기계학습모델을 이용한 RCP 8.5 이상기상 시나리오에 따른 사일리지용 옥수수생산량에 미치는 피해량 산정

초록

This study estimated whole crop maize (WCM; Zea mays L.) yield damage under abnormal climate conditions using amachine-learning approach based on Representative Concentration Pathway (RCP) 8.5 and visualized the results as spatial maps. Atotal of 3,232 WCM observations were compiled, and climate data were obtained from the Korea Meteorological Administration(KMA) Open Data Portal. The machine learning model used DeepCrossing. Dry matter yield (DMY) was predicted using theDeepCrossing model and climate data from the Automated Synoptic Observing System (ASOS; 95 stations). The calculation ofdamage was the difference between the DMYnormal and DMYabnormal. The normal climate was set as the 40-year of climate dataaccording to the year of WCM data (1978-2017). The level of abnormal climate by temperature and precipitation was set as RCP 8.5standard. The predicted DMYnormal ranged from 13,845-19,347 kg/ha. The damage from WCM varied by region and the severity ofabnormal climate, including abnormal temperature and precipitation. Under abnormal temperature conditions, damage in 2050 and 2100ranged from –243 to –133 and –1,797 to –245 kg/ha, respectively. Under abnormal precipitation conditions, damage in 2050 and 2100ranged from –2,998 to 1,447 and –11,308 to 29 kg/ha, respectively. Overall, DMY of WCM tended to increase with higher meanmonthly temperature. The damage calculated through the RCP 8.5 standard was presented as a spatial distribution using QGIS.Although this study used an RCP scenario based on greenhouse gas concentrations, further research is needed to apply an integratedShared Socioeconomic Pathway (SSP) that accounts for socioeconomic factors.

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