• The Plant Pathology Journal
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• v.23 no.4
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• pp.239-244
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• 2007

MicroTom is a miniature tomato plants with various properties that make it as a model system for experiments in plant molecular biology. To extend its utility as a model plant to study a plant - bacterial wilt system, we investigated the potential of the MicroTom as a host plant of bacterial wilt caused by Ralstonia solanacearum. We compared the disease progress on standard tomato and MicroTom by two inoculation methods, root dipping and soil drenching, using a race 1 strain GMI1000. Both methods caused the severe wilting on MicroTom comparable to commercial tomato plant, although initial disease development was faster in root dipping. From the diseased MicroTom plants, the same bacteria were successfully reisolated using semiselective media to fulfill Koch's postulates. Race specific and isolate specific virulence were investigated by root dipping with 10 isolates of R. solanacearum isolated from tomato and potato plants. All of the tested isolates caused the typical wilt symptom on MicroTom. Disease severities by isolates of race 3 was below 50 % until 15 days after inoculation, while those by isolates of race 1 reached over 50% to death until 15 days. This result suggested that MicroTom can be a model host plant to study R. solanacearum - plant interaction.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.2
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• pp.115-126
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• 2017

There is an increasing concern over arsenic (As) contamination in rice. This study was conducted to develope a prediction model for As uptake by rice based on the physico-chemical properties of soil. Soil and brown rice samples were collected from 46 sites in paddy fields near three different areas of closed mines and industrial complexes. Total As concentration, soil pH, Al oxide, available phosphorus (avail-P), organic matter (OM) content, and clay content in the soil samples were determined. Also, 1.0 N HCl, 1.0 M $NH_4NO_3$, 0.01 M $Ca(NO_3)_2$, and Mehlich 3 extractable-As in the soils were measured as phytoavailable As concentration in soil. Total As concentration in brown rice samples was also determined. Relationships among As concentrations in brown rice, total As concentrations in soils, and selected soil properties were as follows: As concentration in brown rice was negatively correlated with soil pH value, where as it was positively correlated with Al oxide concentration, avail-P concentration, and OM content in soil. In addition, the concentration of As in brown rice was statistically correlated only with 1.0 N HCl-extractable As in soil. Also, using multiple stepwise regression analysis, a modelling equation was created to predict As concentration in brown rice as affected by selected soil properties including soil As concentration. Prediction of As uptake by rice was delineated by the model [As in brown rice = 0.352 + $0.00109^*$ HCl extractable As in soil + $0.00002^*$ Al oxide + $0.0097^*$ OM + $0.00061^*$ avail-P - $0.0332^*$ soil pH] ($R=0.714^{***}$). The concentrations of As in brown rice estimated by the modelling equation were statistically acceptable because normalized mean error (NME) and normalized root mean square error (NRMSE) values were -0.055 and 0.2229, respectively, when compared with measured As concentration in the plant.

• Journal of the Korean GEO-environmental Society
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• v.17 no.4
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• pp.17-31
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• 2016

Many researchers have evaluated the influence of vegetation cover on slope stability. However, due to the extensive variety of site conditions and vegetation types, different studies have often provided inconsistent results, especially when evaluating in different regions. Therefore, additional studies need to be conducted to identify the positive impacts of vegetation cover for slope stabilization. This study used the Transient Rainfall Infiltration and Grid-based Regional Slope-stability Model (TRIGRS) to predict the occurrence of landslides in a watershed in Jinbu-Myeon, Pyeongchang-gun, Korea. The influence of vegetation cover was assessed by spatially and temporally comparing the predicted landslides corresponding to multiple trials of cohesion values (which include the role of root cohesion) and real observed landslide scars to back-calculate the contribution of vegetation cover to slope stabilization. The lower bound of cohesion was defined based on the fact that there are no unstable cells in the raster stability map at initial conditions, and the modified success rate was used to evaluate the model performance. In the next step, the most reliable value representing the contribution of vegetation cover in the study area was applied for landslide assessment. The analyzed results showed that the role of vegetation cover could be replaced by increasing the soil cohesion by 3.8 kPa. Without considering the influence of vegetation cover, a large area of the studied watershed is unconditionally unstable in the initial condition. However, when tree root cohesion is taken into account, the model produces more realistic results with about 76.7% of observed unstable cells and 78.6% of observed stable cells being well predicted.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2000.11a
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• pp.64-68
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• 2000

장기간 관측된 지하수위 자료를 시계열분석 중의 하나인 전이함수 모형(Transfer Function - Noise model)을 이용하여 분석하였다. 일반적으로 전이함수 모형은 입력 변수와 출력변수와의 관계가 선형적일 때 적용이 가능하며, 자료가 시간에 대해 연속적으로 존재해야 하는 제한이 있다. 강수량과 지하수위의 변동은 비선형적인 관계를 가지고 있어 이러한 전이함수 모형을 직접 적용하는데는 어려움이 있다. 이러한 비선형성의 정도를 감소시키기 위해 물리모형(HYDRUS)을 이용하여 침투량을 계산하고 이를 입력변수로 사용하여 전이함수 모형을 적용하였다. 침투량을 입력변수로 모형을 추정하였을 때, 강수량을 직접 입력자료로 사용했을 경우보다 ME(mean error), RMSE(root-mean-squre error), MAE(mean absolute error)에서 상대적으로 작은 값을 보여주고 있다. TFN 모형의 모수를 추정하기 위해서 Kalman 필터 알고리즘과 최우추정법(Maximum Likelihood Estimation)을 이용하였다. Kalman 필터 알고리즘을 이용하여 불규칙한 관측주기를 갖는 시계열이나 결측값이 있는 시계열에 대해서도 전이함수 모형을 구하였으며, 이를 통해 결측값에 대한 추정이 가능하였다.

• Journal of the Korean Geotechnical Society
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• v.32 no.6
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• pp.27-40
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• 2016

Conventional warning criteria for landslides due to rainfall in broad regions have limitations, because they did not have proper reflection of topography, forest physiognomy, and unsaturated soil properties, et al. This study suggested a new stability model for unsaturated slope analyses during rainfall, considering rainfall pattern, geomorphological characteristics (slope angle, soil depth), engineering properties of unsaturated soils, and tree surcharge and root reinforcement. Stability analysis not considering root reinforcement and tree surcharge tends to over-predict a factor of safety in unsaturated slopes. Developed slope stability model was used to build database on the factor of safety in unsaturated slopes during rainfall, and it was integrated with GIS to do quantitative risk analysis in landslide risk areas specified in Ulju. Landslide risk areas were located at downstream of the point with sudden drop in safety factor, as well as at regions with low safety factor during rainfall.

• Mycobiology
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• v.48 no.2
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• pp.139-147
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• 2020

Root-feeding Scarabaeidae, particularly white grubs are considered among the most harmful coleopteran insect pests in turfgrass. In this work, sixteen entomopathogenic fungal species were assayed against flower chafer beetle, Protaetia brevitarsis (Coleoptera: Scarabaeidae) and Metarhizium anisopliae JEF-314 showed high virulence. The control ability of the isolate JEF-314 has been in detail tested for a model insect flower chafer beetle. Further analyses showed insect stage-dependent virulence where the fungal virulence was the highest against smaller instar larvae. Additionally, we confirmed that millet-based solid cultured granule was effective against the soil-dwelling larval stage. The isolate also showed a similar ability for a representative pest (Popillia spp.) in laboratory conditions. Our results clearly suggest a high potential of M. anisopliae JEF-314 to control the flower chafer beetle, possibly resulting in controlling of root-feeding white grubs in turfgrass. Based on the insect life cycle and susceptibility to the fungus, late spring and summer time would be the optimum time to apply JEF-314 granules for an effective control. Further characterization of the efficacy of the fungus under field conditions against the Scarabaeidae beetles might provide an efficient tool to control this beetle in an environment-friendly way.

• Korean Journal of Soil Science and Fertilizer
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• v.40 no.3
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• pp.208-213
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• 2007

When overland flow water is small and slow, it moves down a stream slowly and we use it as available resource. However, it could not only be good for nothing but arouse an inundation if a lot of runoff pour down to stream at a torrential rain. So it is important to know how much water to flow out and be stored in soil and on land in order to predict a flood and conserve soil and water quality. We intended to develop the prediction model of runoff in upland at a torrential rain and conducted lysimeter study in soybean cultivation and bare soil with 3 slopeness, 3 slope length and 5 soil texture from 1985 to 1991. The data of rainfall and runoff were used when daily rainfall was over 80 mm, the level of torrential rain warning. Minimum rainfall occurring runoff (MROR) was dependent on surface coverage and slope length. However soil texture and slopeness had a little influence on MROR. Runoff after MROR increased in proportion to precipitation which depended on surface coverage, soil texture and slope. Runoff ratio was larger in fine texture and bare soil than coarse soil and soybean coverage. Runoff ratio was in proportion to a square root of slope angle(radian) and reduced with slope length to converge a certain value. From these basis, we developed the prediction model following as $$Runoff(mm)=a(s^{0.5}+l^b)(Rainfall(mm)-80(1-e^{-bl}))$$ where a is a coefficient relevant soil hydraulic properties, b is a surface coverage coefficient, s is a slope angle and l is a slope length. The coefficient a was 0.5 in sandy loam and 0.6 in clay, and b was 0.06 in bare soil and 0.5 in soybean cultivation.

• Journal of Biosystems Engineering
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• v.34 no.5
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• pp.382-389
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• 2009

An in-situ probe-based spectrophotometer has been developed. This system used two spectrometers to measure soil reflectance spectra from 450 nm to 2200 nm. It collects soil electrical conductivity (EC) and insertion force measurements in addition to the optical data. Six fields in Kansas were mapped with the VIS-NIR (visible-near infrared) probe module and sampled for calibration and validation. Results showed that VIS-NIR correlated well with carbon in all six fields, with RPD (the ratio of standard deviation to root mean square error of prediction) of 1.8 or better, RMSE of 0.14 to 0.22%, and $R^2$ of 0.69 to 0.89. From the investigation of carbon variability within the soil profile and by tillage practice, the 0-5 cm depth in a no-till field contained significantly higher levels of carbon than any other locations. Using the selected calibration model with the soil NIR probe data, a soil profile map of estimated carbon was produced, and it was found that estimated carbon values are highly correlated to the lab values. The array of sensors (VIS-NIR, electrical conductivity, insertion force) used in the probe allowed estimating bulk density, and three of the six fields were satisfactory. The VIS-NIR probe also showed the obtained spectra data were well correlated with nitrogen for all fields with RPD scores of 1.84 or better and coefficient of determination ($R^2$) of 0.7 or higher.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.4
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• pp.9-19
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• 2014

The objective of this study was to evaluate of the Tank model in simulating runoff discharge from rural watershed in comparison to the SWAT (Soil and Water Assessment Tool) model. The model parameters of SWAT was calibrated by the shuffled complex evolution-university Arizona (SCE-UA) method while Tank model was calibrated by genetic algorithm (GA) and validated. Four dam watersheds were selected as the study areas. Hydrological data of the Water Management Information System (WAMIS) and geological data were used as an input data for the model simulation. Runoff data were used for the model calibration and validation. The determination coefficient ($R^2$), root mean square error (RMSE), Nash-Sutcliffe efficiency index (NSE) were used to evaluate the model performances. The result indicated that both SWAT model and Tank model simulated runoff reasonably during calibration and validation period. For annual runoff, the Tank model tended to overestimate, especially for small runoff (< 0.2 mm) whereas SWAT model underestimate runoff as compared to observed data. The statistics indicated that the Tank model simulated runoff more accurately than the SWAT model. Therefore the Tank model could be a good tool for runoff simulation considering its ease of use.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.123-132
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• 2019

This study is to estimate the spatial soil moisture using Terra MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data and machine learning technique. Using the 3 years (2015~2017) data of MODIS 16 days composite NDVI (Normalized Difference Vegetation Index) and daily Land Surface Temperature (LST), ground measured precipitation and sunshine hour of KMA (Korea Meteorological Administration), the RDA (Rural Development Administration) 10 cm~30 cm average TDR (Time Domain Reflectometry) measured soil moisture at 78 locations was tested. For daily analysis, the missing values of MODIS LST by clouds were interpolated by conditional merging method using KMA surface temperature observation data, and the 16 days NDVI was linearly interpolated to 1 day interval. By applying the RNN-LSTM (Recurrent Neural Network-Long Short Term Memory) artificial neural network model, 70% of the total period was trained and the rest 30% period was verified. The results showed that the coefficient of determination ($R^2$), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency were 0.78, 2.76%, and 0.75 respectively. In average, the clay soil moisture was estimated well comparing with the other soil types of silt, loam, and sand. This is because the clay has the intrinsic physical property for having narrow range of soil moisture variation between field capacity and wilting point.