• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.65 no.4
• /
• pp.346-352
• /
• 2020

Currently, many studies are being conducted to cope with climate changes due to global warming and abnormal weather. The objective of this study was to investigate the effects of weather on the growth, yield components, and quality of soybeans using weather data from 2017 and 2018. The average temperature in 2018 was higher than that in 2017 from R1 to R5 of the growth stage for all cultivars. On the other hand, precipitation in 2018 was reduced compared to that in 2017 for Daewon and Daepung-2ho. It was observed that the flowering date in 2018 was earlier than that in 2017 for Daewon and Daepung-2ho, but the flowering date for Pungsannamul in 2018 was similar to that in 2017. Simulating soil water content with the estimation model (AFKAE0.5) determined that there were fewer drought dates in 2017 than those in 2018, and drought lasted from R1 to early R5 of the growth stage in 2018. Soybean growth in 2017 was better than that in 2018, and seed yield and 100-seed weight of soybean were higher in 2017 than those in 2018 for all cultivars. The seed size in 2017 was larger than that in 2018 for all cultivars. Oil content in 2017 was higher than that in 2018; in particular, the difference between both years was observed for Daewon and Daepung-2ho. Protein content was higher in 2018 than that in 2017; however, there were different levels for each cultivar. Thus, these results indicate that the yield component and quality of soybeans are affected by high temperature and drought.

• Progress in Medical Physics
• /
• v.20 no.4
• /
• pp.269-276
• /
• 2009

Radiation treatment techniques using photon beam such as three-dimensional conformal radiation therapy (3D-CRT) as well as intensity modulated radiotherapy treatment (IMRT) demand accurate dose calculation in order to increase target coverage and spare healthy tissue. Both jaw collimator and multi-leaf collimators (MLCs) for photon beams have been used to achieve such goals. In the Pinnacle3 treatment planning system (TPS), which we are using in our clinics, a set of model parameters like jaw collimator transmission factor (JTF) and MLC transmission factor (MLCTF) are determined from the measured data because it is using a model-based photon dose algorithm. However, model parameters obtained by this auto-modeling process can be different from those by direct measurement, which can have a dosimetric effect on the dose distribution. In this paper we estimated JTF and MLCTF obtained by the auto-modeling process in the Pinnacle3 TPS. At first, we obtained JTF and MLCTF by direct measurement, which were the ratio of the output at the reference depth under the closed jaw collimator (MLCs for MLCTF) to that at the same depth with the field size $10{\times}10\;cm^2$ in the water phantom. And then JTF and MLCTF were also obtained by auto-modeling process. And we evaluated the dose difference through phantom and patient study in the 3D-CRT plan. For direct measurement, JTF was 0.001966 for 6 MV and 0.002971 for 10 MV, and MLCTF was 0.01657 for 6 MV and 0.01925 for 10 MV. On the other hand, for auto-modeling process, JTF was 0.001983 for 6 MV and 0.010431 for 10 MV, and MLCTF was 0.00188 for 6 MV and 0.00453 for 10 MV. JTF and MLCTF by direct measurement were very different from those by auto-modeling process and even more reasonable considering each beam quality of 6 MV and 10 MV. These different parameters affect the dose in the low-dose region. Since the wrong estimation of JTF and MLCTF can lead some dosimetric error, comparison of direct measurement and auto-modeling of JTF and MLCTF would be helpful during the beam commissioning.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.21 no.2
• /
• pp.85-96
• /
• 2019

Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.6
• /
• pp.2641-2647
• /
• 2012

Quality and sensory characteristics such as microbial count, pH, acidity, flavor, taste, color and overall acceptance of bamboo shoot pickle cured with red pepper paste and bamboo shoot pickle cured with soy sauce paste made of Maengjong bamboo shoots were investigated during a long-term storage at different temperature (at $25^{\circ}C$, $35^{\circ}C$ and $45^{\circ}C$). Microbial contamination was not observed, and water content did not showed significant change in all samples of both pickles during the whole storage period of 30 days, regardless of storage temperature. At $25^{\circ}C$, all sensory characteristics of bamboo shoot-red pepper paste pickle did not show a significant change for 30 d. However, at $35^{\circ}C$ and $45^{\circ}C$, the flavor, taste and color of bamboo shoot-red pepper paste pickle did not change remarkably, but the overall acceptance significantly changed from the beginning of storage. Bamboo shoot-soy sauce pickle did not give a significant change in flavor, taste and overall acceptance at $25^{\circ}C$, $35^{\circ}C$ and $45^{\circ}C$. However a remarkable change in color started to be shown at 25 d in case of storage at $45^{\circ}C$. Overall acceptance and color were selected as indicating parameters for the shelf-life estimation of bamboo shoot-red pepper paste pickle and bamboo shoot-soy sauce pickle, respectively. Based on room temperature storage and delivery at $20^{\circ}C$, the shelf-life of bamboo shoot-red pepper paste pickle and bamboo shoot-soy sauce pickle were determined as 308 d (about 10 month) and 447 d (about 14 month), respectively.

• Korean Journal of Environmental Agriculture
• /
• v.37 no.3
• /
• pp.189-196
• /
• 2018

BACKGROUND: Water quality management of river requires quantification of pollutant loads and implementation of measures through monitoring study, but it requires labour and costs. Therefore, many researchers are performing nonpoint source pollution analysis using computer models. However, calibration of model parameters needs observed data. Nitrogen concentration in rainfall is one of the factors to be considered when estimating the pollutant loads through application of the nonpoint source pollution model, but the default value provided by the model is used when there are no observed data. Therefore, this study aims to provide the representative nitrogen concentration of the rainfall for the administrative district ensuring rational modeling and reliable results. METHODS AND RESULTS: In this study, rainfall monitoring data from June 2015 to December 2017 were used to determine the nitrogen concentration in rainfall for each administrative district. Range of the $NO_3{^-}$ and $NH_4{^+}$ concentrations were 0.41~6.05 mg/L, 0.39~2.27 mg/L, respectively, and T-N concentration was 0.80~7.71 mg/L. Furthermore, the national average of T-N concentration in this study was $2.84{\pm}1.42mg/L$, which was similar to the national average of T-N 3.03 mg/L presented by the Ministry of Environment in 2015. Therefore, the nitrogen concentrations suggested in this study can be considered to be resonable values. CONCLUSION: The nitrogen concentrations estimated in this study showed regional differences. Therefore, when estimating the pollutant loads through application of the nonpoint source pollution model, resonable parameter estimation of nitrogen concentration in rainfall is possible by reflecting the regional characteristics.

• Korean Journal of Environmental Biology
• /
• v.35 no.4
• /
• pp.549-556
• /
• 2017

Rivers continuously transport terrestrial organic carbon matter to the estuary and the ocean, and they play a critical role in productivity and biodiversity in the marine ecosystem as well as the global carbon cycle. The amount of terrestrial organic carbon transporting from the rivers to ocean is an essential piece of information, not only for the marine ecosystem management but also the carbon budget within catchment. However, this phenomenon is still not well understood. Most large rivers in Korea have a well-established national monitoring system of the river flow and the TOC (Total Organic Carbon) concentration from the mountain to the river mouth, which are fundamental for estimating the amount of the TOC flux. We estimated the flux of the total terrestrial organic carbon of five large rivers which flow out to the Yellow Sea, using the data of the national monitoring system (the monthly mean TOC concentration and the monthly runoff of river flow). We quantified the annual TOC flux of the five rivers, showing their results in the following order: the Han River ($18.0{\times}10^9gC\;yr^{-1}$)>>Geum River ($5.9{\times}10^9gC\;yr^{-1}$)>Yeongsan River ($2.6{\times}10^9gC\;yr^{-1}$)>Sumjin River ($2.0{\times}10^9gC\;yr^{-1}$)>>Tamjin River ($0.2{\times}10^9gC\;yr^{-1}$). The amount of the Han River, which is the highest in the Korean rivers, corresponds to be 4% of the annual total TOC flux of in the Yellow River, and moreover, to be 0.6% of Yangtze River.

• Journal of Korean Society of Environmental Engineers
• /
• v.22 no.7
• /
• pp.1331-1343
• /
• 2000

Through the integration of USLE and GIS, the methodology to estimate the soil loss was developed, and applicated to the Sacheon river in Gangrung. Using GIS, spatial analysis such as watershed boundary determination, flow routing. slope steepness calculation was done. Spatial information from the GIS application was given for each grid. With soil and land use map, information about soil classification and land use was given for each grid too. Based upon these data, thematic maps about the factors of USLE were made. We estimated the soil loss by overlaying the thematic maps. In this manner, we can assess the degree of soil loss for each grid using GIS. Annual average soil loss of Sacheon river watershed is 1.36 ton/ha/yr. Soil loss in forest, dry field, and paddy field is 0.15 ton/ha/yr, 27.04 ton/ha/yr, 0.78 ton/ha/yr respectively. The area of dry field, which is 4% of total area, is $2.4km^2$. But total soil loss of dry field is 6561 ton/yr, and it occupies 84.9 % of total soil loss eroded in Sacheon river watershed. Comparing with the 11.2 ton/ha/yr of an average soil loss tolerance for cropland, provision for the soil loss in dry field is necessary. Run-off and water quality of Sacheon river were measured two times in flood season: from July 24, 1998 to July 28 and from September 29 to October 1. As the run-off of the river increased, SS, TN, TP concentrations and pollutant loadings increased. SS, TN, TP loads of Sacheon river discharged during the 2 heavy rains were 21%, 39%, and 19% of the total pollutant loadings generated in the Sacheon river watershed for one year. We can see that much pollutants are discharged in short period of flood season.

• Journal of Korean Society of Forest Science
• /
• v.90 no.2
• /
• pp.197-209
• /
• 2001

TOPMODEL, semi-distributed hydrological model, is frequently applied to predict the amount of discharge, main flow pathways and water quality in a forested catchment, especially in a spatial dimension. TOPMODEL is a kind of conceptual model, not physical one. The main concept of TOPMODEL is constituted by the topographic index and soil transmissivity. Two components can be used for predicting the surface and subsurface contributing area. This study is conducted for the validation of applicability of TOPMODEL at small forested catchments in Korea. The experimental area is located at Gwangneung forest operated by Korea Forest Research Institute, Gyeonggi-do near Seoul metropolitan. Two study catchments in this area have been working since 1979 ; one is the natural mature deciduous forest(22.0 ha) about 80 years old and the other is the planted young coniferous forest(13.6 ha) about 22 years old. The data collected during the two events in July 1995 and June 2000 at the mature deciduous forest and the three events in July 1995 and 1999, August 2000 at the young coniferous forest were used as the observed data set, respectively. The topographic index was calculated using $10m{\times}10m$ resolution raster digital elevation map(DEM). The distribution of the topographic index ranged from 2.6 to 11.1 at the deciduous and 2.7 to 16.0 at the coniferous catchment. The result of the optimization using the forecasting efficiency as the objective function showed that the model parameter, m and the mean catchment value of surface saturated transmissivity, $lnT_0$ had a high sensitivity. The values of the optimized parameters for m and InT_0 were 0.034 and 0.038; 8.672 and 9.475 at the deciduous and 0.031, 0.032 and 0.033; 5.969, 7.129 and 7.575 at the coniferous catchment, respectively. The forecasting efficiencies resulted from the simulation using the optimized parameter were comparatively high ; 0.958 and 0.909 at the deciduous and 0.825, 0.922 and 0.961 at the coniferous catchment. The observed and simulated hyeto-hydrograph shoed that the time of lag to peak coincided well. Though the total runoff and peakflow of some events showed a discrepancy between the observed and simulated output, TOPMODEL could overall predict a hydrologic output at the estimation error less than 10 %. Therefore, TOPMODEL is useful tool for the prediction of runoff at an ungaged forested catchment in Korea.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.24 no.7
• /
• pp.848-857
• /
• 2018

Recently, around the world, active development of new renewable energy sources including solar power, waves, and fuel cells, etc. has taken place. Particularly, floating offshore wind farms have been developed for saving costs through large scale production, using high-quality wind power and minimizing noise damage in the ocean area. The development of floating wind farms requires an evaluation of the Maritime Safety Audit Scheme under the Maritime Safety Act in Korea. Floating wind farms shall be assessed by applying the line and area concept for systematic development, management and utilization of specified sea water. The development of appropriate evaluation methods and standards is also required. In this study, proper standards for marine traffic surveys and assessments were established and a systemic treatment was studied for assessing marine spatial area. First, a marine traffic data collector using AIS or radar was designed to conduct marine traffic surveys. In addition, assessment methods were proposed such as historical tracks, traffic density and marine traffic pattern analysis applying the line and area concept. Marine traffic density can be evaluated by spatial and temporal means, with an adjusted grid-cell scale. Marine traffic pattern analysis was proposed for assessing ship movement patterns for transit or work in sea areas. Finally, conceptual design of a Marine Traffic and Safety Assessment Solution (MaTSAS) was competed that can be analyzed automatically to collect and assess the marine traffic data. It could be possible to minimize inaccurate estimation due to human errors such as data omission or misprints through automated and systematic collection, analysis and retrieval of marine traffic data. This study could provides reliable assessment results, reflecting the line and area concept, according to sea area usage.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.329-339
• /
• 2021

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R²=0.74, RMSE=246 g/m², RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m² and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m² and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m²) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.