REBHUNS DISEASES OF DAIRY CATTLE PDF
REBHUN'S DISEASES OF DAIRY CATTLE, 2nd Edition is your all-in-one guide to bovine disease management. With thorough, up-to-date coverage of. Diagnose and treat bovine diseases in cattle with Rebhun's Diseases of Dairy Cattle, 3rd Edition — your all-in-one guide to bovine disease management. Rebhun's Diseases of Dairy Cattle, 3rd Edition By Simon F. Peek Thomas J. Divers February ppti.info
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Rebhun's Diseases of Dairy Cattle 3rd Edition PDF Free Download. Rebhun's Diseases of Dairy Cattle By Thomas J. Divers, Simon F. Peek Dairy Farm Business Management Pdf Book Free Download Farm Business, Pdf Book . Rebhun's Diseases of Dairy Cattle: Medicine & Health Science Books Review. "every production animal practitioner should have this".
Fecal samples from suspected clinical cases were collected by veterinarians via rectal retrieval, with a new glove being used to collect each sample. Fecal samples obtained by project personnel during the three monthly visits were collected via rectal retrieval, again with a new glove being used to collect each sample. Approximately 10 g of fecal matter was placed into a Para-Pak bottle and sealed. All of these samples were transported to the research laboratory for bacteriologic culture.
Standard culture methods were used to isolate Salmonella from feces. Individual fecal swabs from sample bottles were enriched in 10 mL of Tetrathionate broth Difco, Detroit, MI containing 0.
Colonies on Kligler Iron Agar slants that exhibited the biochemical properties of Salmonella were then serogrouped by slide agglutination using standard protocols. Department of Agriculture in Ames, Iowa, for serotyping using standard protocols. Antimicrobial susceptibility testing Antimicrobial susceptibility of Salmonella isolates was determined by use of the broth dilution method.
Isolates were classified as being resistant or susceptible to each agent; those isolates with intermediate susceptibility were categorized as being susceptible. Quality control was performed weekly using four strains of bacteria: Escherichia coli ATCC , Staphylococcus aureus , Enterococcus faecalis , and Pseudomonas aeruginosa Data analysis Study herds were considered Salmonella-positive either if Salmonella was isolated from one or more environmental samples or if there was at least one laboratory-confirmed clinical case.
The median within-herd prevalence of Salmonella shedding was estimated for herds that only yielded Salmonella-positive environmental samples and for herds that had one or more clinical cases confirmed by bacteriologic culture.
Descriptive analysis of serovar data and level of antimicrobial resistance was performed, stratified by positive herd type. The proportion of MDR isolates by serovar was also determined. In this study, multidrug resistance was defined as having in vitro resistance to five or more antimicrobial agents. Repeated measures Poisson regression analysis was performed to study the relationship between the within-herd prevalence of Salmonella shedding and the dichotomous predictor variable for positive herd status positive environmental samples only vs.
The response variable was the number of cattle positive for Salmonella upon follow-up sampling, and the offset variable was the logarithm of the number of animals tested via this method.
A random effects regression model was used to account for the clustering of the three sequential prevalence estimates per herd. Time of sampling for the three within-herd prevalence measurements was added to the model as a fixed effect, thus allowing a comparison of the change in prevalence over time for the two positive herd types. Herd size was forced into the final model because it was considered an important potential confounding variable.
The generalized estimating equations method was used for this regression model. Separate logistic regression models were utilized to determine whether herds with confirmed clinical cases were more likely to yield either serovars that are important human pathogens Newport and Typhimurium or MDR isolates, as compared to herds with positive environmental samples only.
Logistic regression analysis was also used to investigate any associations between herd size and Salmonella status. All data analysis was performed in SAS version 9. Results Thirty-four veterinarians representing 11 veterinary practices participated in this study. A total of 62 dairy farms were enrolled, but 5 farms withdrew their involvement.
Among the remaining 57 study herds, the median herd size was female dairy cattle range: — Although current dairy health data recording is of variable and generally poor quality, new disease episodes, repeat episodes, and death and culling provide objective measures of health management that should be routinely monitored [ 27 ]. This project built upon previous efforts to enhance the documentation of dairy cow death [ 28 ], and to better understand the impact of disease on productive life and well-being [ 29 ].
It was founded on the presumption that dairies would benefit from a time-based accounting of the burden of disease inclusive of morbidity, culling, and mortality. Such a consistent and comparative descriptor would provide a platform for ongoing assessments of the impacts of disease, an incentive for improving disease data recording, and ultimately enhance health decision-making and planning processes.
The dairy industry is well versed in the effects of ill-health on milk production [ 30 , 31 ]. Expanding the discussion to view profits and losses in light of the quality and length of productive life would provide dairy health managers alternative insight and perspective. Management included 3 times per day milking and total mixed rations. Cows that freshened twice during the study were enrolled a second time.
Dairy cow disease, culling, and mortality data were acquired for each enrolled cow from herd data backups using the EVENTS function in the on-farm dairy management i.
These data were downloaded on a monthly basis into an Excel spreadsheet Microsoft, designed to organize pertinent information for the project. Information that was recorded for injured, diseased, culled, and dead cows included identification numbers, freshening date, days in milk, lactation number, reason for removal or death given by farm, date of removal or death, and disease events with the date at which events occurred.
These events represented disease states commonly recorded industry-wide and on the participating dairies. Events of interest included 9 standalone categories: diarrhea, ketosis, lameness hoof only , left displaced abomasum LDA , mastitis, metritis, milk fever, pneumonia, and retained placenta.
Two other events of interest, calving trauma and musculoskeletal injury leg, hip, back , only were found as codes within culling or death SOLD or DIED events. Day-to-day oversight of health management including clinical diagnoses and on-farm computer database entries of disease, culling, and death were carried out by farm personnel.
Although case definitions and diagnoses undoubtedly varied to some degree between farms and personnel, this study was developed on the premise of using and comparing available disease data as-is.
A death certificate was created and a mortality code assigned to the DIED event for cows that died during the study [ 28 ]. Necropsies were not performed and underlying causes of death were based on farm employee or veterinarian assessments of health problems and previously recorded disease or injury events.
Causes of death were ultimately attributed to one of the 11 disease states of interest where possible, or were classified as miscellaneous or unknown.
Culling of cows in this study was recorded as a SOLD event and attributed to either economic or biological reasons [ 4 ]. Cows classified as economic culls were removed based on an appraisal of profit versus loss due to milk production. Their removal from the herd was decided at the time by choice rather than dictated by the force of disease or injury.
Biological culls were attributed to disease processes.
The decision to remove the cow was considered mandatory to avoid ongoing welfare implications or death. Although most cows are culled from a herd due to a combination of underlying issues [ 4 , 32 ], this study focused on the best available reason for removing the cow in light of the severity and urgency of ill health.
Similar to causes of death, biological reasons for culling were ultimately attributed to one of the 11 disease states of interest where possible, or were listed as other miscellaneous disease processes. There's a problem loading this menu right now.
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For the most part diseases are recorded based on treatments, and analyzed through frequency measures independent of their outcomes. This article has been cited by other articles in PMC.
Although there has been extensive debate regarding the definition and measurement of disability weights [ 20 ], the latest human disability weights capture the most salient differences in clinical symptoms and functionality [ 23 ].