• KOREAN JOURNAL OF CROP SCIENCE
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• v.42 no.1
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• pp.22-32
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• 1997

This experiment was conducted to obtain information for the cultivation of Korean valerian(Valeriana lauriei var. dasycarpa Hara) which will be useful for medicinal and aromatic resources. The effect of different temperature conditions, light intensities and soil water conditions on growth, yield and component of essential oil of V. fauriei were measured at the Dankook University, Cheonan, and a study on the shading treatment was at Umsung, Chungchongbukdo, and Jinbu, Kangwondo, in 1995. V. laudei was planted at five different temperature conditions, 10, 15, 20, 25 and 3$0^{\circ}C$, eight light intensity conditions, 1, 000, 2, 500, 5, 000, 20, 000, 30, 000, 40, 000, 50, 000 and 60, 000lux, six soil water contents, 30, 45, 55, 70, 80 and 90％ of the saturated soil, during growth stage. Shading treatment was three conditions, 0, 25 and 50％, during the daytime in field conditions. Photosynthesis had a highly significant relationship with temperature conditions in a quadratic regression model, from which the temperature for the plant growth was estimated to be 17.7$^{\circ}C$. A highly significant quadratic regression was noted between temperature and leaf width or root weight of V. fauriei. It was estimated from the regression equation that the optimum temperature for root growth was 20.3$^{\circ}C$. The content of essential oil and extract rate of root was the highest in the 15~2$0^{\circ}C$. Photosynthesis also was significantly affected by light intensity in a quadratic regression model, from which the optimum light intensity for the growth was estimated to be 40, 000lux. Root yield was more produced in Jinbu than that of in Umsung. The root yield was increased by the shading treatment in Umsung, whereas it was decreased by the shading treatment in Jinbu. The content of essential oil was not affected by the shading treatment of plants during the cultivation, while the compositions of components of essential oil were related to the growing locations. As soil water content was higher, the growth and content of root extract were increased. The optimum soil moisture for the growth of V. fauriei was 80~90％ of the saturated soil. In summary, the results indicated that the growth, yield and component of essential oil in V. fauriei were affected by environmental factors as well as soil moisture.

• Korean Journal of Breeding Science
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• v.51 no.4
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• pp.448-453
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• 2019

'Haedamssal' is an early maturing and rice stripe virus disease-resistant cultivar adaptable for early-transplanting cultivation that was developed by the rice breeding team of the Department of Southern Crop, NICS, RDA, in 2014. This cultivar was derived from the cross YR25869 (YR21247-B-B-B-49-1/Sasanishiki BL4//Koshihikari) and YR25868 (Unkwang//YR21247-B-B-B-49-1/Sasanishiki BL4) made in the 2005/2006 winter season and was advanced to the F₅ generation by a bulk breeding method using rapid generation advance. To incorporate rice stripe virus resistance, marker-assisted selection on the RSV gene was conducted in 3-way and 6-way cross F₁ generation using the tightly linked marker RM6897. From testing in the replicated yield trial in 2011, a promising line YR26258-B-B-B-33-3 was selected and it was designated as 'Milyang276'. A local adaptability test of 'Milyang276' was performed at three locations from 2012 to 2014 and it was named as 'Haedamssal', which was a good eating quality variety. The culm length was 67 cm in yield trials, which was 4 cm shorter than 'Jopyeong'. The number of spikelets per panicle was lower than 'Jopyeong', whereas the number of tillers per hill was higher. This variety was resistant to RSV disease, bacterial blight, and leaf blast disease. The milled rice yield of 'Haedamssal' was 5.48 MT per ha at the early transplanting in the local adaptability test. 'Haedamssal' is well adapted to early transplanting cultivation in the southern plain area (Registration No. 6811).

• Journal of Practical Agriculture & Fisheries Research
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• v.21 no.1
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• pp.61-70
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• 2019

"Jungmo2510", a rye cultivar, Secale cereal L., was developed by National Institute of Crop Science, RDA in 2015. It was developed from open pollination from within 10 rye cultivars or lines including "Chochun" in 1995. The line "SR95POP-S1-523-1-5-5-4-7-3-B-16-3-19" was selected for its excellent agronomic appearance and was placed in yield trials for two years from 2011 to 2012. The line was designated "Homil55" and was placed in regional yield trials at the four locations around Korea from 2013 to 2015, during which time the name "Jungmo2510" was given. This cultivar is an erect plant type and leaves of short and broad size with a green color, a yellow colored, medium-diameter culm, and a yellowish brown-colored, medium-size grain. The heading date of "Jungmo2510" was April 16, which were 2 days earlier than that of "Gogu". "Jungmo2510" also showed similar to winter hardiness and greater resistance to lodging compared to those of the check cultivar. Over three years, the average dry matter yield of "Jungmo2510" was 802 kg 10a^-1 , which was harvested in late April and was lower than that of the check cultivar "Gogu" (825 kg). The seed productivity of "Jungmo2510" was approximately 481 kg 10a^-1 , which was 2.4% less than that of the check. "Jungmo2510" was higher to than "Gogu" in term of protein content (9.1% and 8.0%, respectively), total digestible nutrients(TDN)(57.5% and 55.5%, respectively), and TDN yield 10a^-1(419 kg and 392 kg, respectively). This cultivar is recommended as a fall sowing crop in areas where the average daily minimum-mean temperatures are higher than -12 ℃ in January, and as a winter crop for whole-crop forage before the planting of rice or green manure around Korea.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.19-19
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• 2022

Hulled and naked oat is a perspective crop for the low input production systems due to its low requirements for soil quality and nutrition. Oats have good competitive ability against weeds and can provide appropriate yield in organic farming in comparison with other cereal species such as wheat or barley. It is a perspective crop from the point of view of use in the food industry too. The aim of our study was to compare the production and quality parameters of naked and hulled oat grown in both organic (OF) and conventional fields (CF). Small plot trials were conducted in two locations in the Czech Republic (České Budějovice, Prague) for four years (2018-2021) in two production systems (OF, and CF). We used four varieties of hulled oat (Korok, Kertag, Raven, Seldon) and one variety of naked oat (Patrik). During the vegetation, agronomically important data were recorded. After harvest samples were processed in the laboratory and analyzed selected quality parameters of grain dry matter (the protein content was determined by the Kjeldahl method, starch content in grain according to Ewers, fat content in grain dry matter by the modified method according to Soxhlet, and ash content in grain dry matter). The data were evaluated using the program STATISTICA version 13.2, StatSoft, Inc., California, USA. It is clear from the results that the number of panicles before the harvest was influenced by the location, cultivation system, year, and, to a lesser extent, the influence of the variety. The number of panicles in OF averaged 340 per square meter, which was 90% of the value of CF. For thousand grain weight (TGW), a significantly predominant effect of year was found. The independent effect of location on TGW was statistically not significant. Grain yield was predominantly influenced by cultivation system and location. In OF, it reached an average of 3.97 t.ha^-1, which was 75% of the yield of CF. As part of the evaluation of the basic grain quality indicators, the content of protein, starch, fat, and ash in the dry matter of the grain was evaluated. The content of protein in the dry matter of the grain was predominantly influenced by year, followed by the influence of the variety and a fairly comparable influence of the cultivation system and locality. On average, it achieved 16.05% in OF and 17.01% in CF. The starch content was then related to the protein content, where as a result of the lower protein content in the grain of OF oats, the content of starch and fat was on the contrary increased. The year turned out to be the most significant factor, affecting both the starch content in the dry matter of the grain and the fat content. This was followed again by a fairly comparable influence on the cultivation system and locality. The influence of the cultivation system and location was not statistically significantly applied in the case of ash content in dry matter. Based on our results we can propose both types of oat (hulled and naked) as perspective crops for OF. An organic farmer can expect to achieve stable yields which, in less favorable conditions for the production of cereals in the OF, may be close to the level of conventional yields. In the future, it will be important to change agrotechnology in OF and increase oat yield because this crop has a good potential to grow in areas with low nitrogen input or less fertile soil.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.353-365
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• 2023

The effects of climate change on green infrastructure and environmental media remain uncertain and context-specific despite numerous climate projections globally. In this study, the extreme weather conditions in seven major cities in South Korea were characterized through statistical analysis of 20-year daily meteorological data extracted fro m the Korea Meteorological Administration (KMA). Additionally, the impacts of extreme weather on Nature-based Solutions (NbS) were determined through a comprehensive review. The results of the statistical analysis and comprehensive review revealed the studied cities are potentially vulnerable to varying extreme weather conditions, depending on geographic location, surface imperviousness, and local weather patterns. Temperature extremes were seen as potential threats to the resilience of NbS in Seoul, as both the highest maximum and lowest minimum temperatures were observed in the mentioned city. Moreover, extreme values for precipitation and maximum wind speed were observed in cities from the southern part of South Korea, particularly Busan, Ulsan, and Jeju. It was also found that extremely low temperatures induce the most impact on the resilience of NbS and environmental media. Extremely cold conditions were identified to reduce the pollutant removal efficiency of biochar, sand, gravel, and woodchip, as well as the nutrient uptake capabilities of constructed wetlands (CWs). In response to the negative impacts of extreme weather on the effectiveness of NbS, several adaptation strategies, such as the addition of shading and insulation systems, were also identified in this study. The results of this study are seen as beneficial to improving the resilience of NbS in South Korea and other locations with similar climate characteristics.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• Journal of Korean Society of Disaster and Security
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• v.16 no.2
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• pp.15-32
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• 2023

There are some difficulties such as safety problem and need of manpower in measuring discharge by submerging the instruments because of many floating debris and very fast flow in the river during the flood season. As an alternative, microwave water surface current meters have been increasingly used these days, which are easy to measure the discharge in the field without contacting the water surface directly. But it is also hard to apply the method in the sudden and rapidly changing field conditions. Therefore, the estimation of the discharge using the surface velocity in flood conditions requires a theoretical and economical approach. In this study, the measurements from microwave water surface current meter and rating curve were collected and then analyzed by the discharge estimation method using the surface velocity. Generally, the measured and converted discharge are analyzed to be similar in all methods at a hydraulic radius of 3 m or over or a mean velocity of 2 ㎧ or more. Besides, the study computed the discharge by the index velocity method and the velocity profile method with the maximum surface velocity in the section where the maximum velocity occurs at the high water level range of the rating curve among the target locations. As a result, the mean relative error with the converted discharge was within 10%. That is, in flood season, the discharge estimation method using one maximum surface velocity measurement, index velocity method, and velocity profile method can be applied to develop high-level extrapolation, therefore, it is judged that the reliability for the range of extrapolation estimation could be improved. Therefore, the discharge estimation method using the surface velocity is expected to become a fast and efficient discharge measurement method during the flood season.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.120-129
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• 2023

To evaluate the vertical loads on railway bridges, conventional load tests are typically conducted. However, these tests often entail significant costs and procedural challenges. Railway conditions involve nearly identical load profiles due to standardized rail systems, which may appear straightforward in terms of load conditions. Nevertheless, this study aims to validate load tests conducted under operational train conditions by comparing the results with those obtained from conventional load tests. Additionally, static and dynamic structural behaviors are extracted from the measurement data for evaluation. To ensure the reliability of load testing, this research demonstrates feasibility through comparisons of existing measurement data with sensor attachment locations, train speeds, responses between different rail lines, tendency analysis, selection of impact coefficients, and analysis of natural frequencies. This study applies to the Dongho Railway Bridge and verifies the applicability of the proposed method. Ten operational trains and 44 sensors were deployed on the bridge to measure deformations and deflections during load test intervals, which were then compared with theoretical values. The analysis results indicate good symmetry and overlap of loads, as well as a favorable comparison between static and dynamic load test results. The maximum measured impact coefficient (0.092) was found to be lower than the theoretical impact coefficient (0.327), and the impact influence from live loads was deemed acceptable. The measured natural frequencies approximated the theoretical values, with an average of 2.393Hz compared to the calculated value of 2.415Hz. Based on these results, this paper demonstrates that for evaluating vertical loads, it is possible to measure deformations and deflections of truss railway bridges through load tests under operational train conditions without traffic control, enabling the calculation of response factors for stress adjustments.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.426-441
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• 2023

This study investigated the predictive accuracy of a model of landslide displacement in Jecheon-si, where a great number of landslides were triggered by heavy rain on both natural (non-clear-cut) and clear-cut slopes during August 2020. This was accomplished by applying three flow direction methods (single flow direction, SFD; multiple flow direction, MFD; infinite flow direction, IFD) and the degree of root cohesion to an infinite slope stability equation. The application assumed that the soil saturation and any changes in root cohesion occurred following the timber harvest (clear-cutting). In the study area, 830 landslide locations were identified via landslide inventory mapping from satellite images and 25 cm resolution aerial photographs. The results of the landslide modeling comparison showed the accuracy of the models that considered changes in the root cohesion following clear-cutting to be improved by 1.3% to 2.6% when compared with those not considered in the area under the receiver operating characteristics (AUROC) analysis. Furthermore, the accuracy of the models that used the MFD algorithm improved by up to 1.3% when compared with the models that used the other algorithms in the AUROC analysis. These results suggest that the discriminatory application of the root cohesion, which considers changes in the vegetation condition, and the selection of the flow direction method may influence the accuracy of landslide predictive modeling. In the future, the results of this study should be verified by examining the root cohesion and its dynamic changes according to the tree species using the field hydrological monitoring technique.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.