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 with the aim of confirming the impact and relative contribution of extreme weather to dry matter yield (DMY) of silage corn in the central inland region of Korea. The corn data (n=1,812) were obtained from various reports on the new variety of adaptability experiments conducted by the Rural Development Administration from 1978 to 2017. As for the weather variables, mean aerial temperature, accumulated precipitation, maximum wind speed, and sunshine duration, were collected from the Korean Meteorological Administration. The extreme weather was detected by the box plot, the DMY comparison was carried out by the t-test with a 5% significance level, and the relative contribution was estimated by R2 change in multiple regression modeling. The DMY of silage corn was reduced predominantly during the monsoon in summer and autumn, with DMY damage measuring 1,500-2,500 kg/ha and 1,800 kg/ha, respectively. Moreover, the relative contribution of the damage during the monsoons in summer and autumn was 40% and 60%, respectively. Therefore, the impact of autumn monsoon season should be taken into consideration when harvesting silage corn after late August. This study evaluated the effect of extreme weather on the yield damage of silage corn in Korea and estimated the relative contribution of this damage for the first time.

본 연구는 우리나라에서 수수-수단그라스 교잡종 (sorghum bicolor L.: SSH)에 대해 극단기상과 정상기상 간 생산량을 비교할 목적으로 수행하였다. SSH 데이터 (n=1,025)는 농촌진흥청의 신품종 적응성 실험보고서(1979 ―2019)로부터 수집하였다. 기상자료는 기상청으로부터 평균기온, 최저기온, 최고기온, 최대 강수량, 누적 강수량, 최대풍속, 평균풍속 및 일조시간을 10일 기준으로 계산하 여 수집하였다. 극단기상과 정상기상 간 구별을 위해 상 자 그림을 이용하여 탐색하였다. 극단기상과 정상기상 간 생산량 차이는 5% 유의수준 하에서 t-검정 및 ANOVA를 통해 확인하였다. 그 결과, 극단기상은 극단적으로 강한 바람을 동반한 봄 가뭄, 극단적으로 높은 강우량을 기록 하는 여름장마와 가을장마가 두드러졌다. 예측 생산량 피 해(kg/ha)는 각각 1,961―6,541, 2,161―4,526 및 508― 5,582로 나타났다. 본 연구는 우리나라의 SSH에 대한 취 약성 및 피해 산정에 도움이 되는 기초자료로서 극단기상 과 정상기상 사이의 생산량 차이를 확인하는 데 의의가 있다.

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.

This study aimed to confirm the importance ratio of climate and management variables on production of orchardgrass in Korea (1982―2014). For the climate, the mean temperature in January (MTJ, ℃), lowest temperature in January (LTJ, ℃), growing days 0 to 5 (GD 1, day), growing days 5 to 25 (GD 2, day), Summer depression days (SSD, day), rainfall days (RD, day), accumulated rainfall (AR, mm), and sunshine duration (SD, hr) were considered. For the management, the establishment period (EP, 0―6 years) and number of cutting (NC, 2nd―5th) were measured. The importance ratio on production of orchardgrass was estimated using the neural network model with the perceptron method. It was performed by SPSS 26.0 (IBM Corp., Chicago). As a result, EP was the most important variable (100%), followed by RD (82.0%), AR (79.1%), NC (69.2%), LTJ (66.2%), GD 2 (63.3%), GD 1 (61.6%), SD (58.1%), SSD (50.8%) and MTJ (41.8%). It implies that EP, RD, AR, and NC were more important than others. Since the annual rainfall in Korea is exceed the required amount for the growth and development of orchardgrass, the damage caused by heavy rainfall exceeding the appropriate level could be reduced through drainage management. It means that, when cultivating orchardgrass, factors that can be controlled were relatively important. Although it is difficult to interpret the specific effect of climates on production due to neural networking modeling, in the future, this study is expected to be useful in production prediction and damage estimation by climate change by selecting major factors.

This study aimed to investigate the impacts of extreme weather on the dry matter yield (DMY) of silage maize in South Korea. The maize data (n=3,041) were collected from various reports of the new variety of adaptability experiments by the Rural Development Administration (1978-2017). Eight weather variables were collected: mean temperature, low temperature, high temperature, maximum precipitation, accumulated precipitation, maximum wind speed, mean wind speed, and sunshine duration. These variables were calculated based on ten days within seeding to harvesting period. The box plot detected an outlier to distinguish extreme weather from normal weather. The difference in DMY between extreme and normal weather was determined using a t-test with a 5% significance level. As a result, outliers of high-extreme precipitation were observed in July and August. Low-extreme mean temperature was remarkable in middle May, middle June, and late July. Moreover, the difference in DMY between extreme and normal weather was greatest (5,597.76 kg/ha) during the maximum precipitation in early July. This indicates that the impact of heavy rainfall during the Korean monsoon season was fatal to the DMY of silage maize. However, in this study, the frequency of extreme weather was too low and should not be generalize. Thus, in the future, we plan to compare DMY with statistical simulations based on extreme distributions.

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.

This study aimed to analyze causality of climatic factors that affecting the yield of whole crop barley (WCB) by constructing a network within the natural ecosystem via the structural equation model. The WCB dataset (n=316) consisted of data on the forage information and climatic information. The forage information was collected from numerous experimental reports from New Cultivars of Winter Crops (1993-2012) and included details of fresh and dry matter yield, and the year and location of cultivation. The climatic information included details of the daily mean temperature, precipitation, and sunshine duration from the weather information system of the Korea Meteorological Administration. The variables were growing days, accumulated temperature, precipitation, and sunshine duration in the season for the period of seeding to harvesting. The data was collected over 3 consecutive seasons—autumn, winter, and the following spring. We created a causality network depicting the effect of climatic factors on production by structural equation modeling. The results highlight: (i) the differences in the longitudinal effects between autumn and next spring, (ii) the factors that directly affect WCB production, and (iii) the indirect effects by certain factors, via two or more paths. For instance, the indirect effect of precipitation on WCB production in the following spring season via its effect on temperature was remarkable. Based on absolute values, the importance of WCB production in decreasing order was: the following spring temperature (0.45), autumn temperature (0.35), wintering (-0.16), and following spring precipitation (0.04). Therefore, we conclude that other climatic factors indirectly affect production through the final pathway, temperature and growing days in the next spring, in the climate-production network for WCB including temperature, growing days, precipitation and sunshine duration.

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.

This study aimed to determine the trend in dry matter yield (DMY) of a new sorghum-sudangrass hybrid (SSH) in the central inland regions of Korea. The metadata (n=388) were collected from various reports of the experiments examining the adaptability of this new variety conducted by the Rural Development Administration (1988–2013). To determine the trend, the parameters of autoregressive (AR) and moving average (MA) were estimated from correlogram of Autocorrelation function (ACF) and partial ACF (PACF) using time series modeling. The results showed that the trend increased slightly year by year. Furthermore, ARIMA (1, 1, 0) was found to be the optimal model to describe the historical trend. This means that the trend in the DMY of the SSH was associated with changes over the past two years but not with changes from three years ago. Although climatic variables, such as temperature, precipitation, and sunshine were also considered as environmental factors for the annual trends, no clear association was observed between DMY and climates. Therefore, more precise processing and detailed definition of climate considering specific growth stages are required to validate this association. In particular, research on the impact of heavy rainfall and typhoons, which are expected to cause damage in the short term, on DMY trends is ongoing, and the model confirmed in this study is expected to play an important role in studying this aspect. Furthermore, we plan to add the environmental factors such as soil and cultivation management as well as climate to our future studies.

Yield prediction model for mixed pasture was developed with a shortage that the relationship between dry matter yield (DMY) and days of summer depression (DSD) was not properly reflected in the model in the previous research. Therefore, this study was designed to eliminate the data of the regions with distinctly different climatic conditions and then investigate their relationships DMY and DSD using the data in each region separately of regions with distinct climatic characteristics and classify the data based on regions for further analysis based on the previous mixed pasture prediction model. The data set used in the research kept 582 data points from 11 regions and 41 mixed pasture types. The relationship between DMY and DSD in each region were analyzed through scatter plot, correlation analysis and multiple regression analysis in each region separately. In the statistical analysis, DMY was taken as the response variable and 5 climatic variables including DSD were taken as explanatory variables. The results of scatter plot showed that negative correlations between DMY and DSD were observed in 7 out of 9 regions. Therefore, it was confirmed that analyzing the relationship between DMY and DSD based on each region is necessary and 5 regions were selected (Hwaseong, Suwon, Daejeon, Siheung and Gwangju) since the data size in these regions is large enough to perform the further statistical analysis based on large sample approximation theory. Correlation analysis showed that negative correlations were found between DMY and DSD in 3 (Hwaseong, Suwon and Siheung) out of the 5 regions, meanwhile the negative relationship in Hwaseong was confirmed through multiple regression analysis. Therefore, it was concluded that the interpretability of the yield prediction model for mixed pasture could be improved based on constructing the models using the data from each region separately instead of using the pooled data from different regions.

This study was aimed to find yield prediction model of Italian ryegrass using climate big data and geographic information. After that, mapping the predicted yield results using Geographic Information System (GIS) as follows; First, forage data were collected; second, the climate information, which was matched with forage data according to year and location, was gathered from the Korean Metrology Administration (KMA) as big data; third, the climate layers used for GIS were constructed; fourth, the yield prediction equation was estimated for the climate layers. Finally, the prediction model was evaluated in aspect of fitness and accuracy. As a result, the fitness of the model (R2) was between 27% to 95% in relation to cultivated locations. In Suwon (n=321), the model was; DMY = 158.63AGD –8.82AAT +169.09SGD - 8.03SAT +184.59SRD -13,352.24 (DMY: Dry Matter Yield, AGD: Autumnal Growing Days, SGD: Spring Growing Days, SAT: Spring Accumulated Temperature, SRD: Spring Rainfall Days). Furthermore, DMY was predicted as 9,790±120 (kg/ha) for the mean DMY(9,790 kg/ha). During mapping, the yield of inland areas were relatively greater than that of coastal areas except of Jeju Island, furthermore, northeastern areas, which was mountainous, had lain no cultivations due to weak cold tolerance. In this study, even though the yield prediction modeling and mapping were only performed in several particular locations limited to the data situation as a startup research in the Republic of Korea.