/year) were estimated for abortion, which indicated significant increase of genetic predisposition to abortions and improvement of environ-mental conditions to prevent abortion incidence. A significant positive phenotypic trend (0. 002 rate/year) was also estimated, which showed overall increase of abortion rate during the studied years. Overall, it could be concluded that genetic predisposition to abortion is increasing in Iranian Holstein cows, but inclusion of abortion risk in bull proofs and selection indices may help to reduce abortion incidence and increase economic profit of dairy cattle industry.

A deterministic model was employed to simulate various progeny testing programs compatible with the conditions of Holstein dairy cattle population in Iran. In simulated programs, the selection intensity of bulls was increased by increasing the ratio of cow population in mate with young bulls. Selection accuracy in the programs was fixed because of the fixed progeny group. Economic efficiency (the ratio of return to cost) increased with a decline in the increase in the number of young bulls reaching to its maximum value of 6.07 by 29 young bulls. Genetic gain per generation and return of progeny testing programs increased by increase in the number of young bulls, until their maximum values reached. The maximum genetic gain values were 430.7683 Kg, and 21924417479 Rials, with 29 and 99 young bulls, respectively. The cost of program increased linearly with a value of 51984332 Rials per bull. As a consequence, the program with maximum genetic gain per generation or return would not necessarily maximize the economic efficiency. Sensitivity of economic efficiency and number of young bulls of optimized programs were investigated through a 20% fluctuation in economic parameters. As a result, economic efficiency of optimized program had the most sensitivity at 20% increase in discount rate and as well the economic value of milk production, and number of young bulls in the optimized program showed the most sensitivity to the 20% increase in discount rate. No change in the number of young bulls in the optimized program was observed through an increase of 20% in bull price, bull survival rate, semen storage costs, price of culled sires and economic value of milk production.

Introduction: The graphical representation of milk production in a lactation period in the form of a diagram is called a lactation curve. The lactation curve shows the biological efficiency of an animal and can be a tool for management and selection. The shape of the lactation curve is characterized by the initial incremental slope of the curve, the peak production value, the peak time, the slope of the curve after the peak production (continuation of lactation) and the length of the lactation period. The shape of the lactation curve provides valuable information that is essential for evaluating the biological and economic performance of an individual or herd and is useful for genetic evaluation and screening, health testing, nutritional management decisions, and planning purposes. Estimating the genetic parameters of the lactation curve and knowing the values of the parameters can be useful in designing breeding programs and predicting genetic improvements and herd management. According to the very limited research related to the use of the MilkBot function in fitting the lactation curves and the genetic investigation of curve parameters for Iranian Holstein cows, this research aims to fit the lactation curve of the first, second and third abdominal cows and the genetic investigation of the parameters of the lactation curve of cows was designed and executed. Material and Methods: In this study, 1650669, 1198923 and 781859 test day records of 122455, 91064 and 60024 first, second and third calving cows belonging to 17 herds between 2012 and 2023 that were collected by Arin Delta Gen International Company were used. The data were edited based on the following manner: lactation days between 5 and 325 days, the number of test day records for each animal at least 5 records, calving age for cows in the first parity between 21 and 48 months, for cows in the second lactation 33 to 60 and 45 to 72 were considered for cows in the third parity. Gaines, Wood, Wilmink and MilkBot Model were used to fit lactation curves. The NLIN procedure of SAS9. 4 software and Gauss-Newton algorithm were used to fit lactation curves. The AIC criterion was used to select the best model. After the parameters of the curve for the animals were individually fitted using the best model. The parameters estimated for the animals were used as traits to estimate the (co)variance components and genetic parameters. A multi-trait animal model was used to estimate (co)variance components. To estimate the (co)variance components, the restricted maximum likelihood method and AI algorithm used. Airemlf90 software was used to estimate variance components. Results and Discussion: The MilkBot model was chosen as the best model for all three lactations. It can be seen that cows with the first Lactation reached the peak of production later and also have better milking Persistency and these results are consistent with the physiology of cows and the production process of different lactation cows. Estimated heritability for the cows of the first lactation is equal to 0. 107 (0. 040), 0. 052 (0. 020), 0. 034 (0. 010) and 0. 019 (0. 020) respectively. The second is equal to 0. 110 (0. 050), 0. 014 (0. 010), 0. 029 (0. 020) and 0. 086 (0. 011) and for the cows of the third Lactation is 0. 123, (0. 010) 0. 078, (0. 051) 0. 026 and (0. 020) 0. 0631. The genetic correlation between parameters of lactation curve was obtained in the range of-0. 085 between parameters a and d of the first Lactation cows and 0. 891 between parameters a and b. Phenotypic correlation was obtained in the range of 0. 119 between b and c parameters of the second Lactation cows and 0. 697 between a and c parameters of the third Lactation cows. In general, the heritability’s obtained for the lactation curve are low and only first parameter has a relatively moderate heritability. The small heritability of these traits indicates that the lactation curve has been greatly influenced by environmental factors and as a result of the genetic selection of these traits there has been little genetic progress and it takes many generations to reach an optimal level. One of the possible ways to improve the lactation curve is to examine the traits that have a high genetic correlation with the parameters of the lactation curve and have suitable heritability, which can be used as a suitable criterion to improve the shape of the lactation curve. The range of genetic correlations is between-0. 085 to 0. 891 and the range of phenotypic correlations is between 0. 119 and 0. 697. The genetic correlation between parameter a and parameters b, c and d show that the highest genetic correlation is between parameter a and b and the phenotypic correlation is between parameter a and parameter c. The positive correlation between initial production and the increasing slope to peak production indicates that cows with higher initial production reach peak production at a suitable fast. Conclusion: Based on the findings of this research, the MilkBot model was chosen as a very efficient model in fitting the lactation curve and also estimating important parameters in the management and genetic improvement of herds. The heritability of the parameters of the lactation curve shows the low effect of the additive genetic effect on the shape and structure of the curve, and it can be concluded that genetic factors are less effective on the curve than other factors such as environmental factors, and as a result, a large number of generations is needed for the genetic improvement of the herd. It is necessary in terms of the lactation curve. Genetic correlation of lactation curve parameters with other traits can be an important and appropriate way to improve the genetics of lactation curve by selecting correlated traits.

A total of 72124 fertility records was used to estimate the genetic and phenotypic trend of fertility traits in Iranian Holstein cow from 1981 to 2007. Fertility traits in this study were: days from calving to first service (DFS), number of insemination per conception (INS), days open (DO), interval between first and last in semination (IFL), calving interval (CI) and success to first insemination (SF). The overall genetic trend in fertility traits was as desired and statistically significant. Mean breeding value of SF increase by 0.00067 percent per year. The annual genetic trends for INS, DFS, IFL, CI and DO were -0.0029 number/year, -0.062 days/year, -0.041 days/year, -0.23 days/year and -0.24 days/year, respectively. Phenotypes trends for fertility traits were unfavorable except for DFS and DO. Phenotypic trends in IFL, INS and SF were as un-desirable positive. Phenotypically DO and CI did not change over the time period. Phenotypically IFL has increased 1.6 days/year and DFS has decreased 1.6 days/year. The annual phenotypic trends for INS and SF were 0.04 and -0.018, respectively.

The present study aimed to estimate the effects of parity, calving season and year of calving on the prevalence of subclinical mastitis in Holstein cows. A total of 2682 records from 869 Holstein cows in a large dairy farm (Azarbaijan province, Iran), respecting the period from 2006 to 2009, were collected. Data was analyzed using Proc Mixed of SAS software by MIVQUE method. Subclinical mastitis was also studied based on three different models including: 1) considering each udder quarter as a separate unit (Udder quarter model), 2) considering all the four udder quarters of a cow as one overall unit (Subclinm model) and 3) considering the sum of positive subclinical mastitis scores in all udder quarters of an animal (Episode model). Diagnosis of subclinical mastitis was based on California Mastitis Test. Results showed that the prevalence of subclinical mastitis was 20.83%. The prevalence of cows with only one udder quarter affected by subclinical mastitis was 23.71%. Parity and year of calving significantly affected the prevalence of subclinical mastitis (P<0.001). Older cows with higher parity number had increased prevalence of subclinical mastitis. Therefore, the highest prevalence of subclinical mastitis was observed in cows having number of parities between 5 and 11. The lowest subclinical mastitis prevalence (using three mastitis models) was recorded in 2010, whilst its highest prevalence was observed in 2008. Season of calving also significantly influenced subclinical mastitis prevalence (P<0.05): cows calving in autumn had higher prevalence of subclinical mastitis than those calving in the other seasons. Concluding, data from the present study demonstrated that parity, calving season and year of calving influenced the prevalence of subclinical mastitis on the three mentioned models.

Fibroblast growth factor 2 (FGF2) serves in the uterine endometrium during estrous presenting in the bovine mammary gland which is responsible to express interferon-T (IFNT), and is an important agent to encourage the continuation of pregnancy in the ruminants. Significant associations have been found between genes affected by IFNT and genes that are responsible for milk production traits. Semen samples from 101 Iranian Holstein proven bulls were collected to extract the genomic DNA. Forward and reverse primers were designed and a 710-base-pair fragment in intron 1 was amplified using PCR technique. To detect single nucleotide polymorphism (SNP), all samples were sequenced. Three positions including 11474 (C/G), 11513 (C/G) and 11646 (A/G) were considered. The 11474C, 11513C and 11646A alleles are known as wild type alleles. In this study all animals were distinguished as the11474C, 11513C and 11646A alleles. Furthermore, amplified fragments were under consideration to detect new SNPs. Only one new SNP in one sample was observed at position 11863 resulting substitution of thymine to cytosine. This new mutation has been registered on the NCBI database with accession number HM597774.

Objective: Determination of morphology of the pigs car in Iranian Holstein and crossbred cattle. Design: Cross sectional study. Animals: Pigs ear from 72 Iranian Holstein and crossbred cattle. Procedure: In this investigation the pigs car shape and size of 72 cattle (38 male and 34 female) of Holstein and crossbred in Shiraz slaughterhouse were studied. According to their age, cattle were allocated into three groups including: 0-2 years, 2-4 years and older than 4 years. Statical analysis: ANOVA and Duncan Multiple range test. Results: Some animals possessed extra appendix and in three of them a small appendix of about one cm length. Statistical analysis revealed that males had heavier pigs ear than females (P<0.05). There was no significant difference between treatment groups. No significant difference was observed between the two breeds. Dimensions of the pigs ear including length and width were compared in accordance with age, sex and breed, and no significant difference was observed. Conclusion: The results reveal that the pigs ear of male cattle is heavier in both breeds and very different in shape.