• Journal of Animal Science and Technology
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• v.54 no.2
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• pp.111-124
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• 2012

Feeding trial was conducted with 80 Hanwoo steers (7.5 months of age, 204.4 kg body weight) for 680 days from growing period to late fattening period to examine the feeding value of whole crop barley silage TMR (BS-TMR) and whole crop rye silage TMR (RS-TMR) on body gain, feed cost, slaughter characteristics and quality characteristics of $longissimus$ $dorsi$ muscle. Dietary treatments were conventional separate feeding of concentrate and rice straw (control), feeding BS TMR up to middle fattening period and same diet as for control during late fattening period (BS-TMR I), feeding BS-TMR for whole experimental period (BS-TMR II), feeding RS TMR up to middle fattening period and same diet as for control during late fattening period (RS-TMR I) and RS TMR for whole experimental period (RS-TMR II). Sixteen castrated calves were assigned to each treatment (4 pens, 4 heads per pen). Pens in each treatment were randomly distributed. Feeding both BS silage TMR and RS silage TMR slightly increased body gain of Hanwoo steers at the stages of growing and early fattening, and increased (P<0.0001) at middle fattening compared to feeding control diet while control diet tended to increase body gain at late fattening stage compared to feeding BS-TMR I, BS-TMR II and RS-TMR I diets. Total body gain was slightly increased in Hanwoo steers fed both I and II for BS and RS TMR compared to that in control diet. Feed cost per kg gain per head was relatively low in the Hanwoo steers fed silage TMRs to that fed control diet. Carcass weight, back fat thickness and $longissimus$ $dorsi$ area of Hanwoo steers tended to increase but lowered (P<0.047) yield index by feeding silage TMRs. Feeding BS TMR slightly decreased marbling score but no difference was found in the number of head over grade 1 between diets. Control diet tended to improve yield grade compared to silage TMRs. Chemical composition, water holding capacity, drip loss, cooking loss and pH, color and fatty acid composition of $longissimus$ $dorsi$ were not affected by experimental diets and feeding duration of silage TMRs. Shear force, however, was increased (P<0.046) by silage TMRs without difference between them compared to control diet. Based on the results of the current study, BS TMR and RS TMR could improve body gain and reduce feed cost without deteriorating meat quality compared to separate feeding of concentrate and rice straw. Overall feeding value was similar between BS TMR and RS TMR.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.