• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.1
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• pp.35-44
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• 2015

Forests contain a huge amount of carbon (C) and climate change could affect forest C dynamics. This study was conducted to predict the C dynamics of Pinus densiflora and Quercus variabilis forests, which are the most dominant needleleaf and broadleaf forests in Korea, using the Korean Forest Soil Carbon (KFSC) model under the two climate change scenarios (2012-2100; Constant Temperature (CT) scenario and Representative Concentration Pathway (RCP) 8.5 scenario). To construct simulation unit, the forest land areas for those two species in the 5th National Forest Inventory (NFI) data were sorted by administrative district and stand age class. The C pools were initialized at 2012, and any disturbance was not considered during the simulation period. Although the forest C stocks of two species generally increased over time, the forest C stocks under the RCP 8.5 scenario were less than those stocks under the CT scenario. The C stocks of P. densiflora forests increased from 260.4 Tg C in 2012 to 395.3 (CT scenario) or 384.1 Tg C (RCP 8.5 scenario) in 2100. For Q. variabilis forests, the C stocks increased from 124.4 Tg C in 2012 to 219.5 (CT scenario) or 204.7 (RCP 8.5 scenario) Tg C in 2100. Compared to 5th NFI data, the initial value of C stocks in dead organic matter C pools seemed valid. Accordingly, the annual C sequestration rates of the two species over the simulation period under the RCP 8.5 scenario (65.8 and $164.2g\;C\;m^{-2}\;yr^{-1}$ for P. densiflora and Q. variabilis) were lower than those values under the CT scenario (71.1 and $193.5g\;C\;m^{-2}\;yr^{-1}$ for P. densiflora and Q. variabilis). We concluded that the C sequestration potential of P. densiflora and Q. variabilis forests could be decreased by climate change. Although there were uncertainties from parameters and model structure, this study could contribute to elucidating the C dynamics of South Korean forests in future.

• Korean Journal of Ecology and Environment
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• v.44 no.2
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• pp.203-213
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• 2011

Agriculture modern environments can vary due to factors such as land consolidation and ditch enhancement projects. But, these improvements projects can include cover-ing-up of irrigation ditches with concrete, increasing the drop between paddy field and drainage ditches, which might decrease the abundance and diversity of fish fauna around paddy fields. In this study, for the management of agrobiodiversity on fish in paddy fields, we installed a small-scale fishways between paddy fields and drainage ditches, and evaluated the effects on the eco-connection of the paddy fields, ditches and stream. Five fish species were recovered at the drainage ditches. The species exhibited characteristics spawning and growth based on the paddy field. The results indicate that the five fish species could ascend the paddy fields through the small-scale fishways. There are no difference of species numbers at ditches of environment-friendly agriculture paddy fields (A) and good agricultural practices (B) region, but individual numbers were higher at the B region. This result could be interpreted as indicating that ditch diversity was affected by positional properties rather than farming practices because the water flow and connection to adjacent stream of B region were better than A region. After ascertaining the fish species capable of ascending in a pre-survey of the drainage ditches, we set up small-scale fishways at the drop between paddy fields and drainage ditches. Three species of fish (Aphyocypris chinensis, Misgurnus anguillicaudatus, M. mizolepis) arrived at the paddy fields via small-scale fishways. The main movement time was from 18:00~24:00 and 00:00~06:00, indicating a preference for the night time period rather than the day period for migration. Concentrating the operation time from night to dawn seems prudent for effective management of small-scale fishways.

• Korean Journal of Soil Science and Fertilizer
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• v.33 no.1
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• pp.24-31
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• 2000

This study was conducted to determine the effect of soil physical characteristics on rhizome rot incidence of platycodon. Sampling sites were Keochang 4, Kimhae 7, Haman 6, Chinju 6 and Koseong 3 fields in Kyongnam province and Hongcheon 6 fields in Kangwon province. The root disease incidence rate was correlated with soil depth Y=-0.747X+88.19($R^2=0.394^{***}$), soil hardness Y=4.36X+8.93($R^2=0.201^*$), bulk density Y=104.7X-80.99($R^2=0.295^{**}$), clay content Y=1.24X+14.14($R^2=0.196^*$), porosity Y=-3.11X+215.9($R^2=0.220^*$) and silt content Y=-0.75X+67.85($R^2=0.178^*$). The yield was correlated with soil depth Y=0.263X+0.971($R^2=0.105^*$), clay content Y=-0.688X+32.74($R^2=0.158^*$), porosity Y=1.974X-93.19($R^2=0.231^{**}$) and silt content Y=53.05X-108.65($R^2=0.232^*$), The optimum cultivated land of perennial platycodon was soil depth over 1m, soil hardness under $5kg\;cm^{-2}$, bulk density $1.0Mg\;m^{-3}$, moisture content 13~17%. clay content 5~10%, porosity 58~63%, silt content 38~64% and soil texture of silt loam.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.