This study estimated whole crop maize (WCM; Zea mays L.) yield damage under abnormal climate conditions using a machine-learning approach based on Representative Concentration Pathway (RCP) 8.5 and visualized the results as spatial maps. A total 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 the DeepCrossing model and climate data from the Automated Synoptic Observing System (ASOS; 95 stations). The calculation of damage was the difference between the DMYnormal 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 by temperature and precipitation was set as RCP 8.5 standard. The predicted DMYnormal ranged from 13,845-19,347 kg/ha. The damage from WCM varied by region and the severity of abnormal climate, including abnormal temperature and precipitation. Under abnormal temperature conditions, damage in 2050 and 2100 ranged from –243 to –133 and –1,797 to –245 kg/ha, respectively. Under abnormal precipitation conditions, damage in 2050 and 2100 ranged from –2,998 to 1,447 and –11,308 to 29 kg/ha, respectively. Overall, DMY of WCM tended to increase with higher mean monthly 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 integrated Shared Socioeconomic Pathway (SSP) that accounts for socioeconomic factors.

ABSTRACT
Ⅰ. 서론
Ⅱ. 재료 및 방법
    1. 데이터 수집 및 가공
    2. 수량예측모델 제작
    3. 이상기상 피해량 산정
    4. 이상기상 피해량 전자지도 제시
Ⅲ. 결과 및 고찰
    1. RCP 8.5 시나리오에 따른 WCM의 DMY 예측값
    2. RCP 8.5 시나리오에 따른 WCM의 피해량
    3. RCP 8.5 시나리오에 따른 WCM의 피해량 전자지도
Ⅳ. 요약
Ⅴ. 사사
Ⅵ. REFERENCES

• 김지융(경희대학교, 환경학 및 환경공학화) | Jiyung Kim (College of Engineering Department of Environmental Science and Engineering, Kyung Hee University, Yongin 17104, Republic of Korea)
• 성경일(강원대학교, 동물생명과학대학) | Kyung Il Sung (College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea)
• 최재성(강원대학교, 동물생명과학대학) | Jae Seong Choi (College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea)
• 조현욱(강원대학교, 동물생명과학대학) | Hyun Wook Jo (College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea)
• 김병완(강원대학교, 동물생명과학대학) | Byongwan Kim (College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea) Corresponding author
• 김문주(강원대학교, 동물자원공동연구소) | Moonju Kim (Institute of Animal Resources, Kangwon National University, Chuncheon 24341, Republic of Korea)