• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.155-163
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• 2022

The optimization of long-range ensemble climate prediction for rice phenology model with advanced bias correction method is conducted. The daily long-range forecast(6-month) of mean/ minimum/maximum temperature and observation of January to October during 1991-2021 is collected for rice phenology prediction. In this study, the concept of "buffer period" is newly introduced to reduce the problem after bias correction by quantile mapping with constructing the transfer function by month, which evokes the discontinuity at the borders of each month. The four experiments with different lengths of buffer periods(5, 10, 15, 20 days) are implemented, and the best combinations of buffer periods are selected per month and variable. As a result, it is found that root mean square error(RMSE) of temperatures decreases in the range of 4.51 to 15.37%. Furthermore, this improvement of climatic variables quality is linked to the performance of the rice phenology model, thereby reducing RMSE in every rice phenology step at more than 75~100% of Automated Synoptic Observing System stations. Our results indicate the possibility and added values of interdisciplinary study between atmospheric and agriculture sciences.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.535-543
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• 2022

A drone is used to diagnose crop growth and to provide information through images in the agriculture field. In the case of using high spatial resolution drone images, growth information for each object can be produced. However, accurate object detection is required and adjacent objects should be efficiently classified. The purpose of this study is to develop a Chinese cabbage object detection algorithm using multispectral reflectance images observed from drone and computer vision techniques. Drone images were captured between 7 and 15 days after planting a Chinese cabbage from 2018 to 2020 years. The thresholds of object detection algorithm were set based on 2019 year, and the algorithm was evaluated based on images in 2018 and 2019 years. The vegetation area was classified using the characteristics of spectral reflectance. Then, morphology techniques such as dilatation, erosion, and image segmentation by considering the size of the object were applied to improve the object detection accuracy in the vegetation area. The precision of the developed object detection algorithm was over 95.19%, and the recall and accuracy were over 95.4% and 93.68%, respectively. The F1-Score of the algorithm was over 0.967 for 2 years. The location information about the center of the Chinese cabbage object extracted using the developed algorithm will be used as data to provide decision-making information during the growing season of crops.

• Communications of Mathematical Education
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• v.35 no.4
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• pp.527-546
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• 2021

The purpose of this study is to reveal what mathematics teachers focus on and how they assess students' thinking during lessons enacted. For this purpose, we googled and searched internet sites to collect formative assessment materials for the year 2014 to 2019. The formative assessment tasks data were analyzed according to the levels cognitive demand levels and tasks suggested in textbooks in terms of degrees to which how they are related. The data analysis suggested as follows: a) most of the formative assessment tasks were at the low-level, in particular, PNC level tasks that require applying particular procedures without connections to concepts and meaning underlying the procedures, b) the assessment tasks appeared to be very similar to the tasks suggested in the secondary mathematics textbooks, and c) it seemed that 3 types of formative assessment, observation notes, self-assessment, and peer-assessment were dominantly utilized during mathematics lessons and these different types of formative assessment were employed apparently to find out whether students participated actively in class and in group activity, not how they go through understanding or thinking processes.

• Journal of Family Resource Management and Policy Review
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• v.26 no.1
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• pp.43-58
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• 2022

In this study, I analyzed how the hours and frequency of housework change after older couples' retire, using data from the 1st(2007) to 7th(2018) years of the Women's Family Panel (KLoWF) conducted at the Korea Women's Policy Research Institute. In this study, couples where both the wife and the husband were 65 years of age or older were selected for the survey. A total of 2,482 people participated. The results as follows. First, as comparing between two time points, when the weekday housework hours of the wife and husband at t1 time point increased, the weekday housework hours of the wife and husband at t2 time point also increased. Second, on weekdays, when the wife's housework increased, the husband's housework also increased. On the other hand, on weekend, when the wife's housework increased, then the husband's housework decreased on weekdays.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.28 no.1
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• pp.15-21
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• 2022

The purpose of our study was to investigate the migration level of lead (Pb), cadmium (Cd), and barium (Ba) from glassware into a food simulant and to evaluate the exposure of each element. The test articles were glassware, including tableware, pots, and other containers. Pb, Cd, and Ba were analysed by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). The analytical performance of the method was validated in terms of its linearity, limit of detection (LOD), limit of quantification (LOQ), recovery, precision, and uncertainty. The monitoring was performed for 110 samples such as glass cups, containers, pots, and bottles. a food simulant. Migration test was conducted at 25? for 24 hours in a dark place using 4% acetic acid as a food simulant. Based on the data; exposure assessment was carried out to compare the estimated daily intake (EDI) to the human safety criteria. The risk levels of Pb and Ba determined in this study were approximately 1.9% and 0.3% of the provisional tolerable weekly intake (PTWI) and tolerable daily intake (TDI) value, respectively, thereby indicating a low exposure to the population.

• Korean Journal of Environmental Biology
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• v.40 no.1
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• pp.1-10
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• 2022

Global warming has a major impact on the Earth's precipitation and temperature fluctuations, and significantly affects the habitats and biodiversity of many species. Although the number of alien plants newly introduced in South Korea has recently increased due to the increasing frequency of international exchanges and climate change, studies on how climate change affects the distribution of these alien plants are lacking. This study predicts changes in the distribution of suitable habitats according to RCPs climate change scenarios using the current distribution of the invasive alien plant Conyza sumatrensis and bioclimatic variables. C. sumatrensis has a limited distribution in the southern part of South Korea. Isothermality (bio03), the max temperature of the warmest month (bio05), and the mean temperature of the driest quarter (bio09) were found to influence the distribution of C. sumatrensis. In the future, the suitable habitat for C. sumatrensis is projected to increase under RCP 4.5 and RCP 8.5 climate change scenarios. Changes in the distribution of alien plants can have a significant impact on the survival of native plants and cause ecosystem disturbance. Therefore, studies on changing distribution of invasive species according to climate change scenarios can provide useful information required to plan conservation strategies and restoration plans for various ecosystems.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.284-301
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• 2023

The changes in rice climatic yield potential (CYP) across the Korean Peninsula are evaluated based on the new climate change scenario produced by the National Institute of Agricultural Sciences with 18 ensemble members at 1 km resolution under a Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathways (RCP) emission scenarios. To overcome the data availability, we utilize solar radiation f or CYP instead of sunshine duration which is relatively uncommon in the climate prediction f ield. The result show that maximum CYP(CYPmax) decreased, and the optimal heading date is progressively delayed under warmer temperature conditions compared to the current climate. This trend is particularly pronounced in the SSP5-85 scenario, indicating faster warming, except for the northeastern mountainous regions of North Korea. This shows the benef its of lower emission scenarios and pursuing more efforts to limit greenhouse gas emissions. On the other hand, the CYPmax shows a wide range of feasible futures, which shows inherent uncertainties in f uture climate projections and the risks when analyzing a single model or a small number of model results, highlighting the importance of the ensemble approach. The f indings of this study on changes in rice productivity and uncertainties in temperature and solar radiation during the 21st century, based on climate change scenarios, hold value as f undamental information for climate change adaptation efforts.

• 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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.404-414
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• 2023

In this study, digital climate maps with high-resolution (1km, daily) for the period of 1981 to 2020 were produced for the use as reference data within the procedures for statistical downscaling of climate change scenarios. Grid data for the six climate variables including maximum temperature, minimum temperature, precipitation, wind speed, relative humidity, solar radiation was created over Korean Peninsula using statistical downscaling model, so-called IGISRM (Improved GIS-based Regression Model), using global reanalysis data and in-situ observation. The digital climate data reflects topographical effects well in terms of representing general behaviors of observation. In terms of Correlation Coefficient, Slope of scatter plot, and Normalized Root Mean Square Error, temperature-related variables showed satisfactory performance while the other variables showed relatively lower reproducibility performance. These digital climate maps based on observation will be used to downscale future climate change scenario data as well as to get the information of gridded agricultural weather data over the whole Korean Peninsula including North Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.53-62
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• 2024

In this study, we built a model to estimate frost occurrence in South Korea using single-input deep learning and multi-input deep learning. Meteorological factors used as learning data included minimum temperature, wind speed, relative humidity, cloud cover, and precipitation. As a result of statistical analysis for each factor on days when frost occurred and days when frost did not occur, significant differences were found. When evaluating the frost occurrence models based on single-input deep learning and multi-input deep learning model, the model using both GRU and MLP was highest accuracy at 0.8774 on average. As a result, it was found that frost occurrence model adopting multi-input deep learning improved performance more than using MLP, LSTM, GRU respectively.