• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.4
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• pp.414-421
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• 2019

Pod shattering during the maturing stage causes a serious yield loss in soybean. It is the main limiting factor of soybean cultivation and mechanization. It is important to develop varieties suitable for mechanical harvesting and to develop energy-efficient agricultural machinery to save labor and costs. 'Daewonkong,' developed by the National Institute of Crop Science (NICS) in 1997, is an elite cultivar that occupies more than 80% of the soybean cultivation area in Korea because of its strong tolerance to pod shattering. The objectives of this study were to investigate the variation in pod shattering degree in a RIL population developed from a 'Daewonkong' parent and to select promising lines with pod shattering tolerance. 'Daewonkong' demonstrated a high level of tolerance to pod shattering compared to the 'Tawonkong' and 'Saeolkong' varieties, with no shattered pods after 72 hours of drying. Screening of pod shattering showed a clear distinction between the tolerant and susceptible varieties. Also, the distribution of shattering pod ratio in the two populations showed a similar pattern for three years. The promising lines with pod shattering tolerance included 27 lines in the 'Daewonkong'×'Tawonkong' population and 21 lines in the 'Daewonkong'×'Saeolkong' population. The promising lines are expected to be widely used as breeding parents for creating soybean cultivars with pod shattering tolerance.

• Journal of Korean Society of Forest Science
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• v.111 no.3
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• pp.405-417
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• 2022

The purpose of this study was to describe the photosynthetic features of Prunus maximowiczii and Prunus serrulate Lindl. var. pubescens (Makino) Nakai in response to drought stress. Specifically, we studied the effects of drought on photosynthetic ability and photosystem II activity. Drought stress (DS) was induced by cutting the water supply for 30 days. DS decreased the moisture contents in the soil, and between the 10th and 12th days of DS, both species had 10% or less of x., After the 15th day of DS, it was less than 5%, which is a condition for disease to start. We observed a remarkable decrease of maximum photosynthesis rate starting from 10th day of DS; the light compensation point was also remarkable. Dark respiration and net apparent quantum yield decreased significantly on the 15th day of DS, and then increased on the 20th day. In addition, the stomatal transpiration rate of P. maximowiczii decreased significantly on the15th day of DS, and then increased on the 20th day. Water use efficiency increased on the 15th day of DS, and then decreased on the 20th day. The stomatal transpiration rate of P. serrulate decreased significantly on the 20th day of DS, and then increased afterward, while its water use efficiency increased on the 20th day of DS, and then decreased afterward. These results indicate that the closure of stoma prevented water loss, resulting in a temporary increase of water use efficiency. Chlorophyll fluorescence analysis detected remarkable decreases in the functional index (PI_ABS) and energy transfer efficiency in P. maximowiczii after the 15th day of DS. Meanwhile, photosystem II activity decreased in P. serrulate after 20 days of DS. In addition, Ts-Ta, PI_ABS, DI_O/RC, ET_O/RC followed similar trends as those of the soil moisture content and photosynthetic properties, indicating that they can be used as useful variables in predicting DS in trees.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.342-352
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• 2023

Ultra-violet (UV) light is one of abiotic stress factors and causes oxidative stress in plants, but a suitable level of UV radiation can be used to enhance the phytochemical content of plants. The accumulation of antioxidant phenolic compounds in UV-exposed plants may vary depending on the conditions of plant (species, cultivar, age, etc.) and UV (wavelength, energy, irradiation period, etc.). To date, however, little research has been conducted on how leaf thickness affects the pattern of phytochemical accumulation. In this study, we conducted an experiment to find out how the antioxidant phenolic content of kale (Brassica oleracea var. acephala) leaves with different thicknesses react to UV-A light. Kale seedlings were grown in a controlled growth chamber for four weeks under the following conditions: 20℃ temperature, 60% relative humidity, 12-hour photoperiod, light source (fluorescent lamp), and photosynthetic photon flux density of 121±10 µmol m^-2 s^-1. The kale plants were then transferred to two chambers with different CO₂ concentrations (382±3.2 and 1,027±11.7 µmol mol^-1), and grown for 10 days. After then, each group of kale plants were subjected to UV-A LED (275+285 nm at peak wavelength) light of 25.4 W m^-2 for 5 days. As a result, when kale plants with thickened leaves from treatment with high CO₂ were exposed to UV-A, they had lower UV sensitivity than thinner leaves. The Fv/Fm (maximum quantum yield on photosystem II) in the leaves of kale exposed to UV-A in a low-concentration CO₂ environment decreased abruptly and significantly immediately after UV treatment, but not in kale leaves exposed to UV-A in a high-concentration CO₂ environment. The accumulation pattern of total phenolic content, antioxidant capacity and individual phenolic compounds varied according to leaf thickness. In conclusion, this experiment suggests that the UV intensity should vary based on the leaf thickness (age etc.) during UV treatment for phytochemical enhancement.

• Magazine of the Korean Society of Agricultural Engineers
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• v.13 no.1
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• pp.2191-2205
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• 1971

In this research, Nong-rim No. 6 was adopted as a test variety of rice. Rice seedlings were transplanted on June 14, 1970. Roots were settled into soil on June 20 and a total number of days irrigated of $21cm{\times}21cm$ and an area of $9.9m^2$ for a test plot were accepted, planting 70 stumps of rice in a test plot. The soil in test plots are classified by soil test as oam, and its chemical contents are as shown in Table 3. Irrigation water was secured by pumping from the Sudun stream that originates at the Suho reservoir. Accordingly, the qualities of irrigation. water are considered to be the same as those of water stored in the Suho reservoir. There were 54 days of intermittent rainfalls in total during the whole 110-day period of irrigation. As a result, it is likely that the growth of rice plants was influenced by rainfall at a comparatively great degree. In order to measure the amounts of water consumption, infiltrometers, measuring devices for the decreases of water depths and lycimeters were provided. As a result of measurements, an average daily rate of infiltration was observed to be 14mm/day. It is expected from this research that the effect of increased yield will be secured by supplying optimum amounts of water for irrigation on proper times, and that the amounts of water consumption for irrigation can be saved by applying suitable irrigation methods. The test results obtained are summarized as follows: 1. Yields produced in the test plots of continuous irrigation are lower than those in the test plots of rotational irrigation, i.e., yields produced at the test plots irrigatied once in a period of 8 days are higher by 27% in average than those produced at test plots of continuous irrigation. 2. The amounts of irrigation water for test plots, which have a clay layer of 9cm in thickness and vynil diaphragm without holes, are saved by about 52% in comparison with ordinary test plots. 3. Ears are sprouted 5 days earlier at continuous irrigation plots as compared with other test plots. 4. It seems that there are growing stages of rice plants such as those of forming and sprouting of ears, in which the amounts of irrigation water are consumed more in comparison with the other stages. Therefore, it may be possible to increase of decrease the amount of irrigation water, according to the growing stage of rice plant, so as to save irrigation water.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.