KMID : 1024520140230111827
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Journal of the Environmental Sciences 2014 Volume.23 No. 11 p.1827 ~ p.1833
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Estimation in a Model for Determining the Amount of Carbon in Soil and Measurement of the Influences of the Specific Factors
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Suh Jeong-Min
Cho Jae-Hwan Son Beung-Gu Kang Jum-Soon Hong Chang-Oh Kim Woon-Won Park Jeong-Ho Lim Woo-Taik Jin Kyung-Ho
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Abstract
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This study has been carried out to present the valuation system of soil carbon sequestration potentials of soil in accordance with the new climate change scenarios(RCP). For that, by analyzing variation of soil carbon of the each type of agricultural land use, it aims to develop technology to increase the amount of carbon emissions and sequestration. Among the factors which affects the estimation of determining the soil carbon model and influence power after the measurement on soil organic carbon, under the center of a causal relationship between the explanatory variables this study were investigated. Chemical fertilizers (NPK) decreased with increasing the amount of soil organic carbon and as with the first experimental results, when cultivating rice than pepper, the fact that soil organic carbon content increased has been found out. The higher the carbon dioxide concentration, the higher the amount of organic carbon in the soil and this result is reliable under a 10% significance level. On the other hand, soil organic carbon, humus carbon and hot water extractable carbon has been found out that was not affected the soils depth, sames as the result of the first year. The higher concentration of carbon dioxide, the higher carbon content of humus and hot water extractable carbon content. According to IPCC 2006 Guidelines and the new climate change scenario RCP 4.5 and the measurement results of the total amount of soil organic carbon to the crops due to abnormal climate weather, 1% increase in atmospheric carbon dioxide concentration was found to be small when compared to the growing rate of increasing 0.01058% of organic carbon in the soil.
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KEYWORD
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Soil organic carbon, Climate change, RCP, Ordinary least square Method, Multiple regression model
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