• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1097-1107
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• 2020

Assessment of soil carbon stock is essential for climate change mitigation and soil fertility. The digital soil mapping (DSM) is well known as a general technique to estimate the soil carbon stocks and upgrade previous soil maps. The aim of this study is to calculate the soil carbon stock in the top soil layer (0 to 30 cm) in Jeolla Province of South Korea using the DSM technique. To predict spatial carbon stock, we used Cubist, which a data-mining algorithm model base on tree regression. Soil samples (130 in total) were collected from three depths (0 to 10 cm, 10 to 20 cm, 20 to 30 cm) considering spatial distribution in Jeolla Province. These data were randomly divided into two sets for model calibration (70%) and validation (30%). The results showed that clay content, topographic wetness index (TWI), and digital elevation model (DEM) were the most important environmental covariate predictors of soil carbon stock. The predicted average soil carbon density was 3.88 kg·m-2. The R2 value representing the model's performance was 0.6, which was relatively high compared to a previous study. The total soil carbon stocks at a depth of 0 to 30 cm in Jeolla Province were estimated to be about 81 megatons.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.1-8
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• 2019

Carbon dioxide is one of the major driving forces causing climate changes, and many countries have been trying to reduce carbon dioxide emissions from various sources. Soil stores more carbon dioxide(two to three times) amounts than atmosphere indicating that soil organic carbon emission management are a pivotal issue. In this study, we developed a Soil Organic Carbon(SOC) storage estimation model to predict SOC storage amounts in soils. Also, SOC storage values were assessed based on the carbon emission price provided from Republic Of Korea(ROK). Here, the SOC model calculated the soil hydraulic properties based on the soil physical and chemical information. Base on the calculated the soil hydraulic properties and the soil physical chemical information, SOC storage amounts were estimated. In validation, the estimated SOC storage amounts were 486,696 tons($3.526kg/m^2$) in Jindo-gun and shown similarly compared to the previous literature review. These results supported the robustness of our SOC model in estimating SOC storage amounts. The total SOC storage amount in ROK was 305 Mt, and the SOC amount at Gyeongsangbuk-do were relatively higher than other regions. But the SOC storage amount(per unit) was highest in Jeju island indicating that volcanic ashes might influence on the relatively higher SOC amount. Based on these results, the SOC storage value was shown as 8.4 trillion won in ROK. Even though our SOC model was not fully validated due to lacks of measured SOC data, our approach can be useful for policy-makers in reducing soil organic carbon emission from soils against climate changes.

• Journal of Ecology and Environment
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• v.32 no.1
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• pp.47-53
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• 2009

The soil carbon stock for a Pinus densiflora forest at Gwangneung, central Korea was estimated using the soil carbon model, Yasso. The soil carbon stock measured in the forest was 43.73 t C $ha^{-1}$, and the simulated initial (steady state) soil carbon stock and the simulated current soil carbon stock in 2007 were 39.19 t C $ha^{-1}$ and 38.90 t C $ha^{-1}$, respectively. Under the assumption of a $0.1^{\circ}C$ increase in mean annual temperature per year, the decomposition and litter fractionation rates increased from 0.28 to 0.56 % $year^{-1}$ and the soil carbon stock decreased from 0.03 to 0.12 % $year^{-1}$. Yasso is a simple and general model that can be applied in cases where there is insufficient input information. However, in order to obtain more accurate estimates in Korea, parameters need to be recalibrated under Korean climatic and vegetation conditions. In addition, the Yasso model needs to be linked to other models to generate better litter input data.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.447-447
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• 2023

Soil organic carbon (SOC) is a critical indicator of soil fertility. Its importance in maintaining ecological balance has received widespread attention. However, global temperatures have risen by 0.8℃ since the late 1800s due to human-induced greenhouse gas emissions, resulting in severe disruptions in SOC dynamics. To study the impacts of temperature variations on SOC and soil respiration, we used the Soil Carbon and Landscape co-Evolution (SCALE) model, which was capable of estimating the spatial distribution of soil carbon dynamics. The study site was located at Heshan Farm (125°20'10.5"E, 49°00'23.1"N), Nenjiang County in Heilongjiang Province, Northeast China. We validated the model using observed soil organic carbon and soil respiration in 2015 and achieved excellent agreement between observed and modeled variables. Our results showed considerable influences of temperature increases on SOC and soil respiration rates at both erosion and deposition areas. In particular, changes in SOC and soil respiration at the deposition area were greater than at the erosion area. Our study highlights that the impacts of temperature elevations are considerably dependent on soil erosion and deposition processes. Thus, it is important to implement effective soil conservation strategies to maintain soil fertility under global warming.

• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.891-897
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• 2021

There is a need for a technology that can quickly and accurately analyze soil carbon contents. Existing soil carbon analysis methods are cumbersome in terms of professional manpower requirements, time, and cost. It is against this background that the present study leverages the soil physical properties of color and water content levels to develop a model capable of predicting the carbon content of soil sample. To predict the total carbon content of soil, the RGB values, water content of the soil, and lux levels were analyzed and used as statistical data. However, when R, G, and B with high correlations were all included in a multiple regression analysis as independent variables, a high level of multicollinearity was noted and G was thus excluded from the model. The estimates showed that the estimation coefficients for all independent variables were statistically significant at a significance level of 1%. The elastic values of R and B for the soil carbon content, which are of major interest in this study, were -2.90 and 1.47, respectively, showing that a 1% increase in the R value was correlated with a 2.90% decrease in the carbon content, whereas a 1% increase in the B value tallied with a 1.47% increase in the carbon content. Coefficient of determination (R²), root mean square error (RMSE), and mean absolute percentage error (MAPE) methods were used for regression verification, and calibration samples showed higher accuracy than the validation samples in terms of R² and MAPE.

• Journal of Environmental Science International
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• v.23 no.11
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• pp.1827-1833
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• 2014

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.

• Journal of the Korean GEO-environmental Society
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• v.20 no.12
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• pp.33-39
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• 2019

The upsurge of population, internal migration, economic activities and developmental works has brought significant land use and land cover (LULC) change over the period of 1990 and 2010 in the Bagmati basin of Nepal. Along with alteration on various other ecosystem services like water yield, water quality, soil loss etc. carbon sequestration is also altered. This study thus primary deals with evaluation of LULC change and its impact on the soil carbon storage for the period 1990 to 2010. For the evaluation, InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) Carbon model is used. Residential and several other infrastructural development activities were prevalent on the study period and as a result in 2010 major soil carbon reserve like forest area is decreased by 7.17% of its original coverage in 1990. This decrement has brought about a subsequent decrement of 1.39 million tons of carbon in the basin. Conversion from barren land, water bodies and built up areas to higher carbon reserve like forest and agriculture land has slightly increased soil carbon storage but still, net reduction is higher. Thus, the spatial output of the model in the form of maps is expected to help in decision making for future land use planning and for restoration policies.

• Korean Journal of Soil Science and Fertilizer
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• v.47 no.3
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• pp.205-212
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• 2014

Chronological changes of soil organic carbon (SOC) must be prepared by IPCC guidelines for national greenhouse gas inventories. IPCC suggested default reference SOC stocks for mineral soils and relative stock factors for different management activities where country own factors were not prepared. 3.4 million data were downloaded from agricultural soil information system and analyzed to get chronological changes of SOC for some counties and for land use in Korea. SOC content of orchard soil was higher than the other soils but chronological SOC changes of all land use had no tendency in differences with high standard deviation. SOC contents of counties depended on their own management activities and chronological SOC changes of districts also had no tendency in differences. Thus, Korea should survey the official records and relative stock factors on management activities such as land use, tillage and input of organic matter to calculate SOC stocks correctly. Otherwise, Korea should establish a model for predicting SOC by analyzing selected representative fields and by calculating SOC differences from comparing management activities of lands with those of representative fields.

• Journal of The Geomorphological Association of Korea
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• v.23 no.3
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• pp.67-78
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• 2016

Soil carbon(C) is an essential property for characterizing soil quality. Understanding spatial patterns of soil C is particularly limited for mountain areas. This study aims to predict the spatial pattern of soil C using terrain analysis in a steep mountainous area. Specifically, model performances and prediction uncertainties were investigated based on the number of resampling repetitions. Further, important predictors for soil C were also identified. Finally, the spatial distribution of uncertainty was analyzed. A total of 91 soil samples were collected via conditioned latin hypercube sampling and a digital soil C map was developed using support vector regression which is one of the powerful machine learning methods. Results showed that there were no distinct differences of model performances depending on the number of repetitions except for 10-fold cross validation. For soil C, elevation and surface curvature were selected as important predictors by recursive feature elimination. Soil C showed higher values in higher elevation and concave slopes. The spatial pattern of soil C might possibly reflect lateral movement of water and materials along the surface configuration of the study area. The higher values of uncertainty in higher elevation and concave slopes might be related to geomorphological characteristics of the research area and the sampling design. This study is believed to provide a better understanding of the relationship between geomorphology and soil C in the mountainous ecosystem.

• Journal of Korean Society of Forest Science
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• v.99 no.6
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• pp.791-801
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• 2010

Forest soil carbon model is a useful tool for understanding complex soil carbon cycle in forests and estimating dynamics of soil carbon to climate change. However, studies on development and application of the model are insufficient in Korea. The need for development of Korean model is now growing, because there are notable problems and limitations for adapting overseas models in Korea to meet the requirements of the international organizations such as IPCC, which demands highly reliable data for national reports. Therefore, we have studied 7 overseas forest soil carbon models (CBM-CFS3, CENTURY, Forest-DNDC, ROMUL, RothC, Sim-CYCLE, YASSO), analyzed and compared their structure, decomposition mechanism, initializing process and, input and output data. Then we evaluated applicability of these models in Korea with three criteria; availability of input data, performance of model, and possibility of regional modification. Finally, a systematic process for applying a new model was suggested based on these analyses.