This study was conducted to calculate the damage of Italian ryegrass (IRG) by abnormal climate using machine learning and present the damage through the map. The IRG data collected 1,384. The climate data was collected from the Korea Meteorological Administration Meteorological data open portal.The machine learning model called xDeepFM was used to detect IRG damage. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The calculation of damage was the difference between the Dry matter yield (DMY)normal and DMYabnormal. The normal climate was set as the 40-year of climate data according to the year of IRG data (1986~2020). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization (WMO) standard. The DMYnormal was ranged from 5,678 to 15,188 kg/ha. The damage of IRG differed according to region and level of abnormal climate with abnormal temperature, precipitation, and wind speed from -1,380 to 1,176, -3 to 2,465, and -830 to 962 kg/ha, respectively. The maximum damage was 1,176 kg/ha when the abnormal temperature was -2 level (+1.04℃), 2,465 kg/ha when the abnormal precipitation was all level and 962 kg/ha when the abnormal wind speed was -2 level (+1.60 ㎧). The damage calculated through the WMO method was presented as an map using QGIS. There was some blank area because there was no climate 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).

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 DMYabnormal. 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 DMYnormal 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.

The purpose of this study was to investigate the effects of root zone temperatures (RZT) on the germination of bell peppers and tomatoes. Bell peppers and tomatoes had the highest germination rates (85% and 90%, respectively) at 25oC air temperature. Besides, the first germination of bell peppers was shifted by one day ahead. Bell peppers had the highest germination rate of 72,100, and 100%, respectively, when the RZT was adjusted to 30oC at airtemperature of 20, 25, and 30oC, and when the air temperature was adjusted to 35oC, the germination rate was the highest (70%) when the RZT was 15oC. Tomatoes had the highest germination rate at 20oC of the RZT at all atmospheric temperatures. A local cooling and heating system was established to improve the germination rate by controlling the RZT during the low and high temperature period. The optimum RZT for seedlings during the low and high temperature period was investigated.

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 DMYabnormal. 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 DMYnormal 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 ㎧). 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).

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 (1993 – 2013). 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.

본 연구는 기계학습을 통한 수량예측모델을 이용하여 이상기상에 따른 WCM의 DMY 피해량을 산출하기 위한 목적으로 수행하였다. 수량예측모델은 WCM 데이터 및 기상 데이터를 수집 후 가공하여 8가지 기계학습을 통해 제작하였으며 실험지역은 경기도로 선정하였다. 수량예측모델은 기계학습 기법 중 정확성이 가장 높은 DeepCrossing (R2=0.5442, RMSE=0.1769) 기법을 통해 제작하였다. 피해량은 정상기상 및 이상기상의 DMY 예측값 간 차이로 산출하였다. 정상기상에서 WCM의 DMY 예측값은 지역에 따라 차이가 있으나 15,003~17,517 kg/ha 범위로 나타났다. 이상기온, 이상강수량 및 이상풍속에서 WCM의 DMY 예측 값은 지역 및 각 이상기상 수준에 따라 차이가 있었으며 각각 14,947~17,571 kg/ha, 14,986~17,525 kg/ha 및 14,920~17,557 kg/ha 범위로 나타났다. 이상기온, 이상강수량 및 이상풍속에서 WCM의 피해량은 각각 –68~89 kg/ha, -17~17 kg/ha 및 – 112~121 kg/ha 범위로 피해로 판단할 수 없는 수준이었다. WCM의 정확한 피해량을 산출하기 위해서는 수량예측모델에 이용하는 이상기상 데이터 수의 증가가 필요하다.

PURPOSES : This study was conducted to prevent road thinning ice caused by abnormal weather conditions. METHODS : The appropriate amount of de-icer spread rate was verified by presenting the appropriate amount of snow removal agent spraying criteria for the thickness of the water film, owing to abnormal weather phenomena (fog, frost), and applying the standards to the site. Furthermore, we present a method to utilize residual salt, by quantifying the surface state changes according to the amount of deicer. RESULTS : Precautionary spread experiments to prevent road thin ice caused by abnormal weather conditions, indicated no freezing from 7.6g/m2 at 2℃-4℃ but 11.1g/m2 was suggested as a step higher considering external environmental variables. The amount of spraying was presented in two sections of rainfall(freezing rain). It is 17.7g/m2 at 0-7℃, 33.3g/m2 at -7~ -15℃, and 44.4g/m2 and 51.1g/m2 at non-urban, respectively. CONCLUSIONS : The criteria were divided into air temperature and road temperature standards, so that they could be distributed according to the temperature standards that meet the conditions, and the criteria presented were confirmed to be effective in preventing road thinning ice. If the road manager adopts Safety Line, which is suggested by utilizing the amount of residual salt on the road, it is believed that it can help determine the additional deicer.

지구 온난화의 영향으로 우리나라는 지난 30년동안 평균기온이 0.7℃, 겨울철에는 1.4℃가 상승하였다. 이러한 온난화로 인하여 우리나라에서는 이상기상 현상이 자주 발생하여 채소작물에 피해가 발생한다. 특히 노지에서 많이 재배되고 있는 고추, 배추 및 무는 온난화로 인하여 정식시기를 점점 앞당겨 정식후 갑작스런 저온이 오면 이들 작물의 피해가 크다. 따라서 본 실험은 저온에 따른 배추의 생육특성과 엽 세포조직에 미치는 영향을 구명하고자 실시하였다. '춘광' 배추품종을 화분에 정식한 후 노지 처리구, 무가온 하우스 및 가온하우스 처리구 등 3처리를 하였다. 그 결과, 정식후 50일의 생육은 노지처리구의 초장, 엽수, 엽록소 및 엽면적이 가온 처리구에 비해서 현저하게 떨어졌고, 특히 생체중의 경우에는 가온 처리구에 비해서 노지와 무가온 하우스 처리구가 1/3 수준으로 현저하게 낮았다. 배추의 잎이 10매 정도 생육이 되었을 때 저온에 따른 배추 잎의 피해증상은 영하 3.0℃ 조건에서는 배추 겉잎에 약간의 수침증상을 보였으나 회복되었다. 그러나 영하 7.4℃ 조건의 배추 잎은 수침증상이 심하였으며 회복되지 못하고 황색으로 변하면서 결국 잎이 고사하였다. 피해받은 잎의 엽육세포는 영하 3.0℃ 조건에서는 울타리조직과 해면조직이 약한 붕괴증상을 보였지만 어느정도 형태를 갖추고 있었는데, 영하 7.4℃ 조건에서는 세포가 동결된 후 해동되는 과정에서 세포의 막구조가 파괴되어 울타리조직과 해면조직이 완전히 붕괴되었기 때문에 세포 형태를 갖추고 있지 않았다. 따라서 배추 정식후 초기 생육 단계에서 영하 3℃까지는 비닐이나 부직포로 보온, 토양수분 조절, ABA 처리를 하여 동해를 예방할 수 있으나 영하 7℃의 저온이 발생하면 세포가 파괴되어 회복하기 어렵기 때문에 다시 심거나 또는 다른 작물로 대체하는 것이 좋을 것으로 사료되었다.

This study tries to reveal abnormal trends in climate change from 60 stations in Korea during 1981-2010 by comparisons to the standard station, Chupungnyeong station. Trends in climate change from station with the abnormalities, and their implication and causes are also discussed. Although Wando, Wonju, Mungyeong and Mokpo stations show the most abnormalities, normal trends in climate change from some climate data are also found from Mokpo station. On the other hand, some climate data from Suwon, Jeonju, Jinju, Icheon and Geumsan stations indicate the most normalities. It should be noted that variabilities of climate data are largely different, indicating that clear trends in climate change may not be extracted. The fact that some stations with the abnormalities from some climate data also show the normalities should be also noted. This study suggests that most stations with the most abnormalities may be relevant to relocation of station.

The purpose of this article is analyzing the economic impacts of abnormal climate on total revenue of red pepper in Korea, with employing the equilibrium displacement model. Our simulation results show the rate of yield change, price change, and total revenue change according to the climate change scenarios. In th case of by RCP 8.5 Scenario, red pepper production volume would be expected to decrease by 77.2% compared to 2012 while price increasing by 29.6%. As a result, total revenue to be returned to farmers would be reduced by 47.6% than it was in 2012. In contrast, total revenue would be expected to decline by 29.6% according to RCP 4.5 scenario.

The purpose of this article is analyzing the economic impacts of abnormal climate on fall chinese cabbage farmers and consumers in Korea, with employing the equilibrium displacement model. Our results show that there were little difference in gross farm income, even though there were significant yield reductions due to abnormal climate changes. However periodic occurrences of abnormal climates caused serious damage to consumption levels which had declined by 10.6~17.1 percent with higher prices by 15.3~24.6 percent than normal climate years since 1990.