The relationship between milking interval and somatic cell count in automatic milking systems

The aim of this study was to explore whether, during automatic milking, milking interval or its variation is related to somatic cell count (SCC), even when corrected for effects of production, lactation stage, and parity. Data on milking interval and production level were available from the automatic milking systems of 151 farms. Data on SCC, parity, and lactation stage were derived from dairy herd improvement records of the same farms. Mainly due to incomplete records, data of 100 farms were used in the final analysis. For every cow, only 1 test day was used in the final analysis. Milking interval, the coefficient of variation of milking interval, production rate, the difference in production rate between short- and long-term, parity, days in milk, and some biologically relevant interactions were used in a linear mixed model with farm as random variable to assess their association with log10-transformed SCC. None of the interactions was significantly related to SCC, whereas all main effects were, and thus, stayed in the final model. The effect of milking interval was, although significant, not very strong, which shows that the effect of milking interval on SCC is marginal when corrected for the other variables. The variation in milking intervals was positively related with SCC, showing that the variation in milking interval is even more important than the milking interval itself. In the end, this study showed only a limited association between milking interval and SCC when milking with an automatic milking system.     

Regularly fluctuating somatic cell count pattern in dairy herds

Online somatic cell count (SCC) measurement is widely used in dairy herds milked with automatic milking systems (AMS) and gives the opportunity to closely monitor individual cow udder health. Using automated SCC data, we observed cows displaying a remarkably regularly fluctuating SCC (rfSCC) pattern, which is described in this study. We aimed to (1) estimate the prevalence of rfSCC in cows milked by AMS, (2) characterize the rfSCC pattern, and (3) identify factors potentially associated with the rfSCC pattern. We analyzed 30-d episodes of composite SCC recordings of 1,000 cows from 55 dairy herds from 6 countries using an AMS with automated SCC measurement, and we identified the rfSCC pattern in 4.7% (95% CI: 3.5–6.2%) of these episodes. The rfSCC episodes had a median SCC of 701 × 1,000 cells/mL (2.5–97.5% quantile: 539–1,162), a median amplitude of 552 × 1,000 cells/mL (2.5–97.5% quantile: 409–886), and a median cycle length of 4.1 d (2.5–97.5% quantile: 3.7–4.9). Bacteriological culture data from quarter-milk samples collected every 2 wk in 1 Dutch AMS herd were analyzed, yielding no clear association between pathogen species and the rfSCC pattern found in that herd. Altogether, we described an intriguing phenomenon, present in almost 5% of the cows during a 1-mo study period. Further work is needed to quantify its importance in terms of udder health, but also to elucidate the mechanism behind this remarkable SCC pattern.     

Effect of herd characteristics, management practices, and season on different categories of the herd somatic cell count

In this study, the contribution of management practices, herd characteristics, and seasonal variables to the herd somatic cell count (SCC) was quantified in herds with low (<150,000 cells/mL), medium (150,000-200,000 cells/mL), and high (>200,000 cells/mL) herd SCC (HSCC). Selection of the variables was performed using a linear mixed effect model; HSCC was calculated as the arithmetic mean of the individual cow's SCC. The data concerning management practices were derived from 3 questionnaires on mastitis prevention and management practices on 246 Dutch dairy farms. The monthly Dairy Herd Improvement test data of these 246 farms were used to calculate the herd characteristics and seasonal effects. None of the management practices were associated with HSCC in all 3 HSCC categories. Some variables only had a significant association with HSCC in one HSCC category, such as dry premilking treatment (−9,100 cells/mL in the low HSCC category) or feeding calves with high SCC milk (11,100 cells/mL in the medium HSCC category). Others had an opposite effect on HSCC in different HSCC categories, such as average parity (−6,400 and 11,000 cells/mL in the low and medium HSCC category, respectively) and feeding calves with fresh milk (10,300 and −9,700 cells/mL in the low and high HSCC category, respectively). We conclude that, given the individual Dairy Herd Improvement data and information on management practices of an individual farm, it is possible to provide quantitative insight into the contribution of these different variables to the HSCC of an individual farm. Being able to provide such insight is a prerequisite for interpretation, prediction, and control of HSCC on individual dairy farms.     

Invited review: Low milk somatic cell count and susceptibility to mastitis

An enduring controversy exists about low milk cell counts and susceptibility to mastitis. The concentration of milk leukocytes, or somatic cell count (SCC), is a well-established direct indicator of mammary gland inflammation that is highly correlated with the presence of a mammary infection. The SCC is also used as a trait for the selection of dairy ruminants less prone to mastitis. As selection programs favor animals with less SCC, and as milk cells contribute to the defense of the mammary gland, the idea that susceptibility to mastitis could possibly be increased in the long term has been put forward and is still widely debated. Epidemiological and experimental studies aimed at relating SCC to susceptibility to mastitis have yielded results that seem contradictory at first sight. Nevertheless, by taking into account the immunobiology of milk and mammary tissue cells and their role in the defense against infection, along with recent studies on SCC-based divergent selection of animals, the issue can be settled. Apparent SCC-linked susceptibility to mastitis is a phenotypic trait that may be linked to immunomodulation but not to selection.     

Herd management practices and their association with bulk tank somatic cell count on United States dairy operations

The objective of this study was to evaluate associations between bulk tank somatic cell count (BTSCC) and herd management practices using data collected in the National Animal Health Monitoring System Dairy 2002 study. Twenty-six percent and 17.8% of 1,013 operations reported a BTSCC <200,000 cells/mL and >400,000 cells/mL, respectively. Univariate analysis identified associations between management variables and BTSCC. The use of mattresses, sand, and newspaper as bedding were all associated with a lower BTSCC. Primary lactating cow housing facility, outside maternity housing area, flooring type cows walk or stand on, and use of automatic take-offs were also associated with BTSCC. Multivariate associations between management variables and BTSCC were determined by backward elimination ordinal logistic regression. The odds of an operation from the West, Midwest, and Northeast having a high BTSCC were lower than those from the Southeast. The odds of a higher BTSCC were 2 times greater for operations with a rolling herd average milk production <9,090 kg/cow per year compared with those with ≥9,090 kg/cow per year. Operations using composted manure were 2.9 times more likely to have a higher BTSCC than those not using composted manure. Finally, operations that reported not using a coliform mastitis vaccine were 1.7 times more likely to have a higher BTSCC than those using one. Future studies of the association between management practices and BTSCC should include an evaluation of the quality of management practice application and herd prevalence of contagious mastitis pathogens. Significant variables identified in this study dealt with housing, use of composted manure for bedding, and coliform mastitis vaccine use, suggesting the effect of environmental mastitis pathogens may be more influential on BTSCC than previously thought.     

Prediction of the herd somatic cell count of the following month using a linear mixed effect model

An accurate prediction of the average somatic cell count (SCC) for the next month would be a valuable tool to support udder health management decisions. A linear mixed effect (LME) model was used to predict the average herd SCC (HSCC) for the following month. The LME model included data on SCC, herd characteristics, season, and management practices determined in a previous study that quantified the contribution of each factor for the HSCC. The LME model was tested on a new data set of 101 farms and included data from 3 consecutive years. The farms were split randomly in 2 groups of 50 and 51 farms. The first group of 50 farms was used to check for systematic errors in predicting monthly HSCC. An initial model was based on older data from a different part of the Netherlands and systematically overestimated HSCC in most months. Therefore, the model was adjusted for the difference in average HSCC between the 2 sets of farms (from the previous and current study) using the data from the first group of 50 farms. Subsequently, the data from the second group of 51 farms were used to independently assess this final model. A null model (no explanatory variables included) predicted 48 and 59% of the HSCC within the predetermined range of 20,000 and 30,000 cells/mL, respectively. The final LME model predicted 72 and 81% of the HSCC of the next month correctly within these 2 ranges. These outcomes indicate that the final LME model was a valid additional tool for farmers that could be useful in their short-term decisions regarding udder health management and could be included in dairy herd health programs.     

Factors of variation influencing bulk tank somatic cell count in dairy sheep

Between January and December 2002, a total of 21,685 records for bulk tank milk somatic cell count (BTSCC) were obtained from 309 dairy ewe herds belonging to the Sheep Improvement Consortium in Castilla-Leon, Spain. Based on the first statistical model, ANOVA detected significant effects of herd, breed, month within herd, dry therapy, type of milking, contagious agalactia, and installations within machine milking on logBTSCC. A second statistical model was used on herds with machine milking to study the effect of the vacuum level and pulsation rate on BTSCC. Herd and month within herd were important variation factors as they explained 48.4 and 16.1% of the variance in BTSCC. Variability in logBTSCC among breeds ranged from 5.84 (Castellana) to 6.09 (Awassi and Spanish Assaf). Implementing dry-ewe therapy (5.91) significantly reduced logBTSCC compared with when it was not implemented (6.10). Hand milking elicited greater logBTSCC (6.07) than machine milking (5.94). Machine milking of ewes in milking parlors (logBTSCC: 5.88 to 5.94) was associated with better udder health than was the use of bucket-milking machines (6.04). Reduced vacuum levels and elevated pulsation rate during machine milking optimized BTSCC. In all cases, clinical outbreaks of contagious agalactia increased BTSCC. As a result, dry therapy was proposed as the main tool to reduce BTSCC. Optimization of milking-machine standards and parlor systems also improved udder health in dairy sheep.     

Estimating milk loss based on somatic cell count at the cow and herd level

There is a direct relationship between elevated somatic cell count (SCC) in an individual cow milk production and milk loss. This relationship has been used at the herd level to estimate an overall herd milk loss due to subclinical mastitis and to use recovery of this lost milk as a financial benefit to cover the cost of intervention strategies to improve milk quality. The objective of this study was to estimate the recoverable milk revenue on a per cow basis for herds moving from one herd average SCC level to a newer, lower level. Test-day records from 1,005,697 dairy cows in 3,741 herds between 2009 to 2019 were used. Milk yield loss for each cow in each herd on test day was estimated using a mixed effects regression equation, and then summed to estimated total herd milk loss. These herd average daily milk loss estimates were then related to the bulk tank SCC, and the distribution of underlying individual cow SCC were examined. The distributions in daily herd milk loss for various bulk tank SCC values were generated, and estimates of recoverable milk loss were generated to simulate a herd moving from their current bulk tank SCC to a new lower level. The results indicate that estimates of total herd milk yield loss vary with the distribution of cow-level SCC and parity within the herd, so it is imperative that milk loss be calculated on a per cow basis. Further, the recoverable milk loss estimates based on moving to a lower bulk tank SCC where milk loss is still occurring was relatively small compared with the traditional assumption that all milk loss would be recovered, and less than most herd owners and advisors would expect.     

Invited review: udder health of dairy cows in automatic milking

Automatic milking (AM) is increasing in modern dairy farming, and over 8,000 farms worldwide currently use this technology. Automatic milking system is designed to replace conventional milking managed by a milker in a milking parlor or in tie stalls. Cows are generally milked more frequently in AM than in conventional milking, and milking is quarter-based instead of udder-based. Despite improvements in the milking process and often building of a new barn before the introduction of AM, udder health of the cows has not improved; on the contrary, problems may appear following conversion from conventional milking to AM. This review focuses on udder health of dairy cows in AM, and we discuss several aspects of cow and milking management in AM associated with udder health. Finally, adequate management methods in AM are suggested. According to several studies comparing udder health between automatic and conventional milking or comparing udder health before and after the introduction of automatic milking in the same herds, udder health has deteriorated during the first year or more after the introduction of AM. Automatic detection of subclinical and clinical mastitis and cleaning the teats before milking are challenges of AM. Failures in mastitis detection and milking hygiene pose a risk for udder health. These risk factors can partly be controlled by management actions taken by the farmer, but AM also needs further technical development. To maintain good udder health in AM, it is imperative that the barn is properly designed to keep the cows clean and the cow traffic flowing. Milking frequency must be maintained for every cow according to its stage of lactation and milk production. Careful observation of the cows and knowledge of how to use all data gathered from the system are also important. “Automatic” does not mean that the role of a competent herdsman is in any way diminished.     

乳体细胞数对干酪生产的影响

论述了原料乳中体细胞数对干酪加工、成熟和产量的影响,以期为干酪生产提供理论参考.     

The effect of organic status and management practices on somatic cell counts on UK dairy farms

The numbers of organic dairy farms are increasing in the United Kingdom and in other parts of the world. On organic farms, the use of veterinary medicines is restricted. Because of this, there is concern that cow health is poorer on these farms. As udder health is primarily maintained by the use of antimicrobials, the effect of organic status on mastitis and somatic cell counts (SCC) is important to investigate. The aim of this study was therefore to determine whether the organic status and other management factors affect SCC. A group of 80 dairy farms was used in the study: 40 organic farms and 40 nonorganic farms. The farms were recruited in pairs, and each organic:nonorganic pair was matched for herd size, housing type, genetic merit for milk production and geographical location. Somatic cell count data were extracted from national databases for a standard year (2004), and analyzed using stepwise logistic regression models. The organic status of the farm did not appear in the final model, indicating no major influence of organic status on SCC. There were, however, several effects of management on SCC. Somatic cell counts were lower on farms where the udders were not cleaned or cleaned only when dirty. Somatic cell counts were also lower on farms that kept cows in larger management groups and where the majority, but not all cases of mastitis are treated with antimicrobials. It can be concluded that the control measures used on the organic farms in this study are at least as effective as those used on nonorganic farms in controlling SCC. Other management factors are influential and attention to these factors will allow farmers to reduce SCC.     

Distribution of non-aureus staphylococci species in udder quarters with low and high somatic cell count,and clinical mastitis

The effect of non-aureus staphylococci (NAS) in bovine mammary health is controversial. Overall, NAS intramammary infections (IMI) increase somatic cell count (SCC), with an effect categorized as mild, mostly causing subclinical or mild to moderate clinical mastitis. However, based on recent studies, specific NAS may affect the udder more severely. Some of these apparent discrepancies could be attributed to the large number of species that compose the NAS group. The objectives of this study were to determine (1) the SCC of quarters infected by individual NAS species compared with NAS as a group, culture-negative, and major pathogen-infected quarters; (2) the distribution of NAS species isolated from quarters with low SCC (<200,000 cells/mL) and high SCC (≥200,000 cells/mL), and clinical mastitis; and (3) the prevalence of NAS species across quarters with low and high SCC. A total of 5,507 NAS isolates, 3,561 from low SCC quarters, 1,873 from high SCC quarters, and 73 from clinical mastitis cases, were obtained from the National Cohort of Dairy Farms of the Canadian Bovine Mastitis Research Network. Of quarters with low SCC, high SCC, or clinical mastitis, 7.6, 18.5, and 4.3% were NAS positive, respectively. The effect of NAS IMI on SCC was estimated using mixed-effect linear regression; prevalence of NAS IMI was estimated using Bayesian analyses. Mean SCC of NAS-positive quarters was 70,000 cells/mL, which was higher than culture-negative quarters (32,000 cells/mL) and lower than major pathogen-positive quarters (129,000 to 183,000 cells/mL). Compared with other NAS species, SCC was highest in quarters positive for Staphylococcus capitis, Staphylococcus gallinarum, Staphylococcus hyicus, Staphylococcus agnetis, or Staphylococcus simulans. In NAS-positive quarters, Staphylococcus xylosus (12.6%), Staphylococcus cohnii (3.1%), and Staphylococcus equorum (0.6%) were more frequently isolated from quarters with low SCC than other NAS species, whereas Staphylococcus sciuri (14%) was most frequently isolated from clinical mastitis cases. Finally, in NAS-positive quarters, Staphylococcus chromogenes, S. simulans, Staphylococcus epidermidis, and Staphylococcus haemolyticus were isolated with similar frequency from among low SCC and high SCC quarters and clinical mastitis cases. Staphylococcus chromogenes, S. simulans, S. xylosus, S. haemolyticus, S. epidermidis, S. agnetis, Staphylococcus arlettae, S. capitis, S. gallinarum, S. sciuri, and Staphylococcus warneri were more prevalent in high than in low SCC quarters. Because the NAS are a large, heterogeneous group, considering them as a single group rather than at the species, or even subspecies level, has undoubtedly contributed to apparent discrepancies among studies as to their distribution and importance in IMI and mastitis.     

Short communication: Effect of dry therapy using an intramammary infusion on bulk tank somatic cell count in sheep

A total of 3,141 records of bulk tank milk somatic cell counts (BTSCC) and bulk tank milk total bacterial counts (BTTBC) were obtained over 24 mo from 25 dairy flocks of Assaf ewes belonging to the Consortium for Ovine Promotion in Castilla-León, Spain, in which a complete dry therapy program was carried out in 10,313 ewes using an antibiotic infusion containing 100 mg of penethamate hydriodide, 280 mg of benethamine penicillin, and 100 mg of framycetin sulfate. The selection criteria for all flocks were BTSCC mean values ≥1,000 × 10³ cells/mL and absence of dry therapy before the start of this experiment. Significant effects on log BTSCC were detected for treatment, milking system, flock within milking system, month within flock by treatment, the interactions treatment by milking system and flock by treatment within milking system, and log BTTBC. After dry therapy was implemented, log BTSCC decreased significantly in machine-milked flocks (5.95 ± 0.007) compared with values before antibiotic treatment (6.13 ± 0.008). The effect was observed at the beginning of the second lactation posttreatment (5.98 ± 0.013). However, dry therapy was not effective in hand-milked flocks, suggesting poor hygiene conditions. A significant relationship was found between BTSCC and BTTBC; therefore, programs for improving milk hygiene should be implemented for both BTSCC and BTTBC variables at the same time.     

The effect of season on somatic cell count and the incidence of clinical mastitis

Bulk milk somatic cell count (BMSCC), individual cow somatic cell count (ICSCC), and incidence rate of clinical mastitis (IRCM) are all udder health parameters. So far, no studies have been reported on the effect of season on BMSCC, IRCM, and ICSCC in the same herds and period over multiple years. The objectives of this study were to determine the seasonal pattern over a 4-yr period of 1) BMSCC, 2) elevated ICSCC, 3) IRCM, and 4) pathogen-specific IRCM. Bulk milk somatic cell count, ICSCC, and pathogen-specific clinical mastitis data were recorded in 300 Dutch dairy farms. For the analyses of BMSCC, ICSCC, and IRCM, a mixed, a transitional, and a discrete time survival analysis model were used, respectively. Sine and cosine were included in the models to investigate seasonal patterns in the data. For all parameters, a seasonal effect was present. Bulk milk somatic cell count peaked in August to September in all 4 years. The probability of cows getting or maintaining a high ICSCC was highest in August and May, respectively. Older and late-lactation cows were more likely to develop or maintain a high ICSCC. Incidence rate of clinical mastitis was highest in December to January, except for Streptococcus uberis IRCM, which was highest in August. Totally confined herds had a higher Escherichia coli IRCM in summer than in winter. Compared with the major mastitis pathogens, the seasonal differences in IRCM were smaller for the minor pathogens. Distinguishing between Strep. uberis, Streptococcus dysgalactiae, Streptococcus agalactiae, and other streptococci is essential when identifying Streptococcus spp. because each of them has a unique epidemiology. Streptococcus uberis IRCM seemed to be associated with being on pasture, whereas E. coli IRCM was more housing-related.     

Predicting somatic cell count standard violations based on herd's bulk tank somatic cell count. Part I: Analyzing variation

The present study examines the relationship between the bulk tank somatic cell count (SCC) mean and sigma (an estimate of variation) and the probability of exceeding a SCC standard. Daily or every other day, bulk tank SCC data were collected for 24 mo from 1,501 herds. Mean and sigma were estimated for each herd monthly and were compared between months and herd production categories using Kruskal-Wallis nonparametric ANOVA. The effect of month on bulk tank SCC mean and sigma was significant, with estimates for all summer months and most of the spring and fall months being significantly greater than estimates of mean and sigma in December 2004. Logistic regression models were developed to examine the relationship between month and herd production and the odds of a herd exceeding a SCC standard. The odds of exceeding a bulk tank SCC standard were significantly greater in the summer months and for smaller herds. A grid was constructed determining the probability of exceeding any of 5 SCC standards (200,000 to 600,000 cells/mL, step 100,000 cells/mL) in the following month, based on the mean and sigma of the past month. The violation probability grid can be used to assess the prospect of meeting quality premium goals and to proactively encourage more consistent performance in all the processes affecting bulk tank SCC.     

体细胞数对乳的影响及其控制措施

分析了乳中体细胞升高的原因，阐述了高体细胞数对乳的组成和乳制品加工的影响，并简要的说明了降低体细胞数的控制措施。     

Differential leukocyte count method for bovine low somatic cell count milk

Whereas many differential leukocyte count methods for high somatic cell count (SCC) milk from mastitic cows are available, only a few have been developed for low SCC milk. We have developed a flow cytometric differential leukocyte count method for low SCC milk. The procedure consists of 1) 1.5 ml of diluted milk sample (30%, vol/vol dilution with PBS), 2) centrifugation, 3) leukocyte labeling with SYTO 13 and 4) flow cytometric analysis. Four major leukocyte populations can be clearly identified in the green fluorescence-side scatter dot plot: lymphocytes and monocytes (LM), polymorphonuclear neutrophils (PMN), mature macrophages (Mphi), and cells with apoptotic features based on chromatin condensation and nuclear fragmentation. The optimal processing temperature was 20 degrees C. Significant differences among samples with similar differential leukocyte counts were found. Storage of milk samples during 2 d at 7 degrees C had no effect on differential leukocyte count. Using the new method, differential leukocyte count was performed in low SCC milk samples from cows in early, mid, and late lactation. In accordance with previous studies, PMN and Mphi percentages were lower and LM percentages were higher in early lactation than in the other stages of lactation. The percentage of cells with apoptotic features was higher in early lactation than in mid and late lactation. In conclusion, a rapid, simple, accurate, and reproducible standard procedure was developed to determine the differential leukocyte count (Mphi, PMN, LM, and cells with apoptotic features) of bovine low SCC milk.     

Predicting somatic cell count standard violations based on herd's bulk tank somatic cell count. Part II: Consistency index

The present study examines the capability of 1,501 herds in the Upper Midwest and the performance of statistical process control charts and indices as a way of monitoring and controlling milk quality on the farm. For 24 mo, daily or every other day bulk tank somatic cell count (SCC) data were collected. Consistency indices for 5 different SCC standards were developed. The indices calculate the maximum variation allowed to meet a desired SCC level at a given mean bulk tank SCC and were used to identify herds not capable of meeting a specific SCC standard. Consistency index method was compared with a test identifying future bulk tank SCC standard violators based on herds’ past violations. The performance of the consistency index test and the past violation method was evaluated by logistic regression. The comparison focused on detection probability and certainty associated with a result. For the 5 SCC levels, detection probability and certainty associated with a result ranged from 51 to 98%. Detection probability of all violators and certainty associated with a negative result was greater for the consistency index across all 5 SCC levels (by 0.7 to 7.4% and 2.1 to 5.1%, respectively). Control charts were plotted and monthly consistency indices calculated for individual farms. Charts in combination with the consistency indices would warn from 66 to 80% of the herds about an upcoming violation within 30 d before it occurred. They offer a proactive approach to maintaining consistently high milk quality. By assessing process capability and distinguishing between significant changes and random variation in bulk tank SCC, tools presented in this article encourage fact-based decisions in dairy farm milk quality management.     

Effect of somatic cell count on Prato cheese composition

The objective of this research was to evaluate the effect of 2 levels of somatic cell counts (SCC) in raw milk on Prato cheese composition, protein and fat recovery, cheese yield, and ripening. A 2 × 6 factorial design with 3 replications was performed in this study: 2 levels of SCC and 6 levels of storage time. Initially, 2 groups of dairy cows were selected to obtain low (<200,000 cells/ mL) and high (>600,000 cells/mL) SCC in milks that were used to manufacture 2 vats of cheese: 1) low SCC and 2) high SCC. Milk, whey, and cheese compositions were evaluated; clotting time was measured; and cheese yield, protein recovery, and fat recovery were calculated. The cheeses were evaluated after 5, 12, 19, 26, 33, and 40 d of ripening according to pH, moisture, pH 4.6 soluble nitrogen, 12% trichloroacetic acid soluble nitrogen as a percentage of total nitrogen, and firmness. High-SCC milk presented significantly higher total protein and nonprotein nitrogen and lower true protein and casein concentrations than did low-SCC milk, indicating an increased whey protein content and a higher level of proteolysis. Although the pH of the milk was not affected by the somatic cell level, the cheese obtained from high-SCC milk presented significantly higher pH values during manufacture and a higher clotting time. No significant differences in cheese yield and protein recovery were observed for these levels of milk somatic cells. The cheese from high-SCC milk was higher in moisture and had a higher level of proteolysis during ripening, which could compromise the typical sensory quality of the product.     

FossMatic5000检测牛乳体细胞

讨论了体细胞数与乳房炎的关系及体细胞数对牛乳成分及产奶量损失的影响。主要介绍了FossoMadc 5000检测方法，该方法方便快捷适用于大型牧场监测牛群健康状况。