• Korean Journal of Veterinary Research
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• v.42 no.2
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• pp.163-170
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• 2002

The relationship between level of milk composition and conditions of ovary and uterus were analyzed in Holstein cows at seven farms participating in a reproductive herd health management program. Milk data were taken from 503 early lactating cows between 30 and 60 days in milk with reproductive examination with ultrasonography from september 1999 to August 2000. Milk fat, protein and solid-not-fat concentration in the herds were $3.70{\pm}1.08%$, $2.97{\pm}0.35$, and $8.41{\pm}0.61%$, respectively. The reproductive disorder relative to normal cows had higher risk in the cows that the level of protein was lower than 2.70%. Also, the higher milk fat than 4.50% were associated with a higher risks in the uterine disease and follicular cysts. Therefore, the cows with the fat to protein ratio of > 1.30 had higher risks for reproductive disorder such as cystic ovarian diseases, inactive ovaries and endometritis. These results indicated that cows diagnosed with reproductive disorder were energy deficient prior to reproductive disorder diagnosis. Consequently, milk fat and protein analyses may be used serve as a monitoring tool for condition of ovary and uterus in early lactating cows

• Journal of Veterinary Clinics
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• v.33 no.2
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• pp.107-112
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• 2016

The objective of this study was to determine potential contributing factors associated with biosecurity level of farrow-to-finish pig farms and to develop a classification tree model to explore how these factors related to each other based on prediction model. To this end, the author analyzed data (n = 193) extracted from a cross-sectional study of 344 farrow-to-finish farms which was conducted between March and September 2014 aimed to explore swine disease status at farm level. Standardized questionnaires with information about basic demographical data and management practices were collected in each farm by on-site visit of trained veterinarians. For the classification of the data sets regarding biosecurity level as a dependent variable and predictor variables, Chi-squared Automatic Interaction Detection (CHAID) algorithm was applied for modeling classification tree. The statistics of misclassification risk was used to evaluate the fitness of the model in terms of prediction results. Categorical multivariate input data (40 variables) was used to construct a classification tree, and the target variable was biosecurity level dichotomized into low versus high. In general, the level of biosecurity was lower in the majority of farms studied, mainly due to the limited implementation of on-farm basic biosecurity measures aimed at controlling the potential introduction and transmission of swine diseases. The CHAID model illustrated the relative importance of significant predictors in explaining the level of biosecurity; maintenance of medical records of treatment and vaccination, use of dedicated clothing to enter the farm, installing fence surrounding the farm perimeter, and periodic monitoring of the herd using written biosecurity plan in place. The misclassification risk estimate of the prediction model was 0.145 with the standard error of 0.025, indicating that 85.5% of the cases could be classified correctly by using the decision rule based on the current tree. Although CHAID approach could provide detailed information and insight about interactions among factors associated with biosecurity level, further evaluation of potential bias intervened in the course of data collection should be included in future studies. In addition, there is still need to validate findings through the external dataset with larger sample size to improve the external validity of the current model.

• Korean Journal of Veterinary Service
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• v.36 no.4
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• pp.243-248
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• 2013

Q fever is a rickettsial infection caused by Coxiella burnetii that is transmitted from animals to humans. Modes of transmission to humans include inhalation, tick bites and ingestion of unpasteurized milk or dairy products. This survey was aimed at monitoring the seroprevalence of C. burnetii in bulk-tank milk (BTM) in Gyeongbuk province. In addition, the seroprevalence of C. burnetii was investigated at the herd level of dairy cattle in eastern Gyeongbuk province in which many dairy cattle are reared. Among 324 BTM samples collected from 20 country areas, 175 (54%) BTM samples from 15 (75%) country areas were positive for C. burnetii by ELISA. By regions, the seroprevalence of BTM samples in eastern, central, western and northern areas of Gyeongbuk province were 62.7%, 48.4%, 45.1%, and 41.4%, respectively. When analyzed in the dairy cattle reared in the eastern area where high seroprevalence occurred in BTM samples, 119 (24.2%) out of 492 dairy cattle were positive for C. burnetii. Seroprevalence of C. burnetii in dairy cattle was increased with daily milk yield of farm (P<0.05) and age (P<0.001). Since seroprevalence of C. burnetii is relatively high in both BTM samples and dairy cattle reared in Gyeongbuk province, further studies on the high risk farms and herds are needed to evaluate infection status and appropriate control programs in this region.

• Journal of Life Science
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• v.19 no.8
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• pp.1170-1176
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• 2009

Porcine reproductive and respiratory syndrome virus (PRRSV) has plagued pig populations worldwide causing severe economical impacts. In order to establish effective strategies for prevention of PRRS, infection patterns on the herd level are primarily evaluated. In the present study, therefore, serological and virological analyses were conducted in 20 pig farms suffering from PRRS. Seroprevalence levels in each farm were grouped into 3 patterns: SN (Stable sow groups/Not infected piglet groups, SI (Stable sow groups and Infected piglet groups), and UI (Unstable sow groups and Infected piglet groups). The rates of each serological pattern were 15% (n=3), 10% (n=2), and 75% (n=15), respectively. In addition, the pattern analysis was extended to virological monitoring on the same farms that further included suckling pig groups. As a result, the infection pattern was classified into 4 categories: SNI (Stable sow groups/Not infected suckler groups/Infected piglet groups), SII (Stable sow groups/Infected suckler groups/Infected piglet groups), UNI (Unstable sow groups/Not infected suckler groups/Infected piglet groups), and UII (Unstable sow groups/Infected suckler groups/Infected piglet groups). The rates of each viroprevalence were estimated at 50% (n=10), 30% (n=6), 10% (n=2), and 10% (n=2), respectively. PRRSV viroprevalence results of suckling pig groups revealed that 8 farms were considered virus positive. In 2 farms among these farms, PRRSV appeared to be transmitted vertically to suckling piglets from their sows. In contrast, piglet-to-piglet horizontal transmission of PRRSV seemed to occur in sucking herds of the remaining farms. Thus, this virological analysis on suckling piglets will provide useful information to understand PRRSV transmission routes during the suckling period and to improve a PRRS control programs. Our seroprevalence and viroprevalence data found that infection patterns between sow and piglet groups are not always coincident in the same farm. Remarkably, 15 farms belonging to the UI seroprevalence pattern showed four distinct viroprevalence patterns (SNI; 7, SII; 4, UNI; 2 and UII; 2). Among these farms, 11 farms with unstable seroprevalence sow groups were further identified as the stable viroprevalence pattern. These results indicated that despite the absence of typical seroconversion, PRRSV infection was detected in several farms, implying the limitation of serological analysis. Taken together, our data strongly suggests that both seroprevalence and viroprevalence should be determined in parallel so that a PRRS control strategies can be efficiently developed on a farm level.