• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.41-50
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• 2024

Precipitation is an important component of the hydrological cycle and a key input parameter for many applications in hydrology, climatology, meteorology, and weather forecasting research. Grid-based satellite rainfall products with wide spatial coverage and easy accessibility are well recognized as a supplement to ground-based observations for various hydrological applications. The error properties of satellite rainfall products vary as a function of rainfall intensity, climate region, altitude, and land surface conditions. Therefore, this study aims to evaluate the commonly used new global grid-based satellite rainfall product, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), using data collected at different spatial and temporal scales. Additionally, in this study, grid-based CHIRPS satellite precipitation data were used to evaluate the 2022 extreme drought. CHIRPS provides high-resolution precipitation data at 5 km and offers reliable global data through the correction of ground-based observations. A frequency analysis was performed to determine the precipitation deficit in 2022. As a result of comparing droughts in 2015, 2017, and 2022, it was found that May 2022 had a drought frequency of more than 500 years. The 1-month SPI in May 2022 indicated a severe drought with an average value of -1.8, while the 3-month SPI showed a moderate drought with an average value of 0.6. The extreme drought experienced in South Korea in 2022 was evident in the 1-month SPI. Both CHIRPS precipitation data and observations from weather stations depicted similar trends. Based on these results, it is concluded that CHIRPS can be used as fundamental data for drought evaluation and monitoring in unmeasured areas of precipitation.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.338-343
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• 1998

This study is to select appropriate probability distributions for ten days evaporation data for the purpose of representing statistical characteristics of real evaporation data in Korea. Nine probability distribution functions were assumed to be underlying distributions for ten days evaporation data of 20 stations with the duration of 20 years. The parameter of each probability distribution function were estimated by the maximum likelihood approach, and appropriate probability distributions were selected from the goodness of fit test. Log Pearson type III model was selected as an appropriate probability distribution for ten days evaporation data in Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.368-375
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• 2023

In this study, we examined the error characteristic and bias correction method for one-month temperature forecast data produced through joint development between the Rural Development Administration and the H ong Kong University of Science and Technology. For this purpose, hindcast data from 2013 to 2021, weather observation data, and various environmental information were collected and error characteristics under various environmental conditions were analyzed. In the case of maximum and minimum temperatures, the higher the elevation and latitude, the larger the forecast error. On average, the RMSE of the forecast data corrected by the linear regression model and the XGBoost decreased by 0.203, 0.438 (maximum temperature) and 0.069, 0.390 (minimum temperature), respectively, compared to the uncorrected forecast data. Overall, XGBoost showed better error improvement than the linear regression model. Through this study, it was found that errors in prediction data are affected by topographical conditions, and that machine learning methods such as XGBoost can effectively improve errors by considering various environmental factors.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.73-79
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• 2019

Continuous and tremendous data (canopy temperature and meteorological variables) are necessary to determine Crop Water Stress Index (CWSI). This study investigated the optimal monitoring time and interval of canopy temperature and meteorological variables (air temperature, relative humidity, solar radiation and wind speed) to determine CWSIs. The Nash-Sutcliffe model efficiency coefficient (NSE) was used to quantitatively describe the accuracy of sampling method depending upon various time intervals (t=5, 10, 15, 20, 30 and 60 minutes) and CWSIs per every minute were used as a reference. The NSE coefficient of wind speed was 0.516 at the sampling time of 60 minutes, while the ones of other meteorological variables and canopy temperature were greater than 0.8. The pattern of daily CWSIs increased from 8:00 am, reached the maximum value at 12:00 pm, then decreased after 2:00 pm. The statistical analysis showed that the data collection at 11:40 am produced the closest CWSI value to the daily average of CWSI, which indicates that just one time of measurement could be representative throughout the day. Overall, the findings of this study contributes to the economical and convenient method of quantifying CWSIs and irrigation management.

• Korean Journal of Environmental Agriculture
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• v.37 no.4
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• pp.251-259
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• 2018

BACKGROUND: Various culture media have been used for hydroponic cultures of horticultural plants under the smart greenhouses with natural and artificial light types. Management of the culture medium for the control of medium amounts and/or necessary components absorbed by plants during the cultivation period is performed with ICT (Information and Communication Technology) and/or IoT (Internet of Things) in a smart farm system. This study was conducted to develop the cloud-based data analysis system for effective management of culture medium applying to hydroponic culture and plant growth in smart greenhouses. METHODS AND RESULTS: Conventional inorganic Yamazaki and organic media derived from agricultural byproducts such as a immature fruit, leaf, or stem were used for hydroponic culture media. Component changes of the solutions according to the growth stage were monitored and plant growth was observed. Red and green lettuce seedlings (Lactuca sativa L.) which developed 2~3 true leaves were considered as plant materials. The seedlings were hydroponically grown in the smart greenhouse with fluorescent and light-emitting diodes (LEDs) lights of $150{\mu}mol/m^2/s$ light intensity for 35 days. Growth data of the seedlings were classified and stored to develop the relational database in the virtual machine which was generated from an open stack cloud system on the base of growth parameter. Relation of the plant growth and nutrient absorption pattern of 9 inorganic components inside the media during the cultivation period was investigated. The stored data associated with component changes and growth parameters were visualized on the web through the web framework and Node JS. CONCLUSION: Time-series changes of inorganic components in the culture media were observed. The increases of the unfolded leaves or fresh weight of the seedlings were mainly dependent on the macroelements such as a $NO_3-N$, and affected by the different inorganic and organic media. Though the data analysis system was developed, actual measurement data were offered by using the user smart device, and analysis and comparison of the data were visualized graphically in time series based on the cloud database. Agricultural management in data visualization and/or plant growth can be implemented by the data analysis system under whole agricultural sites regardless of various culture environmental changes.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.2
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• pp.7-14
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• 2012

To analyze any given situation, it is necessary to have information on elements which affect the situation. Particularly, there is greater variability in both frequency and magnitude of agricultural product prices as they are affected by various unpredictable factors such as weather conditions etc. This is the reason why it is difficult for the farmers to maintain their stable income through agricultural production and marketing. In this research, attempts are made to quantify the entropy of various situations inherent in the price changes so that the stability of farmers' income can be increased. Through this research, we developed an entropy model which can quantify the uncertainties of price changes using the probability distribution of price changes. The model was tested for its significance by comparing its simulation outcomes with actual ranges and standard deviations of price variations of the past using monthly agricultural product prices data. We confirmed that the simulation results reflected the features of the ranges and standard deviations of actual price variations. Also, it is possible for us to predict standard deviations for changed prices which will occur after a certain time using the information entropy obtained from relevant agricultural product price data before the time.

• Journal of Korean Society of Rural Planning
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• v.19 no.1
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• pp.149-164
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• 2013

This article purposes to make a GIS database of South Korea rural village forest. So we first tried to collect data of their geographic coordinates or location from the many references on the rural village forest. As the result, we collected locations of the 634 forests. Boundaries of the 462 forests could be made by using their satellite imagery. Finally we implemented GIS database of the 462 traditional rural village forest in South Korea. Furthermore we surveyed 100 forests out of them. They were analyzed in the view of location, area, wood species, cultural assets and activities of inhabitants. These data can be used in the rural village planning and I look forward this database is helpful to preserve existing traditional rural village groves as a lasting legacy.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.3
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• pp.63-73
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• 2013

The objectives of this study were to develop a hydrologic simulation model to estimate surface drainage for irrigation districts consisting of paddy and protected cultivation, and to evaluate the applicability of the developed model. The model consists of three sub-models; agricultural supply, paddy block drainage, and protected cultivation runoff. The model simulates daily total drainage as the sum of paddy field drainage, irrigation canal drainage, and protected cultivation runoff at the outlets of the irrigation districts. The agricultural supply sub-model was formulated considering crop water requirement for growing seasons and agricultural water management loss. Agricultural supply was calculated for use as input data for the paddy block sub-model. The paddy block drainage sub-model simulates paddy field drainage based on water balance, and irrigation canal drainage as a fraction of agricultural supply. Protected cultivation runoff is calculated based on NRCS (Natural Resources Conservation Service) curve number method. The Idong reservoir irrigation district was selected for surface drainage monitoring and model verification. The parameters of model were calibrated using a trial and error technique, and validated with the measured data from the study site. The model can be a useful tool to estimate surface drainage for irrigated districts consisting of paddy and protected cultivation.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.6
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• pp.1-9
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• 2022

The study aims to determine the factors and their influence on the income from using public agricultural land of households. Public agricultural land is agricultural land, including land for growing annual crops, perennial crops, and land for aquaculture, leased by commune-level People's Committees with a lease term of not more than 5 years. Secondary data were collected for the 2017-2021 period at state agencies. Primary data were collected from a survey of 150 households renting public agricultural land. The regression model assumed that there were 28 factors belonging to 7 groups. The test results show that 25 factors affect income, and 03 factors do not. The group of COVID-19 pandemic factors has the strongest impact, followed by the groups of agricultural product market factors, land factors, capital factors, production cost factors, labor factors, and climatic factors. The impact rate of COVID-19 pandemic factors is the largest (23.00%); The impact rate of climatic factors is the smallest (6.04%). Proposals to increase income include good implementation of disease prevention and control; increasing the land lease term; accurately forecasting the supply and demand of the agricultural market; raising the level of the household head; ensuring sufficient production capital, and adapting to the climate.

• Genomics & Informatics
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• v.19 no.3
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• pp.23.1-23.7
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• 2021

Currently, coronavirus disease 2019 (COVID-19) literature has been increasing dramatically, and the increased text amount make it possible to perform large scale text mining and knowledge discovery. Therefore, curation of these texts becomes a crucial issue for Bio-medical Natural Language Processing (BioNLP) community, so as to retrieve the important information about the mechanism of COVID-19. PubAnnotation is an aligned annotation system which provides an efficient platform for biological curators to upload their annotations or merge other external annotations. Inspired by the integration among multiple useful COVID-19 annotations, we merged three annotations resources to LitCovid data set, and constructed a cross-annotated corpus, LitCovid-AGAC. This corpus consists of 12 labels including Mutation, Species, Gene, Disease from PubTator, GO, CHEBI from OGER, Var, MPA, CPA, NegReg, PosReg, Reg from AGAC, upon 50,018 COVID-19 abstracts in LitCovid. Contain sufficient abundant information being possible to unveil the hidden knowledge in the pathological mechanism of COVID-19.