• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.858-863
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• 2010

The blueberry farming requires the soil condition of well-drainage, pH of 4.5 to 5.2, and high in organic matters for stable growth and development. Most of soil type of cultivated land in Korea, however, belongs to alkaline soils with low organic matter content and poor drainage. Therefore, the blueberry farmers use peat moss heavily to improve the soil condition, but the guideline on the effective and economic ratio of peat moss is not established yet. This study was performed to determine the cost effective peat moss ratio for amending soils, and to investigate the feasibility of using sawdust and coco peat as soil amendments. Peat moss, coco peat and sawdust are mixed with soil at the ratio of 0, 12.5, 50 and 100% (v/v). Among 3 organic materials with various mixture ratios, the pH of soil was the lowest in 100% peat moss and sawdust mixtures (pH 3.67 and pH 3.73, respectively), followed by pH 5.30 at 50% peat moss. The soil organic matter content are directly proportional to the mixture ratios in all three organic materials and the same trend was observed in the variation of content of exchangeable potassium in the coco peat treatments. On the contrary, the content of available phosphate, exchangeable calcium and magnesium decreased with increasing the ratio of organic materials. The nitrogen content in the leaves decreased as increasing the ratio of peat moss and coco peat in soil, but not of sawdust. The content of phosphate decreased but potassium increased as the ratio of sawdust and coco peat increased. There was no clear difference in the contents of magnesium and calcium among 3 organic materials. The plant height, stem diameter and dry weight of blueberry plants were the highest in 50 % peat moss, followed by 12.5% peat moss and 12.5% coco peat. The plants in 100% peat moss showed very poor growth. It can be concluded that peatmoss, when applied and managed appropriately, will be a good material for improving soil condition as well as securing desirable growth for blueberry. Upon coupling economic aspect, the optimum mixing ratio of peatmoss for blueberry farming is approximately 25-50%.

• Korean Journal of Environment and Ecology
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• v.36 no.4
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• pp.433-441
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• 2022

Light pollution is one of the factors that disturb coastal and island ecosystems. This study examined the factors causing light pollution in the islands in Daedohaehaesang National Park using nighttime satellite images. This study selected 101 islands with an area of 100,000 m² or more in Daedohaehaesang National Park, and measured the levels of light pollution of the selected islands by calculating mean nighttime radiance recorded in VIIRS DNB monthly images for January, April, August, and October 2019. Of seven districts of the park, The highest mean nighttime radiance was recorded in Geumodo district (17,666nW/m²/sr), followed by Geonumdo·Baekdo, Narodo, Soando·Cheongsando districts. By season, mean nighttime radiance in October was the highest at 9,509nW/m²/sr, followed by August, January, and April. Regression analyses show that the total floor area and the number of lighthouses in a 5 km buffer area had a statistically significant effect on mean nighttime radiance at all times, but those within the island did not, indicating that light pollution in islands in a national park where land development is strictly restricted is influenced by artificial lights in nearby areas. However, the total floor area of an island significantly affected mean nighttime radiance only in August, which appears to be attributed to the impact of intensive use of artificial light by visitors during summer vacation. The size of an island had a negative (-) effect on nighttime radiance. This negative effect suggests that light pollution is a type of ecological edge effect, i.e., the smaller island is more likely to have a relatively larger proportion of edge area that is affected by light emitted from the neighboring areas. The results of this study indicate that managing artificial lights in nearby areas is necessary to mitigate light pollution in islands in marine and coastal national parks.

• Journal of Environmental Impact Assessment
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• v.32 no.1
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• pp.15-28
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• 2023

In the Republic of Korea, the target project scope of the small-scale EIA is stipulated as the plan area above around 5,000~60,000m² depending on a type of project and classification of land use. Whereas, the lower limit of the corresponding local government EIA project is generally located above the small-scale EIA's limits, and overlapping ranges exist. This situation has been enlarged since road construction and district unit planning were included as the target projects for small-scale EIA in the "Enforcement Decree of the Environmental Impact Assessment Act", which was partially revised in November 2016, and the current consultation system needed discussion in that small-scale EIA is allowed to be done without gathering review opinions at the local level. In fact, projects subjected to local government EIA but consulted as small-scale EIAs may seem insignificant because of a small number of total cases; however, it is worth paying attention to the fact that a local government may not add a target project due to the small-scale EIA. This study suggested the three policy measures for improving small-scale EIA to enhance the effectiveness of local government EIA: supplementing the institutional arrangements to incorporate the review opinion from the local region in small-scale EIA, giving priority to local EIA for conducing the projects in overlapping ranges with partial amendments on EIA law regarding exceptions to local government EIA, including small target projects (not to be small-scale EIA targets) to the ordinance that are deemed necessary to be conducted as local government EIA. Even though a positive function of small-scale EIA has been confirmed, efforts should be made to improve the situation in which many projects within local governments are consulted without review from the region.

• Asia pacific journal of information systems
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• v.23 no.3
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• pp.25-53
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• 2013

This study analyzes the effect of Computer Aided Innovation (CAI) to improve R&D Capabilities empirically. Survey was distributed by e-mail and Google Docs, targeting CTO of 235 SMEs. 142 surveys were returned back (rate of return 60.4%) from companies. Survey results from 119 companies (83.8%) which are effective samples except no-response, insincere response, estimated value, etc. were used for statistics analysis. Companies with less than 50billion KRW sales of entire researched companies occupy 76.5% in terms of sample traits. Companies with less than 300 employees occupy 83.2%. In terms of the type of company business Partners (called 'partners with big companies' hereunder) who work with big companies for business occupy 68.1%. SMEs based on their own business (called 'independent small companies') appear to occupy 31.9%. The present status of holding IT system according to traits of company business was classified into partners with big companies versus independent SMEs. The present status of ERP is 18.5% to 34.5%. QMS is 11.8% to 9.2%. And PLM (Product Life-cycle Management) is 6.7% to 2.5%. The holding of 3D CAD is 47.1% to 21%. IT system-holding and its application of independent SMEs seemed very vulnerable, compared with partner companies of big companies. This study is comprised of IT infra and IT Utilization as CAI capacity factors which are independent variables. factors of R&D capabilities which are independent variables are organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability. The highest average value of variables was 4.24 in organization capability 2. The lowest average value was 3.01 in IT infra which makes users access to data and information in other areas and use them with ease when required during new product development. It seems that the inferior environment of IT infra of general SMEs is reflected in CAI itself. In order to review the validity used to measure variables, Factors have been analyzed. 7 factors which have over 1.0 pure value of their dependent and independent variables were extracted. These factors appear to explain 71.167% in total of total variances. From the result of factor analysis about measurable variables in this study, reliability of each item was checked by Cronbach's Alpha coefficient. All measurable factors at least over 0.611 seemed to acquire reliability. Next, correlation has been done to explain certain phenomenon by correlation analysis between variables. As R&D capabilities factors which are arranged as dependent variables, organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability turned out that they acquire significant correlation at 99% reliability level in all variables of IT infra and IT Utilization which are independent variables. In addition, correlation coefficient between each factor is less than 0.8, which proves that the validity of this study judgement has been acquired. The pair with the highest coefficient had 0.628 for IT utilization and technology-accumulating capability. Regression model which can estimate independent variables was used in this study under the hypothesis that there is linear relation between independent variables and dependent variables so as to identify CAI capability's impact factors on R&D. The total explanations of IT infra among CAI capability for independent variables such as organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability are 10.3%, 7%, 11.9%, 30.9%, and 10.5% respectively. IT Utilization exposes comprehensively low explanatory capability with 12.4%, 5.9%, 11.1%, 38.9%, and 13.4% for organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability respectively. However, both factors of independent variables expose very high explanatory capability relatively for technology-accumulating capability among independent variable. Regression formula which is comprised of independent variables and dependent variables are all significant (P<0.005). The suitability of regression model seems high. When the results of test for dependent variables and independent variables are estimated, the hypothesis of 10 different factors appeared all significant in regression analysis model coefficient (P<0.01) which is estimated to affect in the hypothesis. As a result of liner regression analysis between two independent variables drawn by influence factor analysis for R&D capability and R&D capability. IT infra and IT Utilization which are CAI capability factors has positive correlation to organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability with inside and outside which are dependent variables, R&D capability factors. It was identified as a significant factor which affects R&D capability. However, considering adjustable variables, a big gap is found, compared to entire company. First of all, in case of partner companies with big companies, in IT infra as CAI capability, organization capability, process capability, human resources capability, and technology capability out of R&D capacities seems to have positive correlation. However, collaboration capability appeared insignificance. IT utilization which is a CAI capability factor seemed to have positive relation to organization capability, process capability, human resources capability, and internal/external collaboration capability just as those of entire companies. Next, by analyzing independent types of SMEs as an adjustable variable, very different results were found from those of entire companies or partner companies with big companies. First of all, all factors in IT infra except technology-accumulating capability were rejected. IT utilization was rejected except technology-accumulating capability and collaboration capability. Comprehending the above adjustable variables, the following results were drawn in this study. First, in case of big companies or partner companies with big companies, IT infra and IT utilization affect improving R&D Capabilities positively. It was because most of big companies encourage innovation by using IT utilization and IT infra building over certain level to their partner companies. Second, in all companies, IT infra and IT utilization as CAI capability affect improving technology-accumulating capability positively at least as R&D capability factor. The most of factor explanation is low at around 10%. However, technology-accumulating capability is rather high around 25.6% to 38.4%. It was found that CAI capability contributes to technology-accumulating capability highly. Companies shouldn't consider IT infra and IT utilization as a simple product developing tool in R&D section. However, they have to consider to use them as a management innovating strategy tool which proceeds entire-company management innovation centered in new product development. Not only the improvement of technology-accumulating capability in department of R&D. Centered in new product development, it has to be used as original management innovative strategy which proceeds entire company management innovation. It suggests that it can be a method to improve technology-accumulating capability in R&D section and Dynamic capability to acquire sustainable competitive advantage.