• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.374-390
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• 2021

Growth and yield of field vegetable crops would be affected by climate conditions, which cause a relatively large fluctuation in crop production and consumer price over years. The yield prediction system for these crops would support decision-making on policies to manage supply and demands. The objectives of this study were to compile literatures related to onion and garlic and to perform data-mining analysis, which would shed lights on the development of crop models for these major field vegetable crops in Korea. The literatures on crop growth and yield were collected from the databases operated by Research Information Sharing Service, National Science & Technology Information Service and SCOPUS. The keywords were chosen to retrieve research outcomes related to crop growth and yield of onion and garlic. These literatures were analyzed using text mining approaches including word cloud and semantic networks. It was found that the number of publications was considerably less for the field vegetable crops compared with rice. Still, specific patterns between previous research outcomes were identified using the text mining methods. For example, climate change and remote sensing were major topics of interest for growth and yield of onion and garlic. The impact of temperature and irrigation on crop growth was also assessed in the previous studies. It was also found that yield of onion and garlic would be affected by both environment and crop management conditions including sowing time, variety, seed treatment method, irrigation interval, fertilization amount and fertilizer composition. For meteorological conditions, temperature, precipitation, solar radiation and humidity were found to be the major factors in the literatures. These indicate that crop models need to take into account both environmental and crop management practices for reliable prediction of crop yield.

• Journal of the Korean Electrochemical Society
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• v.24 no.4
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• pp.120-132
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• 2021

Although the development of high-Nickel is being actively carried out to solve the capacity limitation and the high price of raw cobalt due to the limitation of high voltage use of the existing LiCoO₂, the deterioration of the battery characteristics due to the decrease in structural stability and increase of the Ni content. It is an important cause of delaying commercialization. Therefore, in order to increase the high stability of the Ni-rich ternary cathod material LiNi_0.6Co_0.2Mn_0.2O₂, precursor Ni_0.6Co_0.2Mn_0.2-x(OH)₂/xTiO₂ was prepared using a nanosized TiO₂ suspension type source for uniform Ti substitution in the precursor. It was mixed with Li₂CO₃, and after heating, the cathode active material LiNi_0.6Co_0.2Mn_0.2-xTi_xO₂ was synthesized, and the physical properties according to the Ti content were compared. Through FE-SEM and EDS mapping analysis, it was confirmed that a positive electrode active material having a uniform particle size was prepared through Ti-substituted spherical precursor and Particle Size Analyzer and internal density and strength were increased, XRD structure analysis and ICP-MS quantitative analysis confirmed that the capacity was effectively maintained even when the Ti-substituted positive electrode active material was manufactured and charging and discharging were continued at high temperature and high voltage.

• Economic and Environmental Geology
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• v.55 no.2
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• pp.209-217
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• 2022

The purpose of this study was to confirm the dissolution of arsenic from the stabilized soil around abandoned coal mines by cultivation activities. Experimental soils were collected from the agricultural field around Okdong and Buguk coal mines, and the concentration of arsenic in the soil and the geochemical mobility were confirmed. The average arsenic concentration was 20 mg/kg. The soil with relatively high geochemical mobility of arsenic in the soil was used in the batch and column experiment. The limestone was mixed with soil for soil stabilization, and the mixing ratio was 3% of limestone, based on the soil weight. The phosphoric acid fertilizer (NH₄H₂PO₄) was added to the soil to simulate a cultivation condition according to the Rural Development Administration's rules. Comparative soil without mixing limestone was prepared and used as a control group. The arsenic extraction from soil was increased following the fertilizer mixing amount and it shows a positive relationship. The concentration of phosphate in the supernatant was relatively low under the condition of mixing limestone, which is determined to be result of binding precipitation of phosphate ions and calcium ions dissolved in limestone. Columns were set to mix phosphoric acid fertilizers and limestone corresponding to cultivation and stabilization conditions, and then the column test was conducted. The variations of arsenic extraction from the soil indicated that the stabilization was effectible until 10 P.V.; however, the stabilization effect of limestone decreased with time. Moreover, the geochemical mobility of arsenic has transformed by increasing the mobile fractions in soil compared to initial soil. Therefore, based on the arsenic extraction results, the cultivation activities using phosphoric fertilizer could induce a decrease in the stabilization effect.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.179-188
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• 2022

Global warming causes climate change and increases extreme weather events worldwide, and the occurrence of heatwaves and droughts is also increasing in Korea. For the monitoring of extreme weather, various satellite data such as LST (Land Surface Temperature), TCI (Temperature Condition Index), NDVI (Normalized Difference Vegetation Index), VCI (Vegetation Condition Index), and VHI (Vegetation Health Index) have been used. VHI, the combination of TCI and VCI, represents the vegetation stress affected by meteorological factors like precipitation and temperature and is frequently used to assess droughts under climate change. TCI and VCI require historical reference values for the LST and NDVI for each date and location. So, it is complicated to produce the VHI from the recent satellite GK2A (Geostationary Korea Multi-Purpose Satellite-2A). This study examined the retrieval of VHI using GK2A AMI (Advanced Meteorological Imager) by referencing the historical data from VIIRS (Visible Infrared Imaging Radiometer Suite) NDVI and LST as a proxy data. We found a close relationship between GK2A and VIIRS data needed for the retrieval of VHI. We produced the TCI, VCI, and VHI for GK2A during 2020-2021 at intervals of 8 days and carried out the interpretations of recent extreme weather events in Korea. GK2A VHI could express the changes in vegetation stress in 2020 due to various extreme weather events such as heatwaves (in March and June) and low temperatures (in April and July), and heavy rainfall (in August), while NOAA (National Oceanic and Atmospheric Administration) VHI could not well represent such characteristics. The GK2A VHI presented in this study can be utilized to monitor the vegetation stress due to heatwaves and droughts if the historical reference values of LST and NDVI can be adjusted in a more statistically significant way in the future work.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.4
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• pp.223-233
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• 2021

The objective of this study was to investigate the effect of gypsum on the dry matter yield and the chemical composition of alfalfa in reclaimed tideland with soil dressing. The experimental site was Sukmoon reclaimed tideland. The tideland was reclaimed approximately 17 to 33 years ago and the 70 cm of soil was top-dressed. The soil that covers the reclaimed tideland brought from the island did not treat di-salinized. Treatments were consisted of three groups; control group where no gypsum (G0) was applied and two experimental groups where 2 ton/ha (G2) and 4 ton/ha (G4) of gypsum were applied, respectively. The first harvest was conducted when the alfalfa reached early flowering (open the flower 10%), and after that subsequent harvest was conducted at approximately 35 days intervals. The dry matter yield of the alfalfa showed that G2 was significantly higher in the first year than G0 and G4, and G2 tended to be higher in the second year than G0 and G4, although there were no significant differences between treatments. The reason for the high dry matter yield in G2 was that the soil pH and EC of the soil were at marginal and ideal levels and the coverage and alfalfa botanical composition were also high. In both years, there were no differences in the crude protein, neutral detergent fiber and acid detergent fiber contents and relative feed value between gypsum treatments. Meanwhile, the results in the first and second years showed that the alfalfa dry matter yield were negatively affected by droughts stress in spring and concentrated precipitation in summer. Therefore, this study suggests gypsum treatment in reclaimed tidal land could increase the dry matter yield of alfalfa, and 2 ton/ha of gypsum was the optimum rate.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.761-774
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• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.

• Journal of Ginseng Culture
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• v.4
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• pp.128-141
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• 2022

Baengnyeongdo Island, which belongs to Ongjin-gun, Incheon, is an island in the northernmost part of the West Sea in South Korea. Baengnyeong Island is the 15th largest island in Korea and covers an area of 51 km². The Korea Ginseng Corporation (KGC) investigated the possibility of growing ginseng on Baengnyeong Island in 1996. In 1997, thanks to the support of cultivation costs from Ongjin-gun, the first ginseng seedbed was built on Baengnyeong Island. In 1999, the seedlings were transplanted to a permanent field under a contract with KGC. In 2003, the first six-year-old ginseng harvest was performed, and KGC purchased all production according to the contract. Since then, KGC has signed on to grow ginseng until 2012 and purchased six-year-old ginseng until the fall of 2016. Since 2014, the GimpoPaju Ginseng Agricultural Cooperative Association has signed a ginseng production contract. According to a survey of nine 6-year-old ginseng fields (total 5,961 units) on Baengnyeong Island, the top five with good growth had a survival rate of 42.6 to 68%, and the bottom four with poor growth had an extremely low survival rate of 11.1 to 21.3%. The four fields with low survival rates were where hot peppers were planted before ginseng cultivation. It is believed that the excess nitrogen remaining in the soil due to the treatment of compost or manure during pepper cultivation causes ginseng roots to rot. The average incidence of Alternaria blight was 8.6%. Six six-year-old ginseng gardens were low at 1.1 to 4.7%, while the other three were high at 16.7 to 20.9%. It is assumed that the reason for the low survival rate and high incidence of Alternaria blight is a rain-leaking shield. Farmers used rain-leaking shields because the precipitation on Baengnyeong Island was smaller than on land. One field showed 3% of leaves with yellowish brown spots, a symptom of physiological disturbance of the leaf, which is presumed to be due to the excessive presence of iron in the soil. To increase the production of ginseng on Baengnyeong Island, it is necessary to develop a suitable ginseng cultivation method for the island, such as strengthening the field management based on the results of a scientific study of soil, using rain-resistant shading, and installing drip irrigation facilities. I hope that ginseng will become a new driving force for the development of Baengnyeong Island, allowing ginseng products and food to thrive in the beautiful natural environment of the island.

• Research in Plant Disease
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• v.28 no.3
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• pp.113-121
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• 2022

Rice blast is a major plant disease that occurs worldwide and significantly reduces rice yields. Rice blast disease occurs periodically in Korea, causing significant socio-economic damage due to the unique status of rice as a major staple crop. A disease outbreak prediction system is required for preventing rice blast disease. Epidemiological investigations of disease outbreaks can aid in decision-making for plant disease management. Currently, plant disease prediction and epidemiological investigations are mainly based on quantitatively measurable, structured data such as crop growth and damage, weather, and other environmental factors. On the other hand, text data related to the occurrence of plant diseases are accumulated along with the structured data. However, epidemiological investigations using these unstructured data have not been conducted. The useful information extracted using unstructured data can be used for more effective plant disease management. This study analyzed news articles related to the rice blast disease through text mining to investigate the years and provinces where rice blast disease occurred most in Korea. Moreover, the average temperature, total precipitation, sunshine hours, and supplied rice varieties in the regions were also analyzed. Through these data, it was estimated that the primary causes of the nationwide outbreak in 2020 and the major outbreak in Jeonbuk region in 2021 were meteorological factors. These results obtained through text mining can be combined with deep learning technology to be used as a tool to investigate the epidemiology of rice blast disease in the future.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.931-939
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• 2022

In Korea, about two-thirds of the precipitation is concentrated in the summer season, so the problem of turbidity in the summer flood season varies from year to year. Concentrated rainfall due to abnormal rainfall and extreme weather is on the rise. The inflow of turbidity caused a sudden increase in turbidity in the water, causing a problem of turbidity in the dam reservoir. In particular, in Korea, where rivers and dam reservoirs are used for most of the annual average water consumption, if turbidity problems are prolonged, social and environmental problems such as agriculture, industry, and aquatic ecosystems in downstream areas will occur. In order to cope with such turbidity prediction, research on turbidity modeling is being actively conducted. Flow rate, water temperature, and SS data are required to model turbid water. To this end, the national measurement network measures turbidity by measuring SS in rivers and dam reservoirs, but there is a limitation in that the data resolution is low due to insufficient facilities. However, there is an unmeasured period depending on each dam and weather conditions. As a sensor for measuring turbidity, there are Optical Backscatter Sensor (OBS) and YSI, and a sensor for measuring SS uses equipment such as Laser In-Situ Scattering and Transmissometry (LISST). However, in the case of such a high-tech sensor, there is a limit due to the stability of the equipment. Therefore, there is an unmeasured period through analysis based on the acquired flow rate, water temperature, SS, and turbidity data, so it is necessary to develop a relational expression to calculate the SS used for the input data. In this study, the AEM3D model used in the Water Resources Corporation SURIAN system was used to improve the accuracy of prediction of turbidity through the turbidity-SS relationship developed based on the measurement data near the dam outlet.