• Journal of Korean Society of Forest Science
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• v.56 no.1
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• pp.66-76
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• 1982

Soil harness represents such physical properties as porosity, amount of water, bulk density and soil texture. It is very important to know the mechanical properties of soil as well as the chemical in order to research the fundamental phenomena in the growth and the distribution of tree roots. The writer intended to grip soil hardness by soil layer and also to grasp the root distribution and the correlation between soil hardness and the root distribution of Pinus riguda Mill. planted on the denuded hillside with sooding works by soil layer on soil profile. The site investigated is situated at Peongchang-ri 13, Kocksung county, Chon-nam Province. The area is consisted of 3.63 ha having on elevation of 167.5-207.5 m. Soil texture is sandy loam and parant rock in granite. Average slope of the area is $17^{\circ}-30^{\circ}$. Soil moisture condition is dry. Main exposure of the area is NW or SW. The total number of plots investigated was 24 plots. It divided into two groups by direction each 12 plots in NW and SW and divided into three groups by the position of mountain plots in foot of mountain, in hillside, and in summit of mountain, respectively. Each sampling tree was selected as specimen by purposive sampling and soil profile was made at the downward distance of 50cm form the sampling tree at each plot. Soil hardness, soil layer surveying, root distribution of the tree and vegetation were measured and investigated at the each plot. The soil hardness measured by the Yamanaka Soil Hardness Tester in mm unit. the results are as follows: 1) Soil hardness increases gradually in conformity with the increment of soil depth. The average soil indicator hardness by soil layer are as follows: 14.6mm in I - soil layer (0-10cm in depth from soil surface), 16.2mm in II - soil layer (10-20cm), 17.2 in III - soil layer (20-30cm), 18.3mm in IV - soil layer(30-40cm), 19.8mm in V - soil layer (4.50mm). 2) The tree roots (less than 20mm in diameter) distribute more in the surface layer than in the subsoil layer and decrease gradually according to the increment of soil depth. The ratio of the root distribution can be illustrated by comparing with each of five soil layers from surface to subsoil layer as follows: I - soil layer; 31%, II - soil layer; 26%, III - soil layer; 18%, IV - soil layer; 12%, V - soil layer; 13%, 3) Soil hardness and tree root distribution (less than 20mm in diameter) of Pinus rigida Mill. correlate negatively each other; the more soil hardness increases, the most root distribution decreases. The correlation coefficients between soil hardness and distribution of tree roots by soil layer are as follows: I - soil layer; -0.3675 (at the 10% significance level), II - soil layer; -0.5299 (at the 1% significance level), III - soil layer; -0.5573 (at the 2% significance level), IV - soil layer; -0.6922 (at the 5% significance level), V - soil layer; -0.7325 (at the 2% significance level). 4) the most suitable range of soil hardness for the growth of Pinus rigida Mill is the range of 12-14.9mm in soil indicator hardness. In this range of soil indicator hardness, the root distribution of this tree amounts to 41.8% in spite of 33% in soil harness and under the 20.9mm of soil indicator hardness, the distribution amounts to 93.2% in spite of 82% in soil hardness. Judging from above facts, the roots of Pinus rigida can easily grow within the soil condition of 20.9mm in soil indicator hardness. 5) The soil layers are classified by their depths from the surface soil.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.823-827
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• 2010

This study was conducted to assess the propriety of models for soil water characteristics estimation in anthropogenic soil through the measurement of soil water content and soil water matric potential. Soil profile was characterized with four different soil layers. Soil texture was loamy sand for the first soil layer (from soil surface to 30 cm soil depth), sand for the second (30~70 cm soil depth) and the third soil layers (70~120 cm soil depth), and sandy loam for the fourth soil layer (120 cm < soil depth). Soil water retention curve (SWRC), the relation between soil water content and soil water matric potential, took a similar trend between different layers except the layer of below 120 cm soil depth. The estimation of SWRC and air entry value was better in van Genuchten model by analytical method than in Brooks-Corey model with power function. Therefore, it could be concluded that van Genuchten model is more desirable than Brook-Corey model for estimating soil water characteristics of anthropogenic soil accumulated with saprolite.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.11 no.6
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• pp.81-90
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• 2008

Recently, geofiber(polyester) reinforced soil was added on soil-seed mixture spray to control erosion and to improve vegetation growth on rocky slope sites. This research was conducted to compare vegetation effects and soil hardness on three types of soil-seed mixture spray on stone gabion river bank [A type : soil-seed mixture spray underlying 30cm thick sand with geofiber(geofiber reinforced soil system), B type : soil-seed mixture spray underlying 30cm thick sand without geofiber, C type : soil-seed mixture spray]. Evaluation were made concerning vegetation coverage, soil hardness and moisture content. The results of this study showed that A type system was effective for the growth of vegetation and soil hardness when compareed to B type and C type. A type and B type showed higher covering rate than C type on stone gabion river bank, and especially A type showed the highest covering rate. Soil hardness and water content were high on A type vegetation system compared to B type and C type. We noted that high soil hardness and high moisture content with geofiber(geofiber reinforced soil system) were effective both to control erosion from water current impact and to be high coverage and species of vegetation on stone gabion river bank.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.6
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• pp.426-433
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• 2013

This study was aimed to classify soil desalination area for cultivation using NDVI (Normalized difference vegetation index) of high-resolution satellite image because the soil salinity affects the change of plant community in reclaimed lands. We measured the soil salinity and NDVI at 28 sites in the Saemangeum reclaimed land in June 2013. In halophyte and non-vegetation sites, no relation was found between NDVI and soil salinity. In glycophyte sites, however, we found that the soil salinity was below 0.1% and NDVI ranged from 0.11 to 0.57 which was greater than the other sites. So, we could distinguish the glycophyte sites from the halophyte sites and non-vegetation, and classify the area that soil salinty was below 0.1%. This technique could save the time and labor to measure the soil salinity in large area for agricultural utilization.

• Advances in environmental research
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• v.4 no.3
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• pp.197-210
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• 2015

Hexavalent chromium [Cr (VI)] adsorption on lateritic soil and lateritic soil blended with black cotton (BC) soil, marine clay and bentonite clay were studied in the laboratory using batch adsorption techniques. In the present investigation the natural laterite soil was blended with 10%, 20% and 30% BC soil, marine clay and bentonite clay separately. The interactions on test soils have been studied with respect to the linear, Freundlich and Langmuir isotherms. The linear isotherm parameter, Freundlich and Langmuir isotherm parameters were determined from the batch adsorption tests. The adsorption of Cr (VI) on natural laterite soil and blended laterite soil was determined using double beam spectrophotometer. The distribution coefficients obtained were 1.251, 1.359 and 2.622 L/kg for lateritic soil blended with 10%, 20% and 30% BC soil; 5.396, 12.973 and 48.641 L/kg for lateritic soil blended with marine clay and 5.093, 8.148 and 12.179 L/kg for lateritic soil blended with bentonite clay respectively. The experimental data fitted well to the Langmuir model as observed from the higher value of correlation coefficient. Soil pH and iron content in soil(s) has greater influence on Cr (VI) adsorption. From the study it is concluded that laterite soil can be blended with clayey soils for removing Cr (VI) by adsorption.

• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.7
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• pp.1175-1183
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• 2010

This study investigates the effect of polarity of oily soil on adhesion of oily and particulate soil to PET fabric in oily/particulate mixed soil systems. The potential energy of interaction between two particles was examined as a fundamental environment of adhesion of soil to fabrics. The ${\zeta}$-potential of ${\alpha}-Fe_2O_3$ particles was measured by a microelectrophoresis method, and the potential energy of interaction between two particles was calculated by using the Verwey-Overbeek theory. The ${\zeta}$-potential of particle and the potential energy of interaction between two particles was slightly influenced by the polarity and type of oily soil, but increased with the increased anionic surfactant concentration and amount of oily soil. The adhesion of oily soil to fabric increased with the additional amount of polarity of oily soil and decreased surfactant concentration that was relatively high at a temperature of $60^{\circ}C$ surfactants solution. The adhesion of ${\alpha}-Fe_2O_3$ particle to PET fabric decreased with an increased amount and polarity of oily soil and increased surfactant concentration Although some similarity exists, the general trend of the adhesion to fabric by particulate soil differ from oily soil.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.3
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• pp.153-162
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• 2015

Land information is important for the international agricultural companies. This study investigated the agriculture and soil information in Sri Lanka. This study is the results from investigation of soil properties and agricultural properties determined by the Soil Taxonomy classification system for the soils in Sri Lanka. The order of the main agricultural imports in Sri Lanka was wheat > refined Sugar > dry Onion > Rice > Lentils. The climate of Sri Lanka is divided into three climatic zones. There are a wet zone, an intermediate zone, and a dry zone. Rainfall of the wet zone was $3,000-5,000mm\;year^{-1}$. The rainfall of the dry zone was less than $1,000m^{-1}$. The intermediate zone was in the middle area. Soil series of Sri Lanka were 109 in total. Detailed information of soil series was: 6 of soil Orders, 15 of Suborders, 39 of Great groups, and 56 of Subgroups. Soil texture of topsoil was much more coarse, but subsoil was gravelly coarse soil. Soil of Sri Lanka was classified as a Soil Order. The orders were Entisols > Alfisols > Ultisols > Inceptisols > Histosols > Vertisols.

• Water Engineering Research
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• v.3 no.1
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• pp.31-44
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• 2002

The effect of diurnal cycle, intermittent visit of observation satellite, sensor installation, partial coverage of remote sensing, heterogeneity of soil properties and precipitation to the soil moisture estimation error were analyzed to present the global sampling strategy of soil moisture. Three models, the theoretical soil moisture model, WGR model proposed Waymire of at. (1984) to generate rainfall, and Turning Band Method to generate two dimensional soil porosity, active soil depth and loss coefficient field were used to construct sufficient two-dimensional soil moisture data based on different scenarios. The sampling error is dominated by sampling interval and design scheme. The effect of heterogeneity of soil properties and rainfall to sampling error is smaller than that of temporal gap and spatial gap. Selecting a small sampling interval can dramatically reduce the sampling error generated by other factors such as heterogeneity of rainfall, soil properties, topography, and climatic conditions. If the annual mean of coverage portion is about 90%, the effect of partial coverage to sampling error can be disregarded. The water retention capacity of fields is very important in the sampling error. The smaller the water retention capacity of the field (small soil porosity and thin active soil depth), the greater the sampling error. These results indicate that the sampling error is very sensitive to water retention capacity. Block random installation gets more accurate data than random installation of soil moisture gages. The Walnut Gulch soil moisture data show that the diurnal variation of soil moisture causes sampling error between 1 and 4 % in daily estimation.

• Geomechanics and Engineering
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• v.10 no.6
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• pp.793-806
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• 2016

The pozzolanic characteristics of a sludge incinerated into ash were determined in this study. Lime is commonly used as a stabilizer for the treatment of soils, whereas sewage sludge ash (SSA) is often applied with lime to improve soft subgrade soil. In this study, a cohesive soil categorized as A-4 (low-plasticity clay) by AASHTO classifications was mixed with SSA/lime with a 3:1 ratio. Nano-$SiO_2$ was also added to the soil. To identify changes in the workability, strength, permeability, and shear strength of the soft subgrade soil, basic soil tests were conducted, and the microstructure of the treated soil was analyzed. The results indicate that SSA/lime mixtures improve the properties of soft subgrade soil and transform the soil from "poor subgrade soil" to "good to excellent subgrade soil" with a CBR > 8. Additionally, the addition of 2% nano-$SiO_2$ increases the unconfined compressive strength of soft subgrade soil treated with SSA/lime mixture by approximately 17 kPa. However, the swelling of the treated soil increased by approximately 0.1% after the addition of nano-$SiO_2$ and lime. Thus, soil swelling should be considered before lime and nano-$SiO_2$ are applied to soft subgrade soil.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.1
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• pp.56-64
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• 2017

Soil quality has been regarded as an important factor for maintaining sustainability of ecosystem. Main purpose of this research was i) to select minimum factor for predicting biomass, and ii) to calculate soil quality index for biomass according to soil chemical properties. Result showed that soil pH, electrical conductivity (EC), soil organic matter (SOM), cation exchange capacity (CEC), and available phosphorus are minimum data set for calculating biomass production in soil. Selected representative soil chemical properties were evaluated for soil quality index and rated from 1 to 5 (1 is the best for biomass production). Percentage of each grade in terms of biomass production in national wide was 14.52, 35.23, 33.03, 6.47, 10.75% respectively. Although, only soil chemical properties were evaluated for calculating optimum soil quality, result of this research can be useful to understand basic protocol of soil quality assessment in national wide.