The study objective was to evaluate the effects of reducing dietary starch content in fresh cow diets while maintaining NDF levels by substituting barley grain (BG), corn silage (CS), or both with beet pulp (BP) on DMI, lactation performance, serum mineral and metabolites concentrations, liver enzymes and liver functionality index (LFI), serum insulin and revised quantitative insulin sensitivity check index (RQUICKI). Thirty-six multiparous cows were randomly assigned to 1 of 4 experimental diets from calving to 21 days in lactation. Experimental diets were a high-starch diet with ground BG (CO; 24.9% starch; 0% BP) and 3 low-starch diets where BP substituted for either BG (BB; 19.6% starch; 7% BP), CS (BC; 20.6% starch; 12% BP) or CS and BG (BCB; 20.3% starch; 12% BP). Relative to CO cows (16.50 kg/d), DMI was greater for BC (17.70 kg/d) and BCB (17.50 kg/d) cows, but it was lesser in BB (15.60 kg/d) cows. Similar to DMI results, milk yields tended to be greater for BC (37.89 kg/d) and BCB cows (37.81 kg/d) compared to CO cows (35.41 kg/d), but BB cows (33.05 kg/d) tended to produce less milk than CO cows. Relative to CO, cows fed BB had lower serum glucose concentrations, whereas cows fed BC and BCB had higher serum glucose. Serum insulin concentrations were lower for BB cows than for other exprimental groups. Relative to CO, serum NEFA and BHB concentrations were lower for BC and BCB, but was similar between BB and CO. The RQUICKI was lower for CO, BC cows than BB cows, and cows in BCB tended to have less RQUICKI compared to BB during postpartum. The concentrations of gamma glutamyl transferase were lower in BC and BCB cows relative to CO and BB cows. The cows fed CO and BB had higher serum bilirubin relative to cows fed BC and BCB diets. Although, LFI for CO cows was similar to cows on BB, BC and BCB, BC cows had higher LFI than BB cows and tended to have greater LFI than CO cows. Overall, reducing dietary starch by replacing CS (BC) or a mix of CS and BG (BCB) with BP positively affected DMI and milk yield and indicated improved energy metabolism and liver function during the first 21 d of lactation compared to when BP was fed instead of BG to reduce starch (BB).

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.

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.

The objective of this study was to evaluate the effect of supplementing close-up diets with rumen undegradable protein on serum metabolites and the incidence of health disorders of Holstein dairy cows, during the hot season. Eighty-eight multiparous Holstein dairy cows were randomly assigned to 1 of 2 treatment groups: low crude protein (14.3% CP; 14CP) and high crude protein (17.1% CP; 17CP) diets. Blood samples were collected weekly from d -30 up to calving and then at 0, 5, 14, and 21 days after calving, for serum metabolites determination.  Cows fed the 17CP diet had higher serum concentrations of albumin, blood urea nitogen, and Mg than 14CP cows during the close-up period. In postpartum, the 17CP cows had higher serum albumin and creatinine and lower free fatty acid, BHB, and glucose concentrations than the 14CP cows. The risk to developing subclinical ketosis (SCK), metritis and endometritis were higher for the 14CP cows than the 17CP cows. Overall, feeding high rumen undegradable protein diet to close-up cows during the hot season increased serum creatinine concentrations and lowered the incidence of SCK and metritis and endometritis postpartum.

The objective of this study was to analyze the association of somatic cell score (SCS) with milk, fat and protein yields across parities. Records of production traits and somatic cell counts (SCC) from first, second and third parity that were collected by the Animal Breeding Center of Iran from 1993 to 2010 were used. All animals were grouped into six classes according to SCC and production traits were evaluated in these SCC classes. To obtain an approximate normal distribution, SCC records were transformed on the base of Loge to somatic cell score (LSCS, the lactation mean of the natural log of test-day somatic cell count). A single trait animal model that LSCS was fitted as covariate was used to assess relations of SCS with production traits in fist, second and third parities. Effect of SCS on production traits in all of parities was negative and significant (P<0.05). Ranges of depressing effects on milk, fat and protein production were 107.78-220.41, 2.32-7.08 and 2.1-5.2 kg per 1 unit increase in SCS.

Objective: The cows that don’t have access to sufficient energy in their ration can get sickness. Milk production has a high priority in metabolism of dairy cows so lactating cows are more prone to get sick This can ruin all the benefits of higher milk yield because of the higher prevalence of the disorders.Methods: Data for this research had gathered from a herd with 1600 Holstein cattle in East Azarbayjan province of Iran. Data were from 5052 lactation period of 1796 Holstein-Frisian cows which had parturition among 2005-2009. Seven groups of disorders have been analyzed. They are as follow: Cecal dilatation and rotation, diarrhea, abomasal distention and displacement to the right or left, locomotion problems and metritis. Each model consists of milk yield, parity, season and the year of calving. To determine the correlation between 305-day milk yield and occurring of disorders data have been analyzed with logistic regression model.Results: In this research current lactation was used as the lactation with occurrence of particular disorder. According to performed analysis on all cattle (heifers and the cows) higher milk yield was not a risk factor for the disorders occurrence except for diarrhea. Our outcomes showed a negative correlation between milk production with abomasal distention and left abomasal displacement. Analysis of correlation between prevalence of the disorders and 305- day yield showed a negative correlation with abomasal dilatation and displacement to the left and a positive correlation with diarrhea. Parity had a negative correlation with cecal rotation and dilatation, and a positive correlation with diarrhea. The year of calving had a significant correlation with abomasal dilatation and displacement of that to the right, cecal dilatation and rotation, diarrhea and locomotion problems. Calving season had only a significant relationship with cecal dilation and rotation. This research showed a complete correlation between some diseases and disorders in dairy cows and parameters that refer to milk production.

Introduction: The lack of sufficient information in the pedigree of the animals prevents the correct estimation of the breeding values. Henderson proposed a genetic grouping for a more realistic estimation of breeding values for phenotypic records in different years. For these groups, the birth year, the year that the first daughter of the male had recorded, or the year that the male animal was used for insemination were used for grouping. In fact, this grouping was considered for calculating the genetic trend over the years. The incomplete recording of the animals in the population will result in the elimination of true genetic relationships between animals. Although, these animals are considered as the base animal in the analysis, but not born at the same time, and can affect the accuracy of estimated breeding values. The available pedigree information in Iran does not have a good quality index. So that the average of pedigree completeness criterion for Iranian Holstein cows has been reported less than 0. 7. Genetic evaluation of Iranian Holstein cows with unknown parents may cause a bias in estimating genetic parameters and breeding values. The use of genetic groups in genetic analysis can partly correct the problem of animals with unknown parents. In this regard, the purpose of this study was to estimate the genetic parameters and breeding values of the production traits (milk, fat, protein) of Iranian Holstein cows with and without genetic grouping in model. Materials and Methods: In this study, the pedigree of 1555702 heads of the Iranian Holstein cattle from 14623 sires and 697940 dams that collected by Animal breeding center of Iran till 2013, were used. Production traits, including milk, fat and protein corrected for 305 days and twice milking from first to third lactation periods were used to estimate variance components and breeding values. Herds under 100 heads were not considered for analysis and for all production traits; pedigree related to animals with the record was extracted from the general pedigree using CFC software and used. For animals with unknown parents, genetic grouping was performed based on the sex and the birth year. Traits at different lactation periods analyzed with (model 2) and without (model 1) genetic grouping in the model and genetic trend was calculated. Then the accuracy of breeding values and genetic trend of traits obtained from different models were compared with each other. The Spearman rank correlation was used to investigate the change in animal ranking in a result of considering the genetic grouping. Results and Discussion: The additive genetic variance and their standard error were lower for milk, fat and protein production traits in model with genetic grouping (model 21) than the model without genetic grouping (model 1). The estimated heritability range for milk, fat and protein production in three lactation periods with model 1 was 0. 094-0. 162, 0. 069-0. 114, and 0. 079-0. 123, respectively, that these values were higher than model 21 in terms of magnitude. Spearman rank correlation between the estimated breeding values with model 1 and 21 was significantly different from 1, indicating a change in animal rank with consideration of genetic grouping in the model. The spearman rank correlation was lower for males than females, suggesting a higher change in male animal's rank than females. The average accuracy estimated breeding values with model 21 was higher than model 1 and the average accuracy difference was significant between two models. The genetic trend in the first, second and third lactation periods with the model 21 was estimated 63. 06, 59. 60 and 44. 64 for milk production, 1. 346, 1. 095 and 0. 943 for fat and 1. 542, 1. 514 and 1. 035 kg per year for protein, which were higher than the estimates of model 1. Conclusion: The results showed that consideration of genetic grouping in the model reduced the additive genetic variances of traits and the heritability estimated were higher without consideration of genetic grouping. The significance of the Spearman rank correlation indicates that the rank of males and females changed by inserting genetic groups into the model and change in the animal's rank for males was higher than females. The high accuracy of estimated breeding values and the genetic trends of traits in the model with genetic grouping suggests that genetic grouping for animals with unknown parents has been done and entered into the model in order to more accurately estimate the breeding values and to better reflect the performance of the breeding programs.