• Korean Journal of Soil Science and Fertilizer
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• v.32 no.4
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• pp.398-404
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• 1999

Factor analysis technique was employed to screen the principal indicators influencing soil and water qualities in the intensively cultivated areas of the Han River Basin. Soil chemical parameters were analyzed for the soil samples collected at intensive farming area in Pyungchang-Gun, and water quality monitoring data were obtained from the agricultural small catchments of Han River Basin during 1996 and 1997. Among the $11{\times}11$ cross correlation matrix, 29 correlations were significant out of 55 soil quality indicator pairs. The overall Kaiser's measure of sampling adequacy(KMS) value was acceptable with 0.60. Most indicators except iron were acceptable. Among soil indicators, the first factors showing high factor loadings were pH, Ca and Mg. The factor loading was the highest for Ca. The second factor could be characterized as phosphate and micronutrient. The third factor was organic matter and EC, and the fourth factor was potassium and Fe. Out of 190 water quality indicators, 86 correlations were significant. Overall KMS value was 0.74, but the KMS values for pH, TSS, Cd, Cu and Fe were lower than 50. The first factor of EC accounts 27.1 percents of the total variance, and showed high factor loadings with Na, Ca, $SO_4$, Mg, K, Cl, $NO_3$, and T-N. The second factor showed high loadings with Zn, Fe, Mn and Cd. The third to seventh factors could be characterized as $PO_4$, TSS, inorganic nitrogen, pH and T-P, and Cu factors, respectively. The factor score for EC was the highest in Kuri, followed by Chunchon, Dunnae and Daegwanryng. The factor score for heavy metals were the highest in the Daegwanryng. The results demonstrated that the factor analysis could be useful to select the most principal factor influencing soil and water qualities in the agricultural watershed.

• Journal of Korea Port Economic Association
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• v.37 no.4
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• pp.161-173
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• 2021

The shipping industry plummeted and was depressed due to the global economic crisis caused by the bankruptcy of Lehman Brothers in the US in 2008. In 2020, the shipping market also suffered from a collapse in the unstable global economic situation due to the COVID-19 pandemic, but unexpectedly, it changed to an upward trend from the end of 2020, and in 2021, it exceeded the market of the boom period of 2008. According to the Clarksons report published in May 2021, the decrease in cargo volume due to the COVID-19 pandemic in 2020 has returned to the pre-corona level by the end of 2020, and the tramper bulk carrier capacity of 103~104% of the Panamax has been in the ports due to congestion. Earnings across the bulker segments have risen to ten-year highs in recent months. In this study, as factors affecting BDI, the capacity and congestion ratio of Cape and Panamax ships on the supply side, iron ore and coal seaborne tonnge on the demand side and Granger causality test, IRF(Impulse Response Function) and FEVD(Forecast Error Variance Decomposition) were performed using VAR model to analyze the impact on BDI by congestion caused by strengthen quarantine at the port due to the COVID-19 pandemic and the loading and discharging operation delay due to the infection of the stevedore, etc and to predict the shipping market after the pandemic. As a result of the Granger causality test of variables and BDI using time series data from January 2016 to July 2021, causality was found in the Fleet and Congestion variables, and as a result of the Impulse Response Function, Congestion variable was found to have significant at both upper and lower limit of the confidence interval. As a result of the Forecast Error Variance Decomposition, Congestion variable showed an explanatory power upto 25% for the change in BDI. If the congestion in ports decreases after With Corona, it is expected that there is down-risk in the shipping market. The COVID-19 pandemic occurred not from economic factors but from an ecological factor by the pandemic is different from the past economic crisis. It is necessary to analyze from a different point of view than the past economic crisis. This study has meaningful to analyze the causality and explanatory power of Congestion factor by pandemic.