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

The Korean Society of Grassland and Forage Science (한국초지조사료학회)
권호 표지
DOI QR코드

DOI QR Code

Calculation of Dry Matter Yield Damage of Whole Crop Maize in Accordance with Abnormal Climate Using Machine Learning Model

기계학습 모델을 이용한 이상기상에 따른 사일리지용 옥수수 생산량 피해량

  • Jo, Hyun Wook (College of Animal Life Sciences, Kangwon National University) ;
  • Kim, Min Kyu (College of Information Technology, Kangwon National University) ;
  • Kim, Ji Yung (College of Animal Life Sciences, Kangwon National University) ;
  • Jo, Mu Hwan (College of Animal Life Sciences, Kangwon National University) ;
  • Kim, Moonju (Institute of Animal Life Science, Kangwon National University) ;
  • Lee, Su An (College of Engineering and Information Technology, Semyung University) ;
  • Kim, Kyeong Dae (Gangwondo Agricultural Research and Extension Services) ;
  • Kim, Byong Wan (College of Animal Life Sciences, Kangwon National University) ;
  • Sung, Kyung Il (College of Animal Life Sciences, Kangwon National University)
  • 조현욱 (강원대학교, 동물생명과학대학) ;
  • 김민규 (강원대학교, IT대학) ;
  • 김지융 (강원대학교, 동물생명과학대학) ;
  • 조무환 (강원대학교, 동물생명과학대학) ;
  • 김문주 (강원대학교, 동물생명과연구소) ;
  • 이수안 (세명대학교, IT엔지니어링대학) ;
  • 김경대 (강원도농업기술원) ;
  • 김병완 (강원대학교, 동물생명과학대학) ;
  • 성경일 (강원대학교, 동물생명과학대학)
  • Received : 2021.11.30
  • Accepted : 2021.12.15
  • Published : 2021.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

The objective of this study was conducted to calculate the damage of whole crop maize in accordance with abnormal climate using the forage yield prediction model through machine learning. The forage yield prediction model was developed through 8 machine learning by processing after collecting whole crop maize and climate data, and the experimental area was selected as Gyeonggi-do. The forage yield prediction model was developed using the DeepCrossing (R2=0.5442, RMSE=0.1769) technique of the highest accuracy among machine learning techniques. The damage was calculated as the difference between the predicted dry matter yield of normal and abnormal climate. In normal climate, the predicted dry matter yield varies depending on the region, it was found in the range of 15,003~17,517 kg/ha. In abnormal temperature, precipitation, and wind speed, the predicted dry matter yield differed according to region and abnormal climate level, and ranged from 14,947 to 17,571, 14,986 to 17,525, and 14,920 to 17,557 kg/ha, respectively. In abnormal temperature, precipitation, and wind speed, the damage was in the range of -68 to 89 kg/ha, -17 to 17 kg/ha, and -112 to 121 kg/ha, respectively, which could not be judged as damage. In order to accurately calculate the damage of whole crop maize need to increase the number of abnormal climate data used in the forage yield prediction model.

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 예측값은 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.

Keywords

Acknowledgement

본 논문은 농촌진흥청 공동연구사업의 과제번호: PJ01499603의 지원에 의해 이루어졌습니다.

References

  1. Chung, S.U., Sung, S.H., Zhang, Q.M., Jung, J.S., Oh, M.R., Yun, Y.S., Seong, H.J. and Moon, S.H. 2019. Assessment of productivity and vulnerability of climate impacts of forage corn (Kwangpyeongok) due to climate change in Central Korea. Journal of the Korean Society of Grassland and Forage Science. 39(2):105-113. https://doi.org/10.5333/KGFS.2019.39.2.105
  2. IPCC. 2015. Climate change 2015: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.
  3. Ji, H.C., Cho, J.H., Lee, S.H. and Kim, W.H. 2011. Effect of drought conditions on growth, forage production and quality of silage corn at paddy field. Journal of the Korean Society of Grassland and Forage Science. 31(1):47-54. https://doi.org/10.5333/KGFS.2011.31.1.47
  4. Jo, H.W., Kim, M.K., Lee, S.A., Kim, J.Y., Kim, M.J., Kim, B.W. and Sung, K.I. 2020a. Comparison of fitness of whole crop maize yield prediction according to climate data by machine learning models FM and DeepFM. Proceedings of 2020 Symposium and Conference of Korean Society of Grassland and Forage Science. pp. 150-151.
  5. Jo, H.W., Kim, M.K., Lee, S.A., Kim, J.Y., Kim, M.J., Kim, B.W. and Sung, K.I. 2020b. Detecting the optimal machine learning model using dry matter yield of whole crop maize and climate data. Proceedings of 2020 Annual Congress of Korean Society of Animal Science and Technology. p. 54.
  6. Kim, J.Y., Kim, M.J., Jo, H.W., Lee, B.H., Jo, M.H., Kim, B.W. and Sung, K.I. 2021. Assessment of contribution of climate and soil factors on alfalfa yield by yield prediction model. Journal of the Korean Society of Grassland and Forage Science. 41(1):47-55. https://doi.org/10.5333/KGFS.2021.41.1.47
  7. Kim, M.J. 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
  8. 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
  9. Korean Meteorological Administration(KMA). 2020. Korean climate change assessment report 2020. pp. 327-328.
  10. Kucharik, C.J. and Serbin, S.P. 2008. Impacts of recent climate change on Wisconsin corn and soybean yield trends. Environmental Research Letters. 3:034003. https://doi.org/10.1088/1748-9326/3/3/034003
  11. Lee, J.S. and Kim, S.L. 2019. The theory and practice of corn cultivation. Agricultural Technology Guide, 035. pp. 47-48.
  12. Lee, S.H. and Kwon, W.T. 2004. A variation of summer rainfall in Korea. Journal of the Korean Geographical Society. 39(6):819-832.
  13. Min, C.W., Lee, J.W., Kim, D.H., Jung, J.S. and Lee, B.H. 2021. Correlation analysis of climatic factors with forage maize (Zea mays L.) yield in the southern region of Korea. Journal of Agriculture & Life Science. 55(5):19-26.
  14. Na, H.N. and Jung, W.S. 2021. Characteristics of strong winds on the Korean peninsula during the non-typhoon period: Data analysis for 116 years from 1904 to 2019. Journal of Korean Society for Atmospheric Environment. 37(1):55-65. https://doi.org/10.5572/KOSAE.2021.37.1.055
  15. Oh, S.M. 2017. Application of prediction modeling for improving precision of yield prediction and mixed pasture in stages. Kangwon National University. p. 93.
  16. Park, K.H., 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
  17. Peng, J.L. 2017. Suitability mapping and yield prediction modeling using climatic and geographic information for improving forage crops production in South Korea. Kangwon National University. p. 128.
  18. Shim, K.M., Kim, Y.S., Jung, M.P., Kim, S.C., Min, S.H. and So, K.H. 2013. Agro-climatic zonal characteristic of the frequency of abnormal air temperature occurrence in South Korea. Journal of Climatic Change Research. 4(2):189-199.
  19. Shim, K.M., Lee, J.T., Lee, Y.S. and Kim, G.Y. 2003. Traits of agro-meteorological disasters in 20th century Korea. Korean Journal of Agricultural and Forest Meteorology. 5(4):255-260.
  20. Shin, S.H., Jung, G.H., Kim, S.G., Son, B.Y., Kim, S.G., Lee, J.S., Kim, J.T., Bae, H.H., Kwon, Y.U., Shim, K.B. and Lee, J.E. 2017. Effect of prolonged waterlogging on growth and yield of characteristics of maize (Zea mays L.) at early vegetative stage. Journal of the Korean Society of Grassland and Forage Science. 37(4):271-276. https://doi.org/10.5333/KGFS.2017.37.4.271
  21. Sung, K.I. 2020. Damage assessment in forages and development of cultivation technology for their damage reduction according to extreme weather. Rural Development Administration, National Institute of Agricultural Sciences, Wanju.