• 한국동물위생학회지
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• 제16권1호
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• pp.1-10
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• 1993

Samples of bulk herd milk, foremilk, last milk (stripping) and individual cow sample were collected and their somatic cell number were counted with Fossomatic counter (FCC), Coulter counter(CC), direct microscopic somatic cell count(DMSCC) and Califormia mastitis test (CMT), The results were compared and summarized as follows : 1. Mean somatic cell counts of 120 bulk herd milk samples obtained by DMSCC, FCC and CC were 433,203, 481,213 and 676,245 respectively. 2. Mean somatic cell counts of 116 foremilk samples obtained by DMSCC, FCC and CC were 515,035, 611,845 and 725,051 respectively 3. Mean somatic cell counts of 87 last milk samples obtained by DMSCC, FCC and CC were 718,506, 839,874 and 1,041,160 respectively. 4. Mean somatic cell counts of 57 individual cow samples obtained by DMSCC, FCC and CC were 449,258, 491,018 and 521,315 respectively. 5. Mean somatic cell counts of all samples increased with the increasing CMT score, and the cell counts were higher by CC than by FCC. 6. The correlation coefficients between the somatic cell counts by CMT and CC were 0.926 in bulk herd milk, 0.707 in foremilk 0.688 in last milk and 0.675 in individual cow sample, respectively 7. The correlation coefficients between the somatic cell counts by CMT and FCC were 0. 945 in bulk herd milk, 0.705 in foremilk 0.694 in last milk and 0.727 in individual cow sample, respectively. 8. The correlation coefficients between the somatic cell counts by CC and FCC were 0.978 in bulk herd milk, 0.997 in foremilk 0.983 in last milk and 0.985 in individual cow sample, respectively.

• Asian-Australasian Journal of Animal Sciences
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• 제13권10호
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• pp.1347-1352
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• 2000

The objectives of the study were to examine non-genetic factors that influence somatic cell counts in dairy cattle and to estimate the genetic parameters of somatic cell counts. A total of 34, 097-test day somatic cell count records were obtained from the Zimbabwe Dairy Services Association (ZDSA). The data were from 5, 615 Holstein daughters of 390 sires and 2, 541 dams tested between May 1994 and December 1998. First lactation cows contributed 22, 147 records to the data set, while 11, 950 records were from second and later parity cows. The model for analysis included fixed effects of month of calving, year of calving, stage of lactation, calving interval and test date. Milk yield and age on test day were fitted in the model as covariates. The additive genetic effects pertaining to cows, sires and dams and the residual error were the random effects. The Average Information Restricted Maximum Likelihood algorithm was used for analysis. The heritability of somatic cell scores was low at $0.027{\pm}0.013$ for parity one cows and $0.087{\pm}0.031$ for parity two and above. Repeatability estimates were $0.22{\pm}0.01$ and $0.30{\pm}0.01$ for the two lactation groups, respectively. Genetic and phenotypic correlations between the somatic cell scores and test day milk production were small and negative. It seems that there is no genetic link between somatic cell counts and milk yield in Holstein cattle in Zimbabwe. The results also seem to indicate that somatic cell count is a trait that is mainly governed by environmental factors.

• Asian-Australasian Journal of Animal Sciences
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• 제14권5호
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• pp.684-692
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• 2001

To find out the effect of oxytocin on somatic cell count and milk production, 12 primiparous and multiparous Murrah buffaloes were selected, immediately after the parturition, from the Institute's buffalo herd. These were divided into two groups of 6 each. Buffaloes of group I did not receive oxytocin injection (control); whereas, buffaloes of group II were administered oxytocin during early lactation (av. 42.50 days). The oxytocin injection was given in doses of 2.5 IU i.m. before the start of milking, to let down the milk, for a period of 5 days. Samples of milk from individual buffaloes were collected for 5 days before (Period I), during (Period II) and after (Period III) from both the group of buffaloes. Milk samples of A. M. and P. M. milking were composited in proposition to milk yields for analysis of milk constituents. Normal values of somatic cell counts in group I of buffaloes varied from 0.54 to $0.75{\times}10^{5}cells/ml$. Mean cytoplasmic particles and epithelial cells varied from 3.68 to $7.19{\times}10^{5}cells/ml$ and 0.13 to $0.54{\times}10^{5}cells/ml$. On percentage basis the epithelial and the total leucocyte count were 60 and 40. Total leucocyte count, in the study varied from 0.17 to $0.69{\times}10^{5}cells/ml$. The differential cell count of milk indicated presence of lymphocytes (16.50 to $61.16{\times}1000$), neutrophil (0.00 to $2.00{\times}1000$) and monocyte (0.00 to $18.16{\times}1000$). Somatic cell count (p<0.01) and epithelial cells (p<0.05) varied between buffaloes and between periods of study. Total leucocyte counts of milk were also significantly varied between periods (p<0.05). The change in fat, lactose, chloride, EC and NEFA concentrations during different periods of study, were highly significant, indicated diurnal variations in different buffaloes during different days of experiment. Administration of oxytocin resulted in increase in somatic cell counts of milk (p<0.01) due to the increases in total leucocyte count (p<0.01) during the treatment period. The differential cell count indicated that oxytocin administration increased lymphocyte number significantly (p<0.01). However, secretion of neutrophil, monocyte and cytoplasmic particles were not affected by oxytocin. Eosinophil and basophil cell, though present in few samples, remain unaffected by oxytocin administration. There was no effect of oxytocin on milk production, composition, pH, EC and NEFA concentration.

• 대한수의학회지
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• 제42권4호
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• pp.485-493
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• 2002

The purpose of this study was to develope a computer program to help with gross diagnosis of protein-energy balance and feeding management practice and with the prediction about the risk possibility of productive disease such as reproductive and metabolic disorders by evaluating fat, protein, and milk urea nitrogen (MUN) from individual cow milk in dairy herd Somatic cell counts also represent the condition of udder health. The principal flow charts of this program was to check on herd management, sampling the composite milk, analysis the milk composition, conversion of data from milking equipment to program, input and analysis of data in program, and report. This program is compatible with window 95/98 system. The major analytical elements of this program were presented as; the profile of herd lactation curve analysis of the test-day milk production level, the distribution of somatic cell count, the fat to protein ratio to evaluate body energy balance, and the interpretation of dietary protein-energy balance by milk protein and MUN contents for individual cows. This program using milk fat, protein, MUN, and somatic cell counts will serve as a monitoring tool for the protein-energy balance and the feeding management practice, and for distribution of mastitis in individual cows. It will also be used to manage the nutritional and reproductive disorders and mastitis at the farm level.

• 한국임상수의학회지
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• 제24권2호
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• pp.142-149
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• 2007

The purpose of this study was to develop a computerized software program to help for reproductive management in dairy cows. The developed software program in the study is compatible with a window 95/98 or XP system. Data in the system were recorded, stored, and processed from two sources: 1) Data downloaded monthly from the database of the Korean Dairy Herd Improvement Association (milk yields, milk somatic cell counts, milk fat, protein, lactose and urea nitrogen content). 2) Data recorded by the farmer or veterinarians by the time (body condition score, heats, inseminations, veterinary diagnosis and treatments). These data indices after processing by computerized dairy management system were presented by numerical or graphical display. The presented data were obtained from three dairy farms with more than 50 milking cows. The presented reports of this program using milk fat, protein, urea nitrogen, and somatic cell counts enabled the dairy producer and veterinarians to monitor the protein-energy balance and feeding management practice, and for distribution of diseases (mastitis, metabolic and reproductive disorder) in individual cows. The presented analytical reports of this program also included herd average of reproductive indices such as day to first insemination, days open, and inseminations per conception. This software program will assist in analysis, interpretation and demonstrate the results of reproductive trials conducted in dairy herds.

• Asian-Australasian Journal of Animal Sciences
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• 제17권4호
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• pp.479-484
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• 2004

The study was conducted to assess the effect of milk production, parity, stage of lactation, season and individual milk components themselves on milk urea nitrogen (MUN) concentration and other milk components of 3,219 Holstein dairy cows in Korean dairy farms. The MUN concentrations in Korean dairy cows were estimated to 16.68$\pm$5.87 mg/dl. Milk yield was negatively correlated with fat and protein contents and somatic cell counts (SCC) in milk (p<0.01). The increasing MUN concentration has positive correlation with yield and fat content. By increasing somatic cell, milk yield was reduced and MUN level was increased. Cows in spring and winter produced more milk over 1.43 and 0.93 kg/day, respectively, than cows in summer (p<0.01). Milk urea nitrogen concentrations of milk produced in summer and fall were significantly lower (p<0.01) than those in spring and winter. Both MUN concentration and somatic cell counts were highest in winter. Milk yield was lower (p<0.01) in the first calving than other calving time and was tended to increase until the fifth parity and then decrease. Milk urea nitrogen and SCC were not related to parity of cows in this study. Milk yield and SCC were positively related to lactation period while MUN concentrations and milk fat and protein contents were negatively influenced by stage of lactation. In the present study, the relationship between MUN and reproduction of dairy cows was also investigated. Cow produced milk in high MUN concentrations (greater than 18 mg/dl) had more open days than cows in MUN concentrations less than 18 mg/dl. However, no significant difference between MUN concentration levels and frequency of artificial insemination was found in this study. It is suggested that although MUN values for nutritional management and measures of production or reproduction are used, non-nutritional factors should be considered.

• 한국동물위생학회지
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• 제14권2호
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• pp.87-103
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• 1991

The somatic cell counts SCC and bacteria counts were done by D milk plant, P milk plant, S milk plant and Inch'${\v{\times}}n$ Vet. Serv. Lab from 1987 to 1990 with Coulter counter, Fossomatic 90, Bactoscan, Rolling ball viscometer and Resazurin reduction test. The results were summarized as follows 1. In the distribution of SCC of the bulk herd milk, D milk plant from Nov. 1989 to Oct. 1990 remarks 80.2% on the range below 500, 000, 14.5% ranging from 1, 000, 000 to 1, 500, 000, 1.2% ranging from 1, 500, 000 to 2, 000, 000, 0.69% ranging from 2, 000, 000 to 3, 000, 000, 0.71% on the range over 3, 000, 000. P milk plant remarks 237, 000 in the first half year and 251, 000 in the second half year in 1990 year. S milk plant remarks annual average of 335, 000 in 1987, 273, 000 in 1988 and 262, 000 in 1989. The individual record of Inch'${\v{\times}}n$ Vet. Serv Lab. remarks 79.35% and 80.2% below 500, 000 8.30% and 7.40% from 500, 000 to 1, 000, 000, 2.37% and 3.2% from 1, 000, 000 to 1, 500, 000, 2.77% and 2.30% from 1, 500, 000 to 2, 000, 000, 1.67% and 2.00% from 2, 000, 000 to 3, 000, 000, 5.53% and 4.40% over 3, 000, 000 in 1989 and 1990, respectively. The grade distirbution of SCC is as follows: D milk plant shows 1st grade-80.20%, 2nd grade-l6.5% and 3rd grade-3.30%. And P milk plant shows all 1st grade. S milk plant shows 87.30%, 8.6% and 4.1% in 1987 and 91.90%, 6.1% and 2.0% in 1988, and 92.40%, 6.1% and l.5% in 1989 on the 1st, 2nd and 3rd grade respectively. 2. The distribution of bacteria P milk plant reached 15.123 in 1st half year and 21.515 in 2nd half year. Also, S milk plant reached 81.5%, 12.5%, 6.0% in 1987, and 86.20%, 9.70%, 4.1% in 1988, and 86.2%, 10.8%, 3.0% in 1989 respectively for 1st, 2nd and 3rd grade. 3. The regional SCC distribution in D milk plant shows 1, 540, 000 in three regions and 714, 000 in one region. And monthly SCC distribution shows 671, 000 in December and 1, 165, 000 in June. 4. As a result of the individual SCC test, 9 times for 16 cows in “I”farm(1986-1988), and 6 times for 13 cows in“D”farm(1987-1988) No.3, 5, 9, 14 cows in“I”farm showed the high SCC beyond 1, 000, 000 over 4-5times. 5. If the SCC over 300, 000 reach 40%, the national producing quality of milk can be reduced by 87, 600M /I annually and in the sum of money, it should be about 35.5 billion Won. 6. The difference between high group and low group for SCC in D milk plant reached over 1, 000, 000. In case that the difference reaches 1, 000, 000 in the farm bulk milk at a farm breeding 20 cows which produce 20kg milk per day, it was estimate that the annual difference of producing quantity and sum of money respectively should be reached 26, 280kg in milk and 10, 643, 400 Won in income.

• Asian-Australasian Journal of Animal Sciences
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• 제28권3호
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• pp.303-310
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• 2015

The study was conducted to analyze the genetic parameters of somatic cell score (SCS) of Holstein cows, which is an important indicator to udder health. Test-day records of somatic cell counts (SCC) of 305-day lactation design from first to fifth lactations were collected on Holsteins in Korea during 2000 to 2012. Records of animals within 18 to 42 months, 30 to 54 months, 42 to 66 months, 54 to 78 months, and 66 to 90 months of age at the first, second, third, fourth and fifth parities were analyzed, respectively. Somatic cell scores were calculated, and adjusted for lactation production stages by Wilmink's function. Lactation averages of SCS ($LSCS_1$ through $LSCS_5$) were derived by further adjustments of each test-day SCS for five age groups in particular lactations. Two datasets were prepared through restrictions on number of sires/herd and dams/herd, progenies/sire, and number of parities/cow to reduce data size and attain better relationships among animals. All LSCS traits were treated as individual trait and, analyzed through multiple-trait sire models and single trait animal models via VCE 6.0 software package. Herd-year was fitted as a random effect. Age at calving was regressed as a fixed covariate. The mean LSCS of five lactations were between 3.507 and 4.322 that corresponded to a SCC range between 71,000 and 125,000 cells/mL; with coefficient of variation from 28.2% to 29.9%. Heritability estimates from sire models were within the range of 0.10 to 0.16 for all LSCS. Heritability was the highest at lactation 2 from both datasets (0.14/0.16) and lowest at lactation 5 (0.11/0.10) using sire model. Heritabilities from single trait animal model analyses were slightly higher than sire models. Genetic correlations between LSCS traits were strong (0.62 to 0.99). Very strong associations (0.96 to 0.99) were present between successive records of later lactations. Phenotypic correlations were relatively weaker (<0.55). All correlations became weaker at distant lactations. The estimated breeding values (EBVs) of LSCS traits were somewhat similar over the years for a particular lactation, but increased with lactation number increment. The lowest EBV in first lactation indicated that selection for SCS (mastitis resistance) might be better with later lactation records. It is expected that results obtained from these multi-trait lactation model analyses, being the first large scale SCS data analysis in Korea, would create a good starting step for application of advanced statistical tools for future genomic studies focusing on selection for mastitis resistance in Holsteins of Korea.

• Near Infrared Analysis
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• 제2권1호
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• pp.59-66
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• 2001

Nowadays, medical diagnostics is efficiently supported by clinical chemistry and near infrared spectroscopy is becoming a new dimension, which has shown high potential to provide valuable information for diagnosis. The investigation was carried out to study the influence of mammary gland inflammation, called mastitis, on cow´s milk spectra and milk composition measured by near infrared spectroscopy (NIRS). Milk somatic cell counts (SCC) in milk were used as a measure of mammary gland inflammation. Naturally occurred variations with milk composition within lactation and in the process of milking were included in the experimental design of this study. Time series of unhomogenized, raw milk spectral data were collected from 3 cow along morning and evening milking, for 5 consecutive months, within their second lactation. In the time of the trial, the investigated cows had periods with mammary gland inflammation. Transmittance spectra of 258 milk samples were obtained by NIRSystem 6500 spectrophotometer in 1100-2400 nm region. Calibration equations for the examined milk components were developed by PLS regression using 3 different sets of samples: samples with low somatic cell count (SCC), samples with high SCC and combined data set. The NIR calibration and prediction of individual cow´s milk fat, protein, and lactose were highly influenced by the presence of mil samples from animals with mammary gland inflammation in the data set. The best accuracy of prediction (i.e. the lower SEP and the higher correlation coefficient) for fat, protein and lactose was obtained for equations, developed when using only “healthy” samples, with low SCC. The standard error of prediction increased and correlation coefficient decreased significantly when equations for low SCC milk were used to predict examined components in “mastitis” samples with high SCC, and vice versa. Combined data set that included samples from healthy and mastitis animals could be used to build up regression models for screening. Further use of separate model for healthy samples improved milk composition measurement. Regression vectors for NIR mild protein measurement obtained for “healthy” and “mastitic” group were compared and revealed differences in 1390-1450 nm, 1500-1740 nm and 1900-2200 nm regions and thus illustrated post-secretory breakdown of milk proteins by hydrolytic enzymes that occurred with mastitis. For the first time it has been found that monitoring the spectral differences in water bands at 1440 nm and 1912 nm could provide valuable information for inflammation diagnosis.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1031-1031
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• 2001

The mammary gland is made up of remarkably sensitive tissue, which has the capability of producing a large volume of secretion, milk, under normal or healthy conditions. When bacteria enter the gland and establish an infection (mastitis), inflammation is initiated accompanied by an influx of white cells from the blood stream, by altered secretory function, and changes in the volume and composition of secretion. Cell numbers in milk are closely associated with inflammation and udder health. These somatic cell counts (SCC) are accepted as the international standard measurement of milk quality in dairy and for mastitis diagnosis. NIR Spectra of unhomogenized composite milk samples from 14 cows (healthy and mastitic), 7days after parturition and during the next 30 days of lactation were measured. Different multivariate analysis techniques were used to diagnose the disease at very early stage and determine how the spectral properties of milk vary with its composition and animal health. PLS model for prediction of somatic cell count (SCC) based on NIR milk spectra was made. The best accuracy of determination for the 1100-2500nm range was found using smoothed absorbance data and 10 PLS factors. The standard error of prediction for independent validation set of samples was 0.382, correlation coefficient 0.854 and the variation coefficient 7.63%. It has been found that SCC determination by NIR milk spectra was indirect and based on the related changes in milk composition. From the spectral changes, we learned that when mastitis occurred, the most significant factors that simultaneously influenced milk spectra were alteration of milk proteins and changes in ionic concentration of milk. It was consistent with the results we obtained further when applied 2DCOS. Two-dimensional correlation analysis of NIR milk spectra was done to assess the changes in milk composition, which occur when somatic cell count (SCC) levels vary. The synchronous correlation map revealed that when SCC increases, protein levels increase while water and lactose levels decrease. Results from the analysis of the asynchronous plot indicated that changes in water and fat absorptions occur before other milk components. In addition, the technique was used to assess the changes in milk during a period when SCC levels do not vary appreciably. Results indicated that milk components are in equilibrium and no appreciable change in a given component was seen with respect to another. This was found in both healthy and mastitic animals. However, milk components were found to vary with SCC content regardless of the range considered. This important finding demonstrates that 2-D correlation analysis may be used to track even subtle changes in milk composition in individual cows. To find out the right threshold for SCC when used for mastitis diagnosis at cow level, classification of milk samples was performed using soft independent modeling of class analogy (SIMCA) and different spectral data pretreatment. Two levels of SCC - 200 000 cells/$m\ell$ and 300 000 cells/$m\ell$, respectively, were set up and compared as thresholds to discriminate between healthy and mastitic cows. The best detection accuracy was found with 200 000 cells/$m\ell$ as threshold for mastitis and smoothed absorbance data: - 98% of the milk samples in the calibration set and 87% of the samples in the independent test set were correctly classified. When the spectral information was studied it was found that the successful mastitis diagnosis was based on reviling the spectral changes related to the corresponding changes in milk composition. NIRS combined with different ways of spectral data ruining can provide faster and nondestructive alternative to current methods for mastitis diagnosis and a new inside into disease understanding at molecular level.