한국초지조사료학회지 (Journal of The Korean Society of Grassland and Forage Science)

  • 제43권1호
  • /
  • Pages.11-21
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  • 2023
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  • 2287-5824(pISSN)
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  • 2287-5832(eISSN)
한국초지조사료학회 (The Korean Society of Grassland and Forage Science)
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기계학습모델을 이용한 이상기상에 따른 사일리지용 옥수수 생산량에 미치는 피해 산정

Calculation of Damage to Whole Crop Corn Yield by Abnormal Climate Using Machine Learning

  • 김지융 (강원대학교, 동물생명과학대학) ;
  • 최재성 (강원대학교, 동물생명과학대학) ;
  • 조현욱 (강원대학교, 동물생명과학대학) ;
  • 김문주 (강원대학교, 동물생명과학연구소) ;
  • 김병완 (강원대학교, 동물생명과학대학) ;
  • 성경일 (강원대학교, 동물생명과학대학)
  • Ji Yung Kim (College of Animal Life Sciences, Kangwon National University) ;
  • Jae Seong Choi (College of Animal Life Sciences, Kangwon National University) ;
  • Hyun Wook Jo (College of Animal Life Sciences, Kangwon National University) ;
  • Moonju Kim (Institute of Animal Life Sciences, Kangwon National University) ;
  • Byong Wan Kim (College of Animal Life Sciences, Kangwon National University) ;
  • Kyung Il Sung (College of Animal Life Sciences, Kangwon National University)
  • 투고 : 2022.12.24
  • 심사 : 2023.03.13
  • 발행 : 2023.03.31
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초록

본 연구는 기계학습을 기반으로 제작한 수량예측모델을 이용하여 PCR 4.5 시나리오에 따른 사일리지용 옥수수(WCC)의 피해량 산정 및 전자지도를 작성할 목적으로 수행하였다. WCC 데이터는 수입적응성 시험보고서(n=1,219), 국립축산과학원 시험연구보고서(n=1,294), 한국축산학회지(n=8), 한국초지조사료학회지(n=707) 및 학위논문(n=4)에서 총 3,232점을 수집하였으며, 기상데이터는 기상청의 기상자료개방포털에서 수집하였다. 본 연구에서 이상기상에 따른 WCC의 피해량은 RCP 4.5 시나리오에 따른 월평균기온 및 강수량을 시간단위로 환산하여 준용하여 산정하였다. 정상기상에서 DMY 예측값은 13,845~19,347 kg/ha 범위로 나타났다. 이상기상에 따른 피해량은 이상기온 2050 및 2100년 각각 -263~360 및-1,023~92 kg/ha, 이상강수량 2050 및 2100년 각각 -17~-2 및-12~2 kg/ha였다. 월평균기온이 증가함에 따라서 WCC의 DMY는 증가하는 경향으로 나타났다. RCP 4.5 시나리오를 통해 산정한 WCC의 피해량은 QGIS를 이용하여 전자지도로 제시하였다. 본 연구는 온실가스 저감이 진행된 시나리오를 이용했지만, 추가 연구는 온실가스 저감이 되지 않은 RCP 시나리오를 이용한 연구를 수행할 필요가 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.

키워드

과제정보

본 논문은 농촌진흥청 공동연구사업의 과제번호: PJ01499603의 지원에 의해 이루어졌습니다.

참고문헌

  1. Jo, H.W., Kim, M.K., Kim, J.Y., Jo, M.H., Kim, M.J., Lee, S.A., Kim, K.D., Kim, B.W. and Sung, K.I. 2021. Calculation of dry matter yield damage of whole crop maize in accordance with abnormal climate using machine learning model. Journal of the Korean Society of Grassland and Forage Science. 41(4):287-294. https://doi.org/10.5333/KGFS.2021.41.4.287
  2. Kim, J.Y., Choi, J.S., Jo, H.W., Kim, M.J., Kim, B.W. and Sung, K.I. 2022a. Damage of whole crop maize in abnormal climate using machine learning. Journal of the Korean Society of Grassland and Forage Science. 42(2):127-136. https://doi.org/10.5333/KGFS.2022.42.2.127
  3. Kim, M., Choi, J.S. and Sung, K.I. 2022b. Determination of the impacts of extreme weather affecting dry matter yield of Silage Maize (Zea mays L.) in Korea. Annals of Animal Resource Sciences. 33(4):140-150. https://doi.org/10.12718/AARS.2022.33.4.140
  4. Kim, M. and Sung, K.I. 2021. Impact of abnormal climate events on the production of Italian ryegrass as a season in Korea. Journal of Animal Science and Technology. 63(1):77-90. https://doi.org/10.5187/jast.2021.e9
  5. Kim, J.G., Li, Y.F., Wei, S.N., Jeong, E.C. and Kim, H.J. 2020. Comparison of the forage quality and productivity according to varieties and plant parts of imported silage corn (Zea mays L.). Journal of the Korean Society of Grassland and Forage Science. 40(2):98-105. https://doi.org/10.5333/KGFS.2020.40.2.98
  6. Kim, M.J., Befekadu, C. and Sung, K.I. 2019. Effect of heavy rainfall events on the dry matter yield trend of whole crop maize (Zea mays L.). Agriculture. 9(4):75-85. https://doi.org/10.3390/agriculture9040075
  7. KMA. KMA weather data service-Open MET data portal. Korea Meteorological Administration. URL: https://data.kma.go.kr/cmmn/main.do (Accessed on 2021. 07. 01).
  8. NIHHS. 2014. Climate change scenario-Future climate information for agriculture by cities and counties. Rural Development Administration-National Institute of Horticultural and Herbal Science, Wanju-gun, Republic of Korea.
  9. NIMR. 2011. Report on climate change scenario for responding to the IPCC 5th assessment report. National Institute of Meteorological Sciences. Juju-do. Republic of Korea. pp. 3-16.
  10. Park, K.H. and Chung, W.H. 2015. Protocol development and notification establishment for fact-finding, impact, and vulnerability assessment for climate change in agriculture. Rural Development Administration(RDA). Jeonju. Republic of Korea. pp. 92-106.
  11. Shim, K.M., Kim, Y.S., Jung, M.P., Kim, J.W., Park, M.S., Hong, S.H. and Kang, K.K. 2018. Recent change in the frequency of occurrence of extreme weather events in South Korea. Journal of Climate Change Research. 9(4):461-470.