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).

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 discuss the optimal seeding and harvesting dates with growing degree days(GDD) via meta-data of whole crop maize(WCM). The raw data (n=3,152) contains cultivation year, cultivars, location, seeding and harvesting dates collected from various reports such as thesis, science journals and research reports (1982-2012). The processing was: recording, screening and modification of errors; Then, the final dataset (n=121) consists of seeding cases (n=29), and harvesting cases (n=92) which were used to detect the optimum. In addition, the optimal periods considering tolerance range and GDD also were estimated. As a result, the optimum seeding and harvesting periods were 14th April ~ 3rd May and 15th August ~ 4th September, respectively; where, their GDDs were 23.7~99.6℃ and 1,328.7~ 1,602.1℃, respectively. These GDDs could be used as a judge standard for selecting the seeding and harvesting dates.