• Journal of The Korean Society of Grassland and Forage Science
• /
• v.42 no.2
• /
• pp.127-136
• /
• 2022

This study was conducted to estimate the damage of Whole Crop Maize (WCM) according to abnormal climate using machine learning and present the damage through mapping. The collected WCM data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. Deep Crossing is used for the machine learning model. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The damage was calculated by difference between the Dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of WCM data (1978~2017). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization(WMO) standard. The DMY_normal was ranged from 13,845~19,347 kg/ha. The damage of WCM was differed according to region and level of abnormal climate and ranged from -305 to 310, -54 to 89, and -610 to 813 kg/ha bnormal temperature, precipitation, and wind speed, respectively. The maximum damage was 310 kg/ha when the abnormal temperature was +2 level (+1.42 ℃), 89 kg/ha when the abnormal precipitation was -2 level (-0.12 mm) and 813 kg/ha when the abnormal wind speed was -2 level (-1.60 m/s). The damage calculated through the WMO method was presented as an mapping using QGIS. When calculating the damage of WCM due to abnormal climate, there was some blank area because there was no data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.8 no.2
• /
• pp.68-76
• /
• 2006

A thermal time-based two-step phenological model was used to project flowering dates of Japanese cherry in South Korea from 1941 to 2100. The model consists of two sequential periods: the rest period described by chilling requirement and the forcing period described by heating requirement. Daily maximum and minimum temperature are used to calculate daily chill units until a pre-determined chilling requirement for rest release is met. After the projected rest release date, daily heat units (growing degree days) are accumulated until a pre-determined heating requirement for flowering is achieved. Model calculations using daily temperature data at 18 synoptic stations during 1955-2004 were compared with the observed blooming dates and resulted in 3.9 days mean absolute error, 5.1 days root mean squared error, and a correlation coefficient of 0.86. Considering that the phonology observation has never been fully standardized in Korea, this result seems reasonable. Gridded data sets of daily maximum and minimum temperature with a 270 m grid spacing were prepared for the climatological years 1941-1970 and 1971-2000 from observations at 56 synoptic stations by using a spatial interpolation scheme for correcting urban heat island effect as well as elevation effect. A 25km-resolution temperature data set covering the Korean Peninsula, prepared by the Meteorological Research Institute of Korea Meteorological Administration under the condition of Inter-governmental Panel on Climate Change-Special Report on Emission Scenarios A2, was converted to 270 m gridded data for the climatological years 2011-2040, 2041-2070 and 2071-2100. The model was run by the gridded daily maximum and minimum temperature data sets, each representing a climatological normal year for 1941-1970, 1971-2000, 2011-2040, 2041-2070, and 2071-2100. According to the model calculation, the spatially averaged flowering date for the 1971-2000 normal is shorter than that for 1941-1970 by 5.2 days. Compared with the current normal (1971-2000), flowering of Japanese cherry is expected to be earlier by 9, 21, and 29 days in the future normal years 2011-2040, 2041-2070, and 2071-2100, respectively. Southern coastal areas might experience springs with incomplete or even no Japanese cherry flowering caused by insufficient chilling for breaking bud dormancy.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.43 no.1
• /
• pp.11-21
• /
• 2023

This study was conducted to estimate the damage of Whole Crop Corn (WCC; Zea Mays L.) according to abnormal climate using machine learning as the Representative Concentration Pathway (RCP) 4.5 and present the damage through mapping. The collected WCC data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. The machine learning model used DeepCrossing. The damage was calculated using climate data from the automated synoptic observing system (ASOS, 95 sites) by machine learning. The calculation of damage was the difference between the dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of WCC data (1978-2017). The level of abnormal climate by temperature and precipitation was set as RCP 4.5 standard. The DMY_normal ranged from 13,845-19,347 kg/ha. The damage of WCC which was differed depending on the region and level of abnormal climate where abnormal temperature and precipitation occurred. The damage of abnormal temperature in 2050 and 2100 ranged from -263 to 360 and -1,023 to 92 kg/ha, respectively. The damage of abnormal precipitation in 2050 and 2100 was ranged from -17 to 2 and -12 to 2 kg/ha, respectively. The maximum damage was 360 kg/ha that the abnormal temperature in 2050. As the average monthly temperature increases, the DMY of WCC tends to increase. The damage calculated through the RCP 4.5 standard was presented as a mapping using QGIS. Although this study applied the scenario in which greenhouse gas reduction was carried out, additional research needs to be conducted applying an RCP scenario in which greenhouse gas reduction is not performed.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.59 no.1
• /
• pp.71-79
• /
• 2017

Climate change phenomenon is posing a serious threat to sustainable corn production in Ghana. This study investigated the impacts of climate change on the rain-fed corn yield in the Dangme East district, Ghana by using Aquacrop model with a daily weather data set of 22-year from 1992 to 2013. Analysis of the weather data showed that the area is facing a warming trend as the numbers of years hotter and drier than the normal seemed to be increasing. Aquacrop model was assessed using the limited observed data to verify model's sufficiency, and showed credible results of $R^2$ and Nash-Sutcliffe efficiency (NSE). In order to simulate the corn yield response to climate variability four climate change scenarios were designed by varying long-term average temperature in the range of ${\pm}1^{\circ}C{\sim}{\pm}3^{\circ}C$ and average annual rainfall to ${\pm}5%{\sim}{\pm}30%$, respectively. Generally, the corn yield was negatively correlated to temperature rise and rainfall reduction. Rainfall variations showed more prominent impacts on the corn yield than that of temperature variations. The reduction in average rainfall would instantly limit the crop growth rate and the corn yield irrespective of the temperature variations.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.42 no.1
• /
• pp.1-9
• /
• 2022

This study was carried out to compare the DMY (dry matter yield) of IRG (Italian ryegrass) in the southern coastal regions of Korea due to seasonal climate scenarios such as the Kaul-Changma (late monsoon) in autumn, extreme winter cold, and drought in the next spring. The IRG data (n = 203) were collected from various Reports for Collaborative Research Program to Develop New Cultivars of Summer Crops in Jeju, 203 Namwon, and Yeungam from the Rural Development Administration - (en DASH). In order to define the seasonal climate scenarios, climate variables including temperature, humidity, wind, sunshine were used by collected from the Korean Meteorological Administration. The discriminant analysis based on 5% significance level was performed to distinguish normal and abnormal climate scenarios. Furthermore, the DMY comparison was simulated based on the information of sample distribution of IRG. As a result, in the southern coastal regions, only the impact of next spring drought on DMY of IRG was critical. Although the severe winter cold was clearly classified from the normal, there was no difference in DMY. Thus, the DMY comparison was simulated only for the next spring drought. Under the yield comparison simulation, DMY (kg/ha) in the normal and drought was 14,743.83 and 12,707.97 respectively. It implies that the expected damage caused by the spring drought was about 2,000 kg/ha. Furthermore, the predicted DMY of spring drought was wider and slower than that of normal, indicating on high variability. This study is meaningful in confirming the predictive DMY damage and its possibility by spring drought for IRG via statistical simulation considering seasonal climate scenarios.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.27 no.6
• /
• pp.701-707
• /
• 2021

In this study, we examined the sea surface temperature (SST), air temperature (AT), and their time lag in response to an extreme cold wave in 2018 and a weak cold wave in 2019, cross-correlating these to the northern wind direction frequency. The data used in this study include SST observations of seven ocean buoys Real-time Information System for Aquaculture Environment provided by the National Institute of Fisheries Science and automatic weather station AT near them recorded every hour; null data was interpolated. A finite impulse response filter was used to identify the appropriate data period. In the extreme cold wave in 2018, the seven locations indicated low SST caused by moving cold air through the northern wind direction. A warm cold wave in 2019, the locations showed that the AT data was similar to the normal AT data, but the SST data did not change notably. During the extreme cold wave of 2018, data showed a high correlation coefficient of about 0.7 and a time lag of about 14 hours between AT and SST; during the weak cold wave of 2019, the correlation coefficient was 0.44-0.67 and time lag about 20 hours between AT and SST. This research will contribute to rapid response to such climate phenomena while minimizing aquaculture damage.

• Journal of Integrative Natural Science
• /
• v.13 no.1
• /
• pp.27-33
• /
• 2020

The unsustainable human activities like increased use of automobiles, heavy industrialization and the use of large volumes of fertilizers, chemicals and pesticides in the agricultural land cause climate change problems in one way or another. Under normal circumstances, the heat radiations from the sun will be reflected back. An excessive volume of GHGs in the atmosphere would prevent these radiations from reflecting back. East Asia is facing severe climate change issues in recent times. A lot of climate change problems such as hurricanes and floods have been reported from this region in the last couple of decades. The study aimed at investigating the climate change in East Asia with changing Sea Surface Temperature (SST). The study adopted a quantitative research method with a case study research design where a deliberate focus was made on the East Asia Region. Secondary data was gathered and analyzed to yield both descriptive and inferential statistics. The study concluded that the impact of East Asia Climate variability was significant mainly for some extreme events. Also, the study concluded that there was a significant link between the change of the East Asia climate variability and that of the sea surface temperature. Further, the study concluded that a linear relationship existed between the sea surface temperature and the climate of East Asia. Hence, a linear regression was a significant predictor of the East Asia Climate (EAC) based on changing sea surface temperature. The model revealed that 37.4% of the variations in the climate change index were explained by the changes in the sea surface temperature. The climate was expected to change with a value of 49.48 for a unit change in the sea surface temperature.

• Journal of Climate Change Research
• /
• v.7 no.3
• /
• pp.315-324
• /
• 2016

The relevant ministries, including the Ministry of Environment in Korea, provided Post-2020 Long-term Mitigation Target and Implementation Plan. The plan consisted of four Business As Usual (BAU) reduction levels by 14.7%, 19.2%, 25.7%, and 31.3% until 2030. The Korean government finalized the mitigation target of 37%. But all the initial alternatives were below the goal, 30% from BAU, that has been promised to the international community as well as set out in the Framework Act on Low Carbon Green Growth. In order to achieve a specific goal, performance management should pursue "Justify doing the right things." Otherwise, performance management would not work properly. According to Kingdon's Policy Stream Framework, abnormal alternatives are difficult to be presented as scenarios because alternative building should focus on the role of the need to adhere to the basic principles and professionals. Such a result is possible only when the policy actors does not balance themselves. Performance management statistics has been analyzed by 6 years CCPI data since 2011, taking into account the impact after enactment. This study also has been complemented by a variety of sources, including the media, documents, and artifacts during the period. As a result, raising awareness about climate change was analyzed as one of the solutions because the climate change issue affects the normal performance management throughout the life of the people to stay linked to the environment.

• The Korean Fashion and Textile Research Journal
• /
• v.20 no.6
• /
• pp.744-751
• /
• 2018

This study aimed to provide the information on the thermal sensation and the amount of clothing worn of junior high school students in winter classroom the relation with their climate adaptability. Total usable questionnaires were obtained from 467 male and female students. The questionnaire included general characteristics, physical characteristics, self awareness of body shape, climate adaptability and subjective thermal sensation in winter classroom. The data were analyzed using SPSS Statistics 18.0 for frequency analysis, factor analysis, chi-square analysis, t-test and correlation analysis. The results were as follows. The average body type based on BMI was normal($20.1kg/m^2$ ). Females perceived their body type as thinner than males. They wore more (8.67 garment items compared to 8.14 for males). Only about 25% of students voted the thermal sensation to neutral(47% cool~very cold, 28% warm~very hot). Females were more sensitive to the cold, perceived less healthy, and wore more garments in the cold. Students felt colder in winter classroom when their cold adaptability was lower and they actively adjusted thermal insulation against the cold. It is recommended to suggest the guidelines for the proper indoor temperature and for the wear behavior in classroom in the perspectives of increasing the learning efficiency and improving the students' climate adaptability.

• Safety and Health at Work
• /
• v.11 no.4
• /
• pp.479-484
• /
• 2020

Background: The purpose of this study is to examine the combined effects of organizational climate (OC) with emotional labor (EL) on turnover intention in Korean firefighters. Methods: The data were obtained from the study Firefighters Research: Enhancement of Safety and Health. A total of 4,860 firefighters whose main duty was providing "emergency medical aid" were included. To examine the effects of OC on the relationships between five subscales of EL and turnover intention, four groups were created using various combinations of OC ("good" vs. "bad") and EL ("normal" vs. "risk"): (1) "good" and "normal" (Group I), (2) "bad" and "normal" (Group II), (3) "good" and "risk" (Group III), and (4) "bad" and "risk" (Group IV). Multivariate logistic regression analyses were performed to estimate the risk of turnover intention for the combinations of OC and EL. Results: The results showed turnover intention was significantly higher in the group with "bad" OC (17.7%) than in that with "good" OC (7.6%). Combined effects of OC and EL on turnover intention were found in all five subscales with the exception of Group I for emotional demands and regulation. Groups II, III, and IV were more likely to experience risks of turnover intention than Group I (p for trend <0.001). Conclusions: A positive and cooperative OC plays a role in decreasing the risk of turnover intention and in attenuating the negative effects of EL on turnover intention in firefighters.