• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.1
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• pp.17-28
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• 2007

This study was conducted to analyze the optimum heading period according to the recent climatic change for improvement of rice yield and grain quality in the Yeongnam area. We analyzed climatic elements including daily mean air temperature, daily range of air temperature, sunshine hours, and amount of precipitation from 1996 to 2005 in comparison with those of the 1971 to 2000 normal. Daily mean air temperature and amount of precipitation in the recent 10 years increased, but daily range of air temperature and sunshine hours decreased in comparison with the norm. Also, monthly mean air temperature was lowered remarkably in July and August. The monthly amount of precipitation largely increased in August and September. The daily range of air temperature and sunshine hours were greatly decreased from August to October, Possible cultivation periods for rice in the recent 10 years ranged from 171 days in Boughwa to 228 days in Busan and was expanded about $1{\sim}13$ days in comparison with the normal. Optimum heading date by local regions for the maximum climatic yield potential was estimated as July 31 at Bonghwa to September 7 at Busan, Masan, and Tongyeong in the recent 10 years. There was a wide difference in optimum heading date according to local legions of the Yeongnam area. Compared to the normal, optimum heading date in the recent 10 years was delayed about I~8 days in most local regions except Bonghwa, Mungyeong, and Yeongdeok. These results suggested that it is necessary to develop late maturity rice cultivars for producing high yield and quality rice grain due to the recent climatic change. Moreover, it is still more important to select the most suitable cultivation period appropriate to the changed climate of each local region in Yeongnam area.

• Korean Journal of Plant Resources
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• v.30 no.1
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• pp.38-49
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• 2017

The objective of this research was to develop Near-Infrared Reflectance Spectroscopy (NIRS) model for amylose and protein contents analysis of large accessions of rice germplasm. A total of 511 accessions of rice germplasm were obtained from National Agrobiodiversity Center to make calibration equation. The accessions were measured by NIRS for both brown and milled brown rice which was additionally assayed by iodine and Kjeldahl method for amylose and crude protein contents. The range of amylose and protein content in milled brown rice were 6.15-32.25% and 4.72-14.81%, respectively. The correlation coefficient ($R^2$), standard error of calibration (SEC) and slope of brown rice were 0.906, 1.741, 0.995 in amylose and 0.941, 0.276, 1.011 in protein, respectively, whereas $R^2$, SEC and slope of milled brown rice values were 0.956, 1.159, 1.001 in amylose and 0.982, 0.164, 1.003 in protein, respectively. Validation results of this NIRS equation showed a high coefficient determination in prediction for amylose (0.962) and protein (0.986), and also low standard error in prediction (SEP) for amylose (2.349) and protein (0.415). These results suggest that NIRS equation model should be practically applied for determination of amylose and crude protein contents in large accessions of rice germplasm.

• Korean Journal of Soil Science and Fertilizer
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• v.40 no.1
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• pp.25-30
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• 2007

Objective of this experiment was to investigate the growth effects of Chinese cabbage and soil salinity to alternative irrigation waters for drought periods. The treatments were consisted of the discharge water from industrial wastewater treatment plant (DIWT), the discharge water from municipal wastewater treatment plant (DMWT) and ground water as the control. For the chemical compositions of alternative water, it appeared that concentrations of the $Ni^+$ and SAR values in DIWT were over the reuse criteria of other countries for irrigation, but CODcr concentration in DMWT was higher than the reuse criteria for agricultural irrigation. According to classification of water by $EC_i$ value, DIWT and DMWT are ranged from 0.7 to $2.0dS\;m^{-1}$, slight salinity. Average harvest indexes were 0.64 for DIWT and 0.63 for DMWT as compared to 0.61 of the control regardless of irrigation periods. SAR value in soil was increased with prolonging the irrigation periods at head forming stage, but not much difference except for 30 days of irrigation period at harvesting time for DIWT. However, it was not much difference along with irrigation periods through the growth stages for DMWT as compared with the groundwater. At harvesting time, average $EC_e$ for the soil irrigated with alternative agricultural waters was $0.017dS\;m^{-1}$ for its DIMT and $0.036dS\;m^{-1}$ for its DMWT as compared to $0.013dS\;m^{-1}$ of its groundwater as the control. For $NH_4-N$ concentrations, it observed that there were no differences among the treatments with different irrigation periods at head forming stage in soil after irrigation. Also, $NO_3-N$ concentration in soil was increased up to 20 days after irrigation, and then decreased at 30 days after irrigation with DMWT at head forming stage. The $Ni^+$ concentration in upper layer soil (0-15 cm) irrigated with DIWT was increased with prolonging the irrigation period at head forming stage, but it was dramatically decreased and almost constant in all the treatments at harvesting time. Therefore, it might be concluded that there was potentially safe to irrigate the discharge water from municipal wastewater treatment plant for 20 days after transplanting to drought periods with cultivating the Chinese cabbage.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• Economic and Environmental Geology
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• v.56 no.2
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• pp.139-153
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• 2023

This study aimed to understand the spatiotemporal variations in nitrogen content in the Gyeongan stream along the main stream and at the discharge points of the sub-basins, and to identify the origin of the nitrogen. Field surveys and laboratory analyses, including chemical compositions and isotope ratios of nitrate and boron, were performed from November 2021 to November 2022. Based on the flow duration curve (FDC) derived for the Gyeongan stream, the dry season (mid-December 2021 to mid-June 2022) and wet season (mid-June to early November 2022) were established. In the dry season, most samples had the highest total nitrogen(T-N) concentrations, specifically in January and February, and the concentrations continued to decrease until May and June. However, after the flood season from July to September, the uppermost subbasin points (Group 1: MS-0, OS-0, GS-0) where T-N concentrations continually decreased were separated from the main stream and lower sub-basin points (Group 2: MS-1~8, OS-1, GS-1) where concentrations increased. Along the main stream, the T-N concentration showed an increasing trend from the upper to the lower reaches. However, it was affected by those of the Osan-cheon and Gonjiamcheon, the tributaries that flow into the main stream, resulting in respective increases or decreases in T-N concentration in the main stream. The nitrate and boron isotope ratios indicated that the nitrogen in all samples originated from manure. Mechanisms for nitrogen inflow from manure-related sources to the stream were suggested, including (1) manure from livestock wastes and rainfall runoff, (2) inflow through the discharge of wastewater treatment plants, and (3) inflow through the groundwater discharge (baseflow) of accumulated nitrogen during agricultural activities. Ultimately, water quality management of the Gyeongan stream basin requires pollution source management at the sub-basin level, including its tributaries, from a regional context. To manage the pollution load effectively, it is necessary to separate the hydrological components of the stream discharge and establish a monitoring system to track the flow and water quality of each component.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.337-345
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• 2023

Estimating the rice heading date is one of the most crucial agricultural tasks related to productivity. However, due to abnormal climates around the world, it is becoming increasingly challenging to estimate the rice heading date. Therefore, a more objective classification method for estimating the rice heading date is needed than the existing methods. This study, we aimed to classify the rice heading stage from various images using a CNN classification model. We collected top-view images taken from a drone and a phenotyping tower, as well as slanted-view images captured with a RGB camera. The collected images underwent preprocessing to prepare them as input data for the CNN model. The CNN architectures employed were ResNet50, InceptionV3, and VGG19, which are commonly used in image classification models. The accuracy of the models all showed an accuracy of 0.98 or higher regardless of each architecture and type of image. We also used Grad-CAM to visually check which features of the image the model looked at and classified. Then verified our model accurately measure the rice heading date in paddy fields. The rice heading date was estimated to be approximately one day apart on average in the four paddy fields. This method suggests that the water head can be estimated automatically and quantitatively when estimating the rice heading date from various paddy field monitoring images.

• Journal of Korea Water Resources Association
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• v.57 no.8
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• pp.509-518
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• 2024

Flash drought is a rapid-onset drought that occurs rapidly over a short period due to abrupt changes in meteorological and environmental factors. In this study, we utilized satellite-based soil moisture product from the Advanced Microwave Scanning Radiometer-2(AMSR2) ascending X-band to calculate the weekly Flash Drought Intensity Index (FDII). We also analyzed the characteristics of flash droughts on the Korean Peninsula over a 10-year period from 2013 to 2022. The analysis of monthly spatial distribution patterns of the irrigation period across the Korean Peninsula revealed significant variations. In North Korea (NK), a substantial increase in the rate of intensification (FD_INT) was observed due to the rapid depletion of soil moisture, whereas South Korea (SK) experienced a significant increase in drought severity (DRO_SEV). Additionally, regional time series analysis revealed that both FD_INT and DRO_SEV were significantly high in the Gangwon province of both NK and SK. The estimation of probability density by region revealed a clear difference in FD_INT between NK and SK, with SK showing a higher probability of severe drought occurrence primarily due to the high values of DRO_SEV. As a result, it is inferred that the occurrence frequency and damage of flash droughts in NK are higher than those in SK, as indicated by the higher density of large FDII values in the NK region. We analyzed the correlation between DRO_SEV and the Evaporative Stress Index (ESI) across the Korean Peninsula and confirmed a positive correlation ranging from 0.4 to 0.6. It is concluded that analyzing overall drought conditions through the average drought severity holds high utility. These findings are expected to contribute to understanding the characteristics of flash droughts on the Korean Peninsula and formulating post-event response plans.

• Korean Journal of Poultry Science
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• v.46 no.2
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• pp.65-75
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• 2019

A number of Korean native chicken(KNC) populations were registered in FAO (Food and Agriculture Organization) DAD-IS (Domestic Animal Diversity Information Systems, http://www.fao.org/dad-is). But there is a lack of scientific basis to prove that they are unique population of Korea. For this reason, this study was conducted to prove KNC's uniqueness using 25 Microsatellite markers. A total of 548 chickens from 11 KNC populations (KNG, KNB, KNR, KNW, KNY, KNO, HIC, HYD, HBC, JJC, LTC) and 7 introduced populations (ARA: Araucana, RRC and RRD: Rhode Island Red C and D, LGF and LGK: White Leghorn F and K, COS and COH: Cornish brown and Cornish black) were used. Allele size per locus was decided using GeneMapper Software (v 5.0). A total of 195 alleles were observed and the range was 3 to 14 per locus. The MNA, $H_{\exp}$, $H_{obs}$, PIC value within population were the highest in KNY (4.60, 0.627, 0.648, 0.563 respectively) and the lowest in HYD (1.84, 0.297, 0.286, 0.236 respectively). The results of genetic uniformity analysis suggested 15 cluster (${\Delta}K=66.22$). Excluding JJC, the others were grouped in certain cluster with high genetic uniformity. JJC was not grouped in certain cluster but grouped in cluster 2 (44.3%), cluster 3 (17.7%) and cluster8 (19.1%). As a results of this study, we can secure a scientific basis about KNC's uniqueness and these results can be use to basic data for the genetic evaluation and management of KNC breeds.